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9,302 results for “DETECT”
Detecting TAD-like domains from RNA-associated interactions
<p>The example data comprises six files. One is the RNA-chromatin contact map of chr22 constructed from iMARGI HEK dataset (iMARGI_chr22_RAI_matrix_100kb.txt). For Hi-C data, we collect the contact map of chr22 from the Hi-C HEK293T dataset (HEK293T_chr22_100KR_matrix.txt) as well as the detected TADs result (HEK293T_chr22_100KR_matrix.txt.multi2D_AllH2_sparse.tsv). To evaluate the epigenetic characteristic enrichment of inferred TAD-like domains, we download the CTCF TF ChIP-seq peaks (CTCF_ENCFF206AQV.bed), the result of fold change over control of H3K27me3 (H3K27me3_hg38_GSM3907592.bedgraph) and H3K36me3 (H3K36me3_hg38_ENCSR910LIE.bedgraph).</p> <p>The software SuperTLD (https://github.com/deepomicslab/SuperTLD) can directly take the data as input.</p> <p>Usage: uncompress the example_data.zip into the directory of ./data</p>
Trojan Detection Challenge Datasets
<p><strong>NOTE: To download test networks, please use the Zenodo records for the individual tracks linked to below. This Zenodo record only contains the training and validation networks.</strong></p> <p> </p> <p>This contains the training and validation datasets for the Trojan Detection Challenge NeurIPS 2022 competition. To learn more, please see the <a href="http://trojandetection.ai">competition website</a>.</p> <p>For participants in the detection track who are downloading the updated validation set, you can redownload the full dataset here, or you can download the new files only from this Zenodo upload: <a href="https://zenodo.org/record/6899223">https://zenodo.org/record/6899223</a>. Participants in other tracks do not need to redownload the dataset.</p> <p>To download the datasets for individual tracks, please see the following Zenodo uploads. These include the test networks but are otherwise identical to the subfolders in this upload:</p> <ul> <li>Detection: <a href="https://zenodo.org/record/7059487">https://zenodo.org/record/7059487</a></li> <li>Target Label Prediction: <a href="https://zenodo.org/record/7063081">https://zenodo.org/record/7063081</a></li> <li>Trigger Synthesis: <a href="https://zenodo.org/record/7063103">https://zenodo.org/record/7063103</a></li> <li>Evasive Trojans: <a href="https://zenodo.org/record/7063166">https://zenodo.org/record/7063166</a></li> </ul> <p>The Final Round networks are not included in this record. They can be downloaded here: <a href="https://zenodo.org/record/7269120">https://zenodo.org/record/7269120</a></p>
Trojan Detection Challenge Datasets (New Detection Track Validation Set)
<p>This upload contains the new version of the detection track validation set for the Trojan Detection Challenge NeurIPS 2022 competition. This is meant for participants in the detection who do not want to redownload the full dataset. For the full dataset including data for other tracks, see <a href="https://zenodo.org/record/6894041">https://zenodo.org/record/6894041</a>.</p>
Image Dataset for 'AI-enabled biosensing for rapid pathogen detection: from liquid food to agricultural water'
<p>This dataset is presented in the following publication. Please cite this publication if you use the dataset.</p> <p><em>Jiyoon Yi, Nicharee Wisuthiphaet, Pranav Raja, Nitin Nitin, J. Mason Earles. (2023). AI-enabled biosensing for rapid pathogen detection: from liquid food to agricultural water. Water Research, 120258. doi: <a href="https://doi.org/10.1016/j.watres.2023.120258">10.1016/j.watres.2023.120258</a></em></p>
Data used in the paper Automatically Detecting Visual Bugs in HTML5 <canvas> Games
<p>This repository contains the snapshots (i.e., screenshot, <canvas> object representation pairs) and game assets collected from our test <canvas> game for use in the paper <code>Automatically Detecting Visual Bugs in HTML5 <canvas> Games</code>, accepted at ASE 2022.</p> <p>The data in this repository can be used to benchmark new visual bug detection approaches for HTML5 <canvas> games. The source code for our test <canvas> game and our 24 synthetic visual bugs can be found at the following <a href="https://github.com/asgaardlab/canvas-visual-bugs-testbed">link</a>.</p> <p><a href="https://asgaardlab.github.io/canvas-visual-bugs-testbed/">Project page</a><br> <br> <strong>Directory structure</strong></p> <pre> data/ | - assets/ | - exp_0/ | | - a/ | | | - 0.png | | | - 0.json | | | ... | | | - 9.png | | | - 9.json | | ... | | - j/ | - exp_appearance_1 | ... | - exp_state_6 </pre> <p><br> <strong>Data description</strong></p> <table> <tbody> <tr> <td>assets/</td> <td>Source images from the test <canvas> game</td> </tr> <tr> <td>exp_0/</td> <td>Snapshots with no visual bugs injected (okay/non-buggy experiment)</td> </tr> <tr> <td>exp_*_{1,2,3,4,5,6}/</td> <td>Snapshots for each of the injected visual bugs (buggy experiments)</td> </tr> <tr> <td>*/{a,b,c,d,e,f,g,h,i,j}/</td> <td>10 runs of data for each experiment</td> </tr> <tr> <td>*/{0,1,2,3,4,5,6,7,8,9}.png</td> <td>10 snapshots per experiment, each snapshot has a screenshot (png)</td> </tr> <tr> <td>*/{0,1,2,3,4,5,6,7,8,9}.json</td> <td>10 snapshots per experiment, each snapshot has a COR<sup>1</sup> (json)</td> </tr> </tbody> </table> <p><sup>1</sup>COR = <canvas> objects representation</p>
Sea lice (Lepeophtherius salmonis) detection and quantification around aquaculture installations using environmental DNA
<p>Here, we present supplementary material form our study<em> Sea lice (Lepeophtherius salmonis) detection and quantification around aquaculture installations using environmental DNA</em>. In our study, we developed and tested a new <em>L. salmonis</em> specific DNA-based assay (qPCR assay) for detection and quantification from seawater samples using an analytical pipeline compatible with the Environmental Sample Processor (ESP) for autonomous water sample analysis of gene targets.</p>
Identification of biomarkers for the earlier detection of lung cancer: a systematic review and meta-analysis
<p>All data analysed during this study are included in this excel sheet showing the steps in this systematic literature review process.</p>
SimuShips - A High Resolution Simulation Dataset for Ship Detection with Precise Annotations
<p><strong>SimuShips - A High Resolution Simulation Dataset for Ship Detection with Precise Annotations</strong></p> <p>Availability of domain-specific datasets is a challenge for object detection. In particular, conducting onsite experiments is risky and time-consuming for the maritime domain. It is not always possible to capture challenging situations and incorporate variations in the environment. Therefore, we present a simulation-based dataset for maritime environments consisting of 9471 high-resolution (1920x1080) images resembling the real world with precise annotations.</p> <p>The dataset was acquired using a simulation tool, AILiveSim [17]. AILiveSim is a 3D simulation platform for targeted scalable development and integration of autonomous systems through digital twins. The tool provides a realistic 3D model of the route of the watercraft extended from the city of Turku to Ruisalo in South-West Finland.</p> <p>Our dataset incorporates diversity of objects, weather, illumination, visible proportion and scale in images that resemble the real world. </p> <p>Please cite the following publication when using the dataset:</p> <p>M. Raza, H. Prokopova, S. Huseynzade, S. Azimi and S. Lafond, "SimuShips - A High Resolution Simulation Dataset for Ship Detection with Precise Annotations," <em>OCEANS 2022, Hampton Roads</em>, Hampton Roads, VA, USA, 2022, pp. 1-5, doi: 10.1109/OCEANS47191.2022.9977182.</p> <p>The publication is available at: <a href="https://doi.org/10.1109/OCEANS47191.2022.9977182">https://doi.org/10.1109/OCEANS47191.2022.9977182</a></p> <p>A preprint version of the publication is available at <a href="https://arxiv.org/abs/2211.05237">https://arxiv.org/abs/2211.05237</a></p> <p> </p> <p> </p>
KITTI 3D Object Detection
<p>The dataset downloaded from official KITTI website was used in eGAC3D's evaluation. This dataset includes raw images (data_object_label_2.zip) and corresponding ground-truth (data_object_image_2.zip). </p>
Training data for ship track detection machine learning algorithms
<p>The training data and labels used to train the linked machine learning algorithm</p>
Non-detected changes of dark spots on Mercury in 30 Earth months
<p>Central coordinates for each of the cataloged dark spots on Mercury.</p>
DataSet for UAV-based Untrained Small Object Detection using Distance Metric Method
<p>DataSet for UAV-based Untrained Small Object Detection using Distance Metric Method</p>
A Large TV Dataset for Speech and Music Activity Detection
<p>Automatic speech and music activity detection (SMAD) is an enabling task that can help segment, index, and pre-process audio content in radio broadcast and TV programs. However, due to copyright concerns and the cost of manual annotation, the limited availability of diverse and sizeable datasets hinders the progress of state-of-the-art (SOTA) data-driven approaches. We address this challenge by presenting a large-scale dataset containing Mel spectrogram, VGGish, and MFCCs features extracted from around 1600 hours of professionally produced audio tracks and their corresponding noisy labels indicating the approximate location of speech and music segments. The labels are derived from several sources such as subtitles. A test set curated by human annotators is also included as a subset for evaluation. To the best of our knowledge, this dataset is the first large-scale, open-sourced dataset that contains features extracted from professionally produced audio tracks and their corresponding frame-level speech and music annotations. </p>
Policy compliance detection with system call traces
<p>This repository includes data and results in chapter 4 - Policy compliance detection with syscall profiling of Ph.D. thesis "A policy compliance detection architecture for data exchange infrastructures"</p>
real time intrusion detection systems
<p>This repository includes data and results in chapter 5 of PhD thesis "A policy compliance detection architecture for data exchange infrastructures"</p>
Data from: Artificial intelligence model for analyzing colonic endoscopy images to detect changes associated with irritable bowel syndrome
<p><strong><span>Background/Aims</span></strong></p> <p><span>IBS is not considered to be an organic disease and usually shows no abnormality on lower gastrointestinal endoscopy, although biofilm formation, dysbiosis, and histological microinflammation have recently been reported in patients with IBS. In this study, we investigated whether an artificial intelligence (AI) colorectal image model can identify minute endoscopic changes, which cannot typically be detected by human investigators, that are associated with IBS.</span></p> <p><span>Study subjects were identified based on electronic medical records and categorized as IBS (Group I; n=11), IBS with predominant constipation (IBS-C; Group C; n=12), and IBS with predominant diarrhea (IBS-D; Group D; n=12). The study subjects had no other diseases. Colonoscopy images from IBS patients and from asymptomatic healthy subjects (Group N; n=88) were obtained. Google Cloud Platform AutoML Vision (single-label classification) was used to construct AI image models to calculate sensitivity, specificity, predictive value, and AUC. A total of 2479, 382, 538, and 484 images were randomly selected for Groups N, I, C and D, respectively. </span></p> <p><strong><span>Results</span></strong></p> <p><span>The AUC of the model discriminating between Group N and I was 0.95. Sensitivity, specificity, positive predictive value, and negative predictive value of Group I detection were 30.8%, 97.6%, 66.7%, and 90.2%, respectively. The overall AUC of the model discriminating between Groups N, C, and D was 0.83; sensitivity, specificity, and positive predictive value of Group N were 87.5%, 46.2%, and 79.9%, respectively.</span></p> <p><strong><span>Conclusions</span></strong></p> <p><span>Using the image AI model, colonoscopy images of IBS could be discriminated from healthy subjects at AUC 0.95. Prospective studies are needed to further validate whether this externally validated model has similar diagnostic capabilities at other facilities and whether it can be used to determine treatment efficacy.</span></p>
IEC 60870-5-104 Intrusion Detection Dataset
<p><strong>IEC 60870-5-104</strong></p> <p><strong>Intrusion Detection Dataset</strong></p> <p><strong>Readme File</strong></p> <p>ITHACA – University of Western Macedonia - <a href="https://ithaca.ece.uowm.gr/">https://ithaca.ece.uowm.gr/</a></p> <p>Authors: Panagiotis Radoglou-Grammatikis, Thomas Lagkas, Vasileios Argyriou, Panagiotis Sarigiannidis</p> <p><strong>Publication Date:</strong> September 23, 2022</p> <p> </p> <p>1.Introduction</p> <p>The evolution of the Industrial Internet of Things (IIoT) introduces several benefits, such as real-time monitoring, pervasive control and self-healing. However, despite the valuable services, security and privacy issues still remain given the presence of legacy and insecure communication protocols like IEC 60870-5-104. IEC 60870-5-104 is an industrial protocol widely applied in critical infrastructures, such as the smart electrical grid and industrial healthcare systems. The IEC 60870-5-104 Intrusion Detection Dataset was implemented in the context of the research paper entitled "Modeling, Detecting, and Mitigating Threats Against Industrial Healthcare Systems: A Combined Software Defined Networking and Reinforcement Learning Approach" [1], in the context of two H2020 projects: ELECTRON: rEsilient and seLf-healed EleCTRical pOwer Nanogrid (101021936) and SDN-microSENSE: SDN - microgrid reSilient Electrical eNergy SystEm (833955). This dataset includes labelled Transmission Control Protocol (TCP)/Internet Protocol (IP) network flow statistics (Common-Separated Values (CSV) format) and IEC 60870-5-104 flow statistics (CSV format) related to twelve IEC 60870-5-104 cyberattacks. In particular, the cyberattacks are related to unauthorised commands and Denial of Service (DoS) activities against IEC 60870-5-104. Moreover, the relevant Packet Capture (PCAP) files are available. The dataset can be utilised for Artificial Intelligence (AI)-based Intrusion Detection Systems (IDS), taking full advantage of Machine Learning (ML) and Deep Learning (DL).</p> <p>2.Instructions</p> <p>The IEC 60870-5-104 dataset was implemented following the methodology of A. Gharib et al. in [2], including eleven features: (a) Complete Network Configuration, (b) Complete Traffic, (c) Labelled Dataset, (d) Complete Interaction, (e) Complete Capture, (f) Available Protocols, (g) Attack Diversity, (h) Heterogeneity, (i) Feature Set and (j) Metadata.</p> <p>A network topology consisting of (a) seven industrial entities, (b) one Human Machine Interfaces (HMI) and (c) three cyberattackers was used to construct the IEC 60870-5-104 Intrusion Detection Dataset. The industrial entities use IEC TestServer<a href="#_ftn1">[1]</a>, while the HMI uses Qtester104<a href="#_ftn2">[2]</a>. On the other hand, the cyberattackers use Kali Linux<a href="#_ftn3">[3]</a> equipped with Metasploit<a href="#_ftn4">[4]</a>, OpenMUC j60870<a href="#_ftn5">[5]</a> and Ettercap<a href="#_ftn6">[6]</a>. The cyberattacks were performed during the following days.</p> <ul> <li>On Saturday, April 25, 2020, a DoS cyberattack (M_SP_NA_1_DoS) was executed for 2 hours, using the M_SP_NA_1 command.</li> <li>On Sunday, April 26, 2020, two cyberattacks were executed, namely (a) DoS (C_CI_NA_1_DoS) and (b) unauthorised injection (C_CI_NA_1), using the C_CI_NA_1 command for 2 hours.</li> <li>On Monday, April 27, 2020, one unauthorised injection attack (C_SE_NA_1) was executed for 4 hours, using the C_SE_NA_1 command.</li> <li>Tuesday, April 28, 2020 two cyberattacks were executed, namely (a) unauthorised injection (C_SC_NA_1) and (b) DoS (C_SE_NA_1_DoS), using the C_SC_NA_1 and C_SE_NA_1 commands for 2 hours and 4 hours, respectively.</li> <li>Wednesday, April 29, 2020, one DoS (C_SC_NA_1) cyberattack was performed for 2 hours, using the C_SC_NA_1 command.</li> <li>Friday, June 05, 2020, two cyberattacks were executed, namely (a) DoS (C_RD_NA_1_DoS) and (b) unauthorised injection (C_RD_NA_1), using the C_RD_NA_1 command for 2 and 4 hours, respectively.</li> <li>Saturday, June 06, 2020, two cyberattacks were executed, namely (a) DoS (C_RP_NA_1_DoS) and (b) unauthorised injection (C_RP_NA_1), using the C_RP_NA_1 command for 2 and 4 hours, respectively.</li> <li>Monday, June 08, 2020, a Man In The Middle (MITM) cyberattack was executed for 2 hours, filtering and dropping the IEC 60870-5-104 packets.</li> </ul> <p>For each attack, a 7zip file is provided, including the network traffic and the network flow statistics for each entity. Moreover, a relevant diagram is provided, illustrating the corresponding cyberattack. In particular, for each entity, a folder is given, including (a) the relevant pcap file, (b) Transmission Control Protocol (TCP) / Internet Protocol (IP) network flow statistics in a Common Separated Value (CSV) format and (c) IEC 60870-5-104 flow statistics in a CSV format. The TCP/IP network flow statistics were generated by CICFlowMeter<a href="#_ftn7">[7]</a>, while the IEC 60870-5-104 flow statistics were generated based on a Custom IEC 60870-5-104 Python Parser<a href="#_ftn8">[8]</a>, taking full advantage of Scapy<a href="#_ftn9">[9]</a>.</p> <p>3.Dataset Structure</p> <p>The dataset consists of the following files:</p> <ul> <li><strong>20200425_UOWM_IEC104_Dataset_m_sp_na_1_DoS.7z</strong>: A 7zip file including the pcap and CSV files related to the M_SP_NA_1 attack.</li> <li><strong>20200426_UOWM_IEC104_Dataset_c_ci_na_1_DoS.7z</strong>: A 7zip file including the pcap and CSV files related to the C_CI_NA_1_DoS attack.</li> <li><strong>20200426_UOWM_IEC104_Dataset_c_ci_na_1.7z</strong>: A 7zip file including the pcap and CSV files related to C_CI_NA_1 attack.</li> <li><strong>20200427_UOWM_IEC104_Dataset_c_se_na_1.7z</strong>: A 7zip file including the pcap and CSV files related to the C_SE_NA_1 attack.</li> <li><strong>20200428_UOWM_IEC104_Dataset_c_sc_na_1.7z</strong>: A 7zip file including the pcap and CSV files related to the C_SC_NA_1 attack.</li> <li><strong>20200428_UOWM_IEC104_Dataset_c_se_na_1_DoS.7z</strong>: A 7zip file including the pcap and CSV files related to the C_SE_NA_1_DoS attack.</li> <li><strong>20200429_UOWM_IEC104_Dataset_c_sc_na_1_DoS.7z</strong>: A 7zip file including the pcap and CSV files related to the C_SC_NA_1_DoS attack.</li> <li><strong>20200605_UOWM_IEC104_Dataset_c_rd_na_1_DoS.7z</strong>: A 7zip file including the pcap and CSV files related to the C_RD_NA_1_DoS attack.</li> <li><strong>20200605_UOWM_IEC104_Dataset_c_rd_na_1.7z</strong>: A 7zip file including the pcap and CSV files related to the C_RD_NA_1 attack.</li> <li><strong>20200606_UOWM_IEC104_Dataset_c_rp_na_1_DoS.7z</strong>: A 7zip file including the pcap and CSV files related to the C_RP_NA_1_DoS attack.</li> <li><strong>20200606_UOWM_IEC104_Dataset_c_rp_na_1.7z</strong>: A 7zip file including the pcap and CSV files related to the C_RP_NA_1 attack.</li> <li><strong>20200608_UOWM_IEC104_Dataset_mitm_drop.7z</strong>: A 7zip file including the pcap and CSV files related to the MITM attack.</li> <li><strong>Balanced_IEC104_Train_Test_CSV_Files.zip</strong>: This zip file includes balanced CSV files from CICFlowMeter and the Custom IEC 60870-5-104 Python Parser that could be utilised for training ML and DL methods. The zip file includes different folders for the corresponding flow timeout values used for CICFlowMeter and IEC 60870-5-104 Python Parser, respectively.</li> </ul> <p>Each 7zip file includes respective folders related to the entities/devices (described in the following section) participating in each attack. In particular, for each entity/device, there is a folder including (a) the overall network traffic (pcap file) related to this entity/device during each attack, (b) the TCP/IP network flow statistics (CSV file) from CICFlowMeter for the overall network traffic, (c) the IEC 60870-5-104 network traffic (pcap file) related to this entity/device during each attack, (d) the TCP/IP network flow statistics (CSV file) from CICFlowMeter for the IEC 608770-5-104 network traffic, (e) the IEC 60870-5-104 flow statistics (CSV file) from the Custom IEC 60870-5-104 Python Parser for the IEC 608770-5-104 network traffic and finally, (f) an image showing how the attack was executed. Finally, it is noteworthy that the network flow from both CICFlowMeter and Custom IEC 60870-5-104 Python Parser in each CSV file are <strong>labelled</strong> based on the IEC 60870-5-104 cyberattacks executed for the generation of this dataset. The description of these attacks is given in the following section, while the various features from CICFlowMeter and Custom IEC 60870-5-104 Python Parser are presented in Section 5.</p> <p>4.Testbed & IEC 60870-5-104 Attacks</p> <p>The testbed created for generating this dataset is composed of five virtual RTU devices emulated by IEC TestServer and two real RTU devices. Moreover, there is another workstation which plays the role of Master Terminal Unit (MTU) and HMI, sending legitimate IEC 60870-5-104 commands to the corresponding RTUs. For this purpose, the workstation uses QTester104. In addition, there are three attackers that act as malicious insiders executing the following cyberattacks against the aforementioned RTUs. Finally, the network traffic data of each entity/device was captured through tshark.</p> <p>Table 1: IEC 60870-5-104 Cyberattacks Description</p> <table> <tbody> <tr> <td> <p><strong>IEC 60870-5-104 Cyberattack Description</strong></p> </td> <td> <p><strong>Description</strong></p> </td> <td> <p><strong>Dataset Files</strong></p> </td> </tr> <tr> <td> <p>MITM Drop</p> </td> <td> <p>During this attack, the cyberattacker is placed between two endpoints, thus monitoring and dropping the network traffic exchanged.</p> </td> <td> <p>20200608_UOWM_IEC104_Dataset_mitm_drop.7z</p> </td> </tr> <tr> <td> <p>C_CI_NA_1</p> </td> <td> <p>The C_CI_NA_1 is a Counter Interrogation command in the control direction. This cyberattack sends unauthorised IEC 60870-5-104 C_CI_NA_1 packets to the target system.</p> </td> <td> <p>20200426_UOWM_IEC104_Dataset_c_ci_na_1.7z</p> </td> </tr> <tr> <td> <p>C_SC_NA_1</p> </td> <td> <p>The C_SC_NA_1 command is a single command. This cyberattack sends unauthorised C_SC_NA_1 60870-5-104 packets to the target system</p> </td> <td> <p>20200428_UOWM_IEC104_Dataset_c_sc_na_1.7z</p> </td> </tr> <tr> <td> <p>C_SE_NA_1</p> </td> <td> <p>The C_SE_NA_1 command is a set-point command with normalised values. This cyberattack sends unauthorised IEC 60870-5-104 C_SE_NA_1 packets to the target system.</p> </td> <td> <p>20200427_UOWM_IEC104_Dataset_c_se_na_1.7z</p> </td> </tr> <tr> <td> <p>C_RD_NA_1</p> </td> <td> <p>The C_RD_NA_1 command is a read command. This cyberattack sends unauthorised IEC 60870-5-104 C_RD_NA_1 packets to the target system.</p> </td> <td> <p>20200605_UOWM_IEC104_Dataset_c_rd_na_1.7z</p> </td> </tr> <tr> <td> <p>C_RP_NA_1</p> </td> <td> <p>The C_RP_NA_1 command is a reset command. This cyberattack sends unauthorised IEC 60870-5-104 C_RP_NA_1 packets to the target system.</p> </td> <td> <p>20200606_UOWM_IEC104_Dataset_c_rp_na_1.7z</p> </td> </tr> <tr> <td> <p>M_SP_NA_1_DoS</p> </td> <td> <p>This attack floods the target system with IEC 60870-5-104 M_SP_NA_1 packets.</p> </td> <td> <p>20200425_UOWM_IEC104_Dataset_m_sp_na_1_DoS.7z</p> </td> </tr> <tr> <td> <p>C_CI_NA_1_DoS</p> </td> <td> <p>This attack floods the target system with IEC 60870-5-104 C_CI_NA_1 packets.</p> </td> <td> <p>20200426_UOWM_IEC104_Dataset_c_ci_na_1_DoS.7z</p> </td> </tr> <tr> <td> <p>C_SE_NA_1_DoS</p> </td> <td> <p>This attack floods the target system with IEC 60870-5-104 C_SE_NA_1 packets.</p> </td> <td> <p>20200428_UOWM_IEC104_Dataset_c_se_na_1_DoS.7z</p> </td> </tr> <tr> <td> <p>C_SC_NA_1_DoS</p> </td> <td> <p>This attack floods the target system with IEC 60870-5-104 C_SC_NA_1 packets.</p> </td> <td> <p>20200429_UOWM_IEC104_Dataset_c_sc_na_1_DoS.7z</p> </td> </tr> <tr> <td> <p>C_RD_NA_1_DoS</p> </td> <td> <p>This attack floods the target system with IEC 60870-5-104 C_RD_NA_1 packets.</p> </td> <td> <p>20200605_UOWM_IEC104_Dataset_c_rd_na_1_DoS.7z</p> </td> </tr> <tr> <td> <p>C RP NA 1 DoS</p> </td> <td> <p>This attack floods the target system with IEC 60870-5-104 C_RP_NA_1 packets.</p> </td> <td> <p>20200606_UOWM_IEC104_Dataset_c_rp_na_1_DoS.7z</p> </td> </tr> </tbody> </table> <p>5.Features</p> <p>The TCP/IP network flow statistics generated by CICFlowMeter are summarised below. <strong>It is worth mentioning that the TCP/IP network flows and their statistics generated by </strong><strong>CICFlowMeter are labelled based on the IEC 60870-5-104 attacks described above, thus allowing the training of ML/DL models.</strong></p> <p>Table 2: CICFlowMeter TCP/IP Network Flow Statistics - Features</p> <table> <tbody> <tr> <td> <p><strong>Feature</strong></p> </td> <td> <p><strong>Description</strong></p> </td> </tr> <tr> <td> <p>Flow ID</p> </td> <td> <p>ID of the flow</p> </td> </tr> <tr> <td> <p>Src IP</p> </td> <td> <p>Source IP address</p> </td> </tr> <tr> <td> <p>Src Port</p> </td> <td> <p>Source TCP/UDP port</p> </td> </tr> <tr> <td> <p>Dst IP</p> </td> <td> <p>Destination IP address</p> </td> </tr> <tr> <td> <p>Dst Port</p> </td> <td> <p>Destination TCP/UDP port</p> </td> </tr> <tr> <td> <p>Protocol</p> </td> <td> <p>The protocol related to the corresponding flow</p> </td> </tr> <tr> <td> <p>Timestamp</p> </td> <td> <p>Flow timestamp</p> </td> </tr> <tr> <td> <p>Flow Duration</p> </td> <td> <p>Duration of the flow in Microsecond</p> </td> </tr> <tr> <td> <p>Tot Fwd Pkts</p> </td> <td> <p>Total packets in the forward direction</p> </td> </tr> <tr> <td> <p>Tot Bwd Pkts</p> </td> <td> <p>Total packets in the backward direction</p> </td> </tr> <tr> <td> <p>TotLen Fwd Pkts</p> </td> <td> <p>Total size of packets in forward direction</p> </td> </tr> <tr> <td> <p>TotLen Bwd Pkts</p> </td> <td> <p>Total size of packets in backward direction</p> </td> </tr> <tr> <td> <p>Fwd Pkt Len Max</p> </td> <td> <p>Maximum size of packet in forward direction</p> </td> </tr> <tr> <td> <p>Fwd Pkt Len Min</p> </td> <td> <p>Minimum size of packet in forward direction</p> </td> </tr> <tr> <td> <p>Fwd Pkt Len Mean</p> </td> <td> <p>Mean size of packet in forward direction</p> </td> </tr> <tr> <td> <p>Fwd Pkt Len Std</p> </td> <td> <p>Standard deviation size of packet in forward direction</p> </td> </tr> <tr> <td> <p>Bwd Pkt Len Max</p> </td> <td> <p>Maximum size of packet in backward direction</p> </td> </tr> <tr> <td> <p>Bwd Pkt Len Min</p> </td> <td> <p>Minimum size of packet in backward direction</p> </td> </tr> <tr> <td> <p>Bwd Pkt Len Mean</p> </td> <td> <p>Mean size of packet in backward direction</p> </td> </tr> <tr> <td> <p>Bwd Pkt Len Std</p> </td> <td> <p>Standard deviation size of packet in backward direction</p> </td> </tr> <tr> <td> <p>Flow Byts/s</p> </td> <td> <p>Number of flow bytes per second</p> </td> </tr> <tr> <td> <p>Flow Pkts/s</p> </td> <td> <p>Number of flow packets per second</p> </td> </tr> <tr> <td> <p>Flow IAT Mean</p> </td> <td> <p>Mean time between two packets sent in the flow</p> </td> </tr> <tr> <td> <p>Flow IAT Std</p> </td> <td> <p>Standard deviation time between two packets sent in the flow</p> </td> </tr> <tr> <td> <p>Flow IAT Max</p> </td> <td> <p>Maximum time between two packets sent in the flow</p> </td> </tr> <tr> <td> <p>Flow IAT Min</p> </td> <td> <p>Minimum time between two packets sent in the flow</p> </td> </tr> <tr> <td> <p>Fwd IAT Tot</p> </td> <td> <p>Total time between two packets sent in the forward direction</p> </td> </tr> <tr> <td> <p>Fwd IAT Mean</p> </td> <td> <p>Mean time between two packets sent in the forward direction</p> </td> </tr> <tr> <td> <p>Fwd IAT Std</p> </td> <td> <p>Standard deviation time between two packets sent in the forward direction</p> </td> </tr> <tr> <td> <p>Fwd IAT Max</p> </td> <td> <p>Maximum time between two packets sent in the forward direction</p> </td> </tr> <tr> <td> <p>Fwd IAT Min</p> </td> <td> <p>Minimum time between two packets sent in the forward direction</p> </td> </tr> <tr> <td> <p>Bwd IAT Tot</p> </td> <td> <p>Total time between two packets sent in the backward direction</p> </td> </tr> <tr> <td> <p>Bwd IAT Mean</p> </td> <td> <p>Mean time between two packets sent in the backward direction</p> </td> </tr> <tr> <td> <p>Bwd IAT Std</p> </td> <td> <p>Standard deviation time between two packets sent in the backward direction</p> </td> </tr> <tr> <td> <p>Bwd IAT Max</p> </td> <td> <p>Maximum time between two packets sent in the backward direction</p> </td> </tr> <tr> <td> <p>Bwd IAT Min</p> </td> <td> <p>Minimum time between two packets sent in the backward direction</p> </td> </tr> <tr> <td> <p>Fwd PSH Flags</p> </td> <td> <p>Number of times the PSH flag was set in packets travelling in the forward direction (0 for UDP)</p> </td> </tr> <tr> <td> <p>Bwd PSH Flags</p> </td> <td> <p>Number of times the PSH flag was set in packets travelling in the backward direction (0 for UDP)</p> </td> </tr> <tr> <td> <p>Fwd URG Flags</p> </td> <td> <p>Number of times the URG flag was set in packets travelling in the forward direction (0 for UDP)</p> </td> </tr> <tr> <td> <p>Bwd URG Flags</p> </td> <td> <p>Number of times the URG flag was set in packets travelling in the backward direction (0</p> <p>for UDP)</p> </td> </tr> <tr> <td> <p>Fwd Header Len</p> </td> <td> <p>Total bytes used for headers in the forward direction</p> </td> </tr> <tr> <td> <p>Bwd Header Len</p> </td> <td> <p>Total bytes used for headers in the backward direction</p> </td> </tr> <tr> <td> <p>Fwd Pkts/s</p> </td> <td> <p>Number of forward packets per second</p> </td> </tr> <tr> <td> <p>Bwd Pkts/s</p> </td> <td> <p>Number of backward packets per second</p> </td> </tr> <tr> <td> <p>Pkt Len Min</p> </td> <td> <p>Minimum length of a packet</p> </td> </tr> <tr> <td> <p>Pkt Len Max</p> </td> <td> <p>Maximum length of a packet</p> </td> </tr> <tr> <td> <p>Pkt Len Mean</p> </td> <td> <p>Mean length of a packet</p> </td> </tr> <tr> <td> <p>Pkt Len Std</p> </td> <td> <p>Standard deviation length of a packet</p> </td> </tr> <tr> <td> <p>Pkt Len Var</p> </td> <td> <p>Variance length of a packet</p> </td> </tr> <tr> <td> <p>FIN Flag Cnt</p> </td> <td> <p>Number of packets with FIN</p> </td> </tr> <tr> <td> <p>SYN Flag Cnt</p> </td> <td> <p>Number of packets with SYN</p> </td> </tr> <tr> <td> <p>RST Flag Cnt</p> </td> <td> <p>Number of packets with RST</p> </td> </tr> <tr> <td> <p>PSH Flag Cnt</p> </td> <td> <p>Number of packets with PUSH</p> </td> </tr> <tr> <td> <p>ACK Flag Cnt</p> </td> <td> <p>Number of packets with ACK</p> </td> </tr> <tr> <td> <p>URG Flag Cnt</p> </td> <td> <p>Number of packets with URG</p> </td> </tr> <tr> <td> <p>CWE Flag Count</p> </td> <td> <p>Number of packets with CWE</p> </td> </tr> <tr> <td> <p>ECE Flag Cnt</p> </td> <td> <p>Number of packets with ECE</p> </td> </tr> <tr> <td> <p>Down/Up Ratio</p> </td> <td> <p>Download and upload ratio</p> </td> </tr> <tr> <td> <p>Pkt Size Avg</p> </td> <td> <p>Average size of packet</p> </td> </tr> <tr> <td> <p>Fwd Seg Size Avg</p> </td> <td> <p>Average size observed in the forward direction</p> </td> </tr> <tr> <td> <p>Bwd Seg Size Avg</p> </td> <td> <p>Average size observed in the backward direction</p> </td> </tr> <tr> <td> <p>Fwd Byts/b Avg</p> </td> <td> <p>Average number of bytes bulk rate in the forward direction</p> </td> </tr> <tr> <td> <p>Fwd Pkts/b Avg</p> </td> <td> <p>Average number of packets bulk rate in the forward direction</p> </td> </tr> <tr> <td> <p>Fwd Blk Rate Avg</p> </td> <td> <p>Average number of bulk rate in the forward direction</p> </td> </tr> <tr> <td> <p>Bwd Byts/b Avg</p> </td> <td> <p>Average number of bytes bulk rate in the backward direction</p> </td> </tr> <tr> <td> <p>Bwd Pkts/b Avg</p> </td> <td> <p>Average number of packets bulk rate in the backward direction</p> </td> </tr> <tr> <td> <p>Bwd Blk Rate Avg</p> </td> <td> <p>Average number of bulk rate in the backward direction</p> </td> </tr> <tr> <td> <p>Subflow Fwd Pkts</p> </td> <td> <p>The average number of packets in a sub flow in the forward direction</p> </td> </tr> <tr> <td> <p>Subflow Fwd Byts</p> </td> <td> <p>The average number of bytes in a sub flow in the forward direction</p> </td> </tr> <tr> <td> <p>Subflow Bwd Pkts</p> </td> <td> <p>The average number of packets in a sub flow in the backward direction</p> </td> </tr> <tr> <td> <p>Subflow Bwd Byts</p> </td> <td> <p>The average number of bytes in a sub flow in the backward direction</p> </td> </tr> <tr> <td> <p>Init Fwd Win Byts</p> </td> <td> <p>The total number of bytes sent in initial window in the forward direction</p> </td> </tr> <tr> <td> <p>Init Bwd Win Byts</p> </td> <td> <p>The total number of bytes sent in initial window in the backward direction</p> </td> </tr> <tr> <td> <p>Fwd Act Data Pkts</p> </td> <td> <p>Count of packets with at least 1 byte of TCP data payload in the forward direction</p> </td> </tr> <tr> <td> <p>Fwd Seg Size Min</p> </td> <td> <p>Minimum segment size observed in the forward direction</p> </td> </tr> <tr> <td> <p>Active Mean</p> </td> <td> <p>Mean time a flow was active before becoming idle</p> </td> </tr> <tr> <td> <p>Active Std</p> </td> <td> <p>Standard deviation time a flow was active before becoming idle</p> </td> </tr> <tr> <td> <p>Active Max</p> </td> <td> <p>Maximum time a flow was active before becoming idle</p> </td> </tr> <tr> <td> <p>Active Min</p> </td> <td> <p>Minimum time a flow was active before becoming idle</p> </td> </tr> <tr> <td> <p>Idle Mean</p> </td> <td> <p>Mean time a flow was idle before becoming active</p> </td> </tr> <tr> <td> <p>Idle Std</p> </td> <td> <p>Standard deviation time a flow was idle before becoming active</p> </td> </tr> <tr> <td> <p>Idle Max</p> </td> <td> <p>Maximum time a flow was idle before becoming active</p> </td> </tr> <tr> <td> <p>Idle Min</p> </td> <td> <p>Minimum time a flow was idle before becoming active</p> </td> </tr> <tr> <td> <p>Label</p> </td> <td> <p>Attack label</p> </td> </tr> </tbody> </table> <p>The IEC 60870-5-104 flow statistics generated by IEC 60870-5-104 Python Parser are summarised below. <strong>It is worth mentioning that the IEC 60870-5-104 flows and their statistics generated by </strong><strong>IEC 60870-5-104 Python Parser are labelled based on the IEC 60870-5-104 attacks described above, thus allowing the training of ML/DL models.</strong></p> <p>Table 3: IEC 60870-5-104 Flow Statistics – Features</p> <table> <tbody> <tr> <td> <p><strong>Feature</strong></p> </td> <td> <p><strong>Field description</strong></p> </td> </tr> <tr> <td> <p>flow id</p> </td> <td> <p>ID of the flow</p> </td> </tr> <tr> <td> <p>protocol</p> </td> <td> <p>The relevant protocol of the flow. It equals IEC 60870-5-104</p> </td> </tr> <tr> <td> <p>src ip</p> </td> <td> <p>The source IP address of the flow. It is defined with the first relevant packet.</p> </td> </tr> <tr> <td> <p>dst ip</p> </td> <td> <p>The destination IP address of the flow.</p> </td> </tr> <tr> <td> <p>src port</p> </td> <td> <p>The source TCP/UDP port.</p> </td> </tr> <tr> <td> <p>dst port</p> </td> <td> <p>The destination TCP/UDP port.</p> </td> </tr> <tr> <td> <p>flow idle time max</p> </td> <td> <p>The maximum time where the flow was idle</p> </td> </tr> <tr> <td> <p>flow idle time min</p> </td> <td> <p>The minimum time where the flow was idle</p> </td> </tr> <tr> <td> <p>flow idle time mean</p> </td> <td> <p>The time mean where the flow was idle</p> </td> </tr> <tr> <td> <p>flow idle time std</p> </td> <td> <p>The time standard deviation where the flow was idle</p> </td> </tr> <tr> <td> <p>flow idle time variance</p> </td> <td> <p>The time variance where the flow was idle</p> </td> </tr> <tr> <td> <p>flow active time max</p> </td> <td> <p>The maximum time where the flow was active</p> </td> </tr> <tr> <td> <p>flow active time min</p> </td> <td> <p>The minimum time where the flow was active</p> </td> </tr> <tr> <td> <p>flow active time mean</p> </td> <td> <p>The time mean where the flow was active</p> </td> </tr> <tr> <td> <p>flow active time std</p> </td> <td> <p>The time standard deviation where the flow was active</p> </td> </tr> <tr> <td> <p>flow active time variance</p> </td> <td> <p>The time variance where the flow was active</p> </td> </tr> <tr> <td> <p>flow IAT max</p> </td> <td> <p>The maximum interarrival time</p> </td> </tr> <tr> <td> <p>fw IAT max</p> </td> <td> <p>The maximum interarrival time in the forward direction</p> </td> </tr> <tr> <td> <p>bw IAT max</p> </td> <td> <p>The maximum interarrival time in the backyard direction</p> </td> </tr> <tr> <td> <p>flow IAT min</p> </td> <td> <p>The minimum interarrival time</p> </td> </tr> <tr> <td> <p>fw IAT min</p> </td> <td> <p>The minimum interarrival time in the forward direction</p> </td> </tr> <tr> <td> <p>bw IAT min</p> </td> <td> <p>The minimum interarrival time in the backyard direction</p> </td> </tr> <tr> <td> <p>flow IAT mean</p> </td> <td> <p>The mean of the interarrival time</p> </td> </tr> <tr> <td> <p>fw IAT mean</p> </td> <td> <p>The mean of the interarrival time in the forward direction</p> </td> </tr> <tr> <td> <p>bw IAT mean</p> </td> <td> <p>The mean of the interarrival time in the backyard direction</p> </td> </tr> <tr> <td> <p>flow IAT std</p> </td> <td> <p>The standard deviation of the inter arrival time</p> </td> </tr> <tr> <td> <p>fw IAT std</p> </td> <td> <p>The standard deviation of the inter arrival time in the forward direction</p> </td> </tr> <tr> <td> <p>bw IAT std</p> </td> <td> <p>The standard deviation of the inter arrival time in the backyard direction</p> </td> </tr> <tr> <td> <p>flow IAT tot</p> </td> <td> <p>The total number of the interarrival times</p> </td> </tr> <tr> <td> <p>fw iAT tot</p> </td> <td> <p>The total number of the interarrival times in the forward direction</p> </td> </tr> <tr> <td> <p>bw IAT tot</p> </td> <td> <p>The total number of the interarrival times in the backyard direction</p> </td> </tr> <tr> <td> <p>flow iec104 packts/s</p> </td> <td> <p>The number of IEC 60870-51-04 packets per second</p> </td> </tr> <tr> <td> <p>fw iec104 packts/s</p> </td> <td> <p>The number of IEC 60870-51-04 packets per second in the forward direction</p> </td> </tr> <tr> <td> <p>bw iec104 packts/s</p> </td> <td> <p>The number of IEC 60870-51-04 packets per second in the backyard direction</p> </td> </tr> <tr> <td> <p>flow iec104 bytes/s</p> </td> <td> <p>The sum of APDU lengths per second</p> </td> </tr> <tr> <td> <p>fw iec104 bytes/s</p> </td> <td> <p>The sum of APDU lengths per second in the forward direction</p> </td> </tr> <tr> <td> <p>bw iec104 bytes/s</p> </td> <td> <p>The sum of APDU lengths per second in the backyard direction</p> </td> </tr> <tr> <td> <p>flow packet APDU length max</p> </td> <td> <p>The maximum value of the APDU lengths</p> </td> </tr> <tr> <td> <p>flow packet APDU length min</p> </td> <td> <p>The minimum value of the APDU lengths</p> </td> </tr> <tr> <td> <p>flow packet APDU length mean</p> </td> <td> <p>Mean of the APDU lengths</p> </td> </tr> <tr> <td> <p>flow packet APDU length std</p> </td> <td> <p>The standard deviation of the APDU lengths</p> </td> </tr> <tr> <td> <p>flow packet APDU length var</p> </td> <td> <p>Variance of the APDU lengths</p> </td> </tr> <tr> <td> <p>fw packet APDU length max</p> </td> <td> <p>The maximum value of the APDU lengths in the forward direction</p> </td> </tr> <tr> <td> <p>fw packet APDU length min</p> </td> <td> <p>The minimum value of the APDU lengths in the forward direction</p> </td> </tr> <tr> <td> <p>fw packet APDU length mean</p> </td> <td> <p>Mean of the APDU lengths in the forward direction</p> </td> </tr> <tr> <td> <p>fw packet APDU length std</p> </td> <td> <p>The standard deviation of the APDU lengths in the forward direction</p> </td> </tr> <tr> <td> <p>fw packet APDU length var</p> </td> <td> <p>The variance of the APDU lengths in the forward direction</p> </td> </tr> <tr> <td> <p>bw packet APDU length max</p> </td> <td> <p>The maximum value of the APDU lengths in the backyard direction</p> </td> </tr> <tr> <td> <p>bw packet APDU length min</p> </td> <td> <p>The minimum value of the APDU lengths in the backyard direction</p> </td> </tr> <tr> <td> <p>bw packet APDU length mean</p> </td> <td> <p>Mean of the APDU lengths in the backyard direction</p> </td> </tr> <tr> <td> <p>bw packet APDU length std</p> </td> <td> <p>The standard deviation of the APDU lengths in the backyard direction</p> </td> </tr> <tr> <td> <p>bw packet APDU length var</p> </td> <td> <p>The variance of the APDU lengths in the backyard direction</p> </td> </tr> <tr> <td> <p>total flow packets</p> </td> <td> <p>Total flow packets</p> </td> </tr> <tr> <td> <p>total fw packets</p> </td> <td> <p>Total flow packets in the forward direction</p> </td> </tr> <tr> <td> <p>total bw packets</p> </td> <td> <p>Total flow packets in the backyard direction</p> </td> </tr> <tr> <td> <p>flow packets APDU total length</p> </td> <td> <p>The sum of all APDU lengths</p> </td> </tr> <tr> <td> <p>fw packets APDU total length</p> </td> <td> <p>The sum of all APDU lengths in the forward direction</p> </td> </tr> <tr> <td> <p>bw packets APDU total length</p> </td> <td> <p>The sum of all APDU lengths in the backyard direction</p> </td> </tr> <tr> <td> <p>flow duration</p> </td> <td> <p>Flow duration in seconds</p> </td> </tr> <tr> <td> <p>flow down/up ratio</p> </td> <td> <p>The fraction between the IEC 60870-5-104 packets in the backyard direction and the IEC 60870-5-104 packets in the forward direction</p> </td> </tr> <tr> <td> <p>flow total IEC104_I_Message_SeqIOA packets</p> </td> <td> <p>The total number of the I-format APCI packets that have more than one information objects</p> </td> </tr> <tr> <td> <p>fw total IEC104_I_Message_SeqIOA packets</p> </td> <td> <p>The total number of the I-format APCI packets that have more than one information objects in the forward direction</p> </td> </tr> <tr> <td> <p>bw total IEC104_I_Message_SeqIOA packets</p> </td> <td> <p>The total number of the I-format APCI packets that have more than one information objects in the backyard direction</p> </td> </tr> <tr> <td> <p>flow total IEC104_I_Message_SingleIOA packets</p> </td> <td> <p>The total number of the I-format APCI packets that have one information object in ASDU</p> </td> </tr> <tr> <td> <p>fw total IEC104_I_Message_SingleIOA packets</p> </td> <td> <p>The total number of the I-format APCI packets that have one information object in ASDU in the forward direction</p> </td> </tr> <tr> <td> <p>bw total IEC104_I_Message_SingleIOA packets</p> </td> <td> <p>The total number of the I-format APCI packets that have one information object in ASDU in the backyard direction</p> </td> </tr> <tr> <td> <p>flow total IEC104_S_Message packets</p> </td> <td> <p>The total number of the S-format APCI packets</p> </td> </tr> <tr> <td> <p>fw total IEC104_S_Message packets</p> </td> <td> <p>The total number of the S-format APCI packets in the forward direction</p> </td> </tr> <tr> <td> <p>bw total IEC104_S_Message packets</p> </td> <td> <p>The total number of the S-format APCI packets in the backyard direction</p> </td> </tr> <tr> <td> <p>flow total IEC104_U_Message packets</p> </td> <td> <p>The total number of the U-format APCI packets</p> </td> </tr> <tr> <td> <p>fw total IEC104_U_Message packets</p> </td> <td> <p>The total number of the U-format APCI packets in the forward direction</p> </td> </tr> <tr> <td> <p>bw total IEC104_U_Message packets</p> </td> <td> <p>The total number of the U-format APCI packets in the backyard direction</p> </td> </tr> <tr> <td> <p>fw URG flag amount</p> </td> <td> <p>The number of the URG flags in the forward direction</p> </td> </tr> <tr> <td> <p>fw PSH flag amount</p> </td> <td> <p>The number of the PSH flags in the forward direction</p> </td> </tr> <tr> <td> <p>bw URG flag amount</p> </td> <td> <p>The number of the URG flags in the backyard direction</p> </td> </tr> <tr> <td> <p>bw PSH flag amount</p> </td> <td> <p>The number of the PSH flags in the backyard direction</p> </td> </tr> <tr> <td> <p>flow SYN flag count</p> </td> <td> <p>The number of the TCP SYN packets</p> </td> </tr> <tr> <td> <p>flow RST flag count</p> </td> <td> <p>The number of the TCP RST packets</p> </td> </tr> <tr> <td> <p>flow PSH flag count</p> </td> <td> <p>The number of the TCP PSH packets</p> </td> </tr> <tr> <td> <p>flow ACK flag count</p> </td> <td> <p>The number of the TCP ACK packets</p> </td> </tr> <tr> <td> <p>flow URG flag count</p> </td> <td> <p>The number of the TCP URG packets</p> </td> </tr> <tr> <td> <p>flow CWE flag count</p> </td> <td> <p>The number of the TCP CWE packets</p> </td> </tr> <tr> <td> <p>flow ECE flag count</p> </td> <td> <p>The number of the TCP ECE packets</p> </td> </tr> <tr> <td> <p>fw_subflow_packets</p> </td> <td> <p>The average number of packets in a sub flow in the forward direction</p> </td> </tr> <tr> <td> <p>bw_subflow_packets</p> </td> <td> <p>The average number of packets in a sub flow in the backward direction</p> </td> </tr> <tr> <td> <p>fw_subflow_bytes</p> </td> <td> <p>The average number of bytes in a sub flow in the forward direction</p> </td> </tr> <tr> <td> <p>bw_subflow_bytes</p> </td> <td> <p>The average number of bytes in a sub flow in the backward direction</p> </td> </tr> <tr> <td> <p>flow start timestamp</p> </td> <td> <p>The timestamp of the flow. It is defined with the first relevant packet.</p> </td> </tr> <tr> <td> <p>fw avg bytes/bulk</p> </td> <td> <p>Average number of bytes bulk rate in the forward direction</p> </td> </tr> <tr> <td> <p>bw avg bytes/bulk</p> </td> <td> <p>Average number of bytes bulk rate in the backyard direction</p> </td> </tr> <tr> <td> <p>fw avg bulk rate</p> </td> <td> <p>Average number of bulk rate in the forward direction</p> </td> </tr> <tr> <td> <p>bw avg bulk rate</p> </td> <td> <p>Average number of bulk rate in the backyard direction</p> </td> </tr> <tr> <td> <p>fw avg packets/bulk</p> </td> <td> <p>Average number of packets bulk rate in the forward direction</p> </td> </tr> <tr> <td> <p>bw avg packets/bulk</p> </td> <td> <p>Average number of packets bulk rate in the backyard direction</p> </td> </tr> <tr> <td> <p>init fw window bytes</p> </td> <td> <p>The window size of the first packet in the forward direction</p> </td> </tr> <tr> <td> <p>init bw window bytes</p> </td> <td> <p>The window size of the first packet in the backyard direction</p> </td> </tr> <tr> <td> <p>fw TCP total header length</p> </td> <td> <p>The length of the TCP headers in the forward direction</p> </td> </tr> <tr> <td> <p>bw TCP total header length</p> </td> <td> <p>The length of the TCP headers in the backyard direction</p> </td> </tr> <tr> <td> <p>cot=1</p> </td> <td> <p>The total number of the IEC 60870-5-104 packets where COT = 1 (periodic,cyclic)</p> </td> </tr> <tr> <td> <p>cot=2</p> </td> <td> <p>The total number of the IEC 60870-5-104 packets where COT = 2 (background interrogation)</p> </td> </tr> <tr> <td> <p>cot=3</p> </td> <td> <p>The total number of the IEC 60870-5-104 packets where COT = 3 (spontaneous)</p> </td> </tr> <tr> <td> <p>cot=4</p> </td> <td> <p>The total number of the IEC 60870-5-104 packets where COT = 4 (initialized)</p> </td> </tr> <tr> <td> <p>cot=5</p> </td> <td> <p>The total number of the IEC 60870-5-104 packets where COT = 5 (interrogation)</p> </td> </tr> <tr> <td> <p>cot=6</p> </td> <td> <p>The total number of the IEC 60870-5-104 packets where COT = 6 (activation)</p> </td> </tr> <tr> <td> <p>cot=7</p> </td> <td> <p>The total number of the IEC 60870-5-104 packets where COT = 7 (confirmation activation)</p> </td> </tr> <tr> <td> <p>cot=8</p> </td> <td> <p>The total number of the IEC 60870-5-104 packets where COT = 8 (deactivation)</p> </td> </tr> <tr> <td> <p>cot=9</p> </td> <td> <p>The total number of the IEC 60870-5-104 packets where COT = 9 (confirmation deactivation)</p> </td> </tr> <tr> <td> <p>cot=10</p> </td> <td> <p>The total number of the IEC 60870-5-104 packets where COT = 10 (termination activation)</p> </td> </tr> <tr> <td> <p>cot=11</p> </td> <td> <p>The total number of the IEC 60870-5-104 packets where COT = 11 (feedback, caused by distant command)</p> </td> </tr> <tr> <td> <p>cot=12</p> </td> <td> <p>The total number of the IEC 60870-5-104 packets where COT = 12 (feedback, caused by local command)</p> </td> </tr> <tr> <td> <p>cot=13</p> </td> <td> <p>The total number of the IEC 60870-5-104 packets where COT = 13 (COT data transmission)</p> </td> </tr> <tr> <td> <p>cot=20</p> </td> <td> <p>The total number of the IEC 60870-5-104 packets where COT = 20 (interrogated by general interrogation)</p> </td> </tr> <tr> <td> <p>type_id_process_information_in_monitor_direction</p> </td> <td> <p>The total number of the IEC 60870-5-104 packets where TypeID is in the range 1-40</p> </td> </tr> <tr> <td> <p>type_id_process_information_in_control_direction</p> </td> <td> <p>The total number of the IEC 60870-5-104 packets where TypeID is in the range 45-51</p> </td> </tr> <tr> <td> <p>type_id_system_information_in_monitor_direction</p> </td> <td> <p>The total number of the IEC 60870-5-104 packets where TypeID is in the range 70</p> </td> </tr> <tr> <td> <p>type_id_system_information_in_control_direction</p> </td> <td> <p>The total number of the IEC 60870-5-104 packets where TypeID is in the range 100-106</p> </td> </tr> <tr> <td> <p>type_id_parameter_in_control_direction</p> </td> <td> <p>The total number of the IEC 60870-5-104 packets where TypeID is in the range 110-113</p> </td> </tr> <tr> <td> <p>type_id_file_transfer</p> </td> <td> <p>The total number of the IEC 60870-5-104 packets where TypeID is in the range 120-126</p> </td> </tr> <tr> <td> <p>Label</p> </td> <td> <p>Attack label</p> </td> </tr> </tbody> </table> <p>6.Citation</p> <p>Please cite the following paper when using this dataset:</p> <p><em>P. Radoglou-Grammatikis, K. Rompolos, P. Sarigiannidis, V. Argyriou, T. Lagkas, A. Sarigiannidis, S. Goudos and S. Wan, "Modeling, Detecting, and Mitigating Threats Against Industrial Healthcare Systems: A Combined Software Defined Networking and Reinforcement Learning Approach", in IEEE Transactions on Industrial Informatics, vol. 18, no. 3, pp. 2041-2052, March 2022, doi: 10.1109/TII.2021.3093905.</em></p> <p><a href="https://ieeexplore.ieee.org/document/9470933"><em>https://ieeexplore.ieee.org/document/9470933</em></a></p> <p>7.Acknowledgment</p> <p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreements No 101021936 (ELECTRON) and No 833955 (SDN-microSENSE).</p> <p>References</p> <ol> <li>P. Radoglou-Grammatikis, K. Rompolos, P. Sarigiannidis, V. Argyriou, T. Lagkas, A. Sarigiannidis, S. Goudos and S. Wan, "Modeling, Detecting, and Mitigating Threats Against Industrial Healthcare Systems: A Combined Software Defined Networking and Reinforcement Learning Approach", in IEEE Transactions on Industrial Informatics, vol. 18, no. 3, pp. 2041-2052, March 2022, doi: 10.1109/TII.2021.3093905.</li> <li>A. Gharib, I. Sharafaldin, A. H. Lashkari and A. A. Ghorbani, "An Evaluation Framework for Intrusion Detection Dataset," 2016 International Conference on Information Science and Security (ICISS), 2016, pp. 1-6, doi: 10.1109/ICISSEC.2016.7885840.</li> </ol> <p> </p> <p><a href="#_ftnref1">[1]</a> IEC TestServer - https://sourceforge.net/projects/iecserver/</p> <p><a href="#_ftnref2">[2]</a> QTester104 - https://sourceforge.net/projects/qtester104/</p> <p><a href="#_ftnref3">[3]</a> Kali Linux - https://www.kali.org/</p> <p><a href="#_ftnref4">[4]</a> Metasploit - https://www.metasploit.com/</p> <p><a href="#_ftnref5">[5]</a> OpenMUC j60870 - https://www.openmuc.org/iec-60870-5-104/</p> <p><a href="#_ftnref6">[6]</a> Ettercap - https://www.ettercap-project.org/</p> <p><a href="#_ftnref7">[7]</a> CICFlowMeter - https://github.com/ahlashkari/CICFlowMeter</p> <p><a href="#_ftnref8">[8]</a> This parser is provided after a communication with the authors.</p> <p><a href="#_ftnref9">[9]</a> Scapy - https://scapy.net/</p>
Demo data and models for: Automated speech detection in eco-acoustic data enables privacy protection and human disturbance quantification
<p>Folder containing a <strong>demo dataset</strong> and the <strong>model weights</strong> resulting from the ecoVAD pipeline. The data contained in this folder allows for full reproducibility of the pipeline described on the <a href="https://github.com/NINAnor/ecoVAD">ecoVAD GitHub repository</a>.</p> <p>If you have any questions or issues with the dataset, please open an issue on the ecoVAD GitHub repository.</p>
Change Detection in Dynamic Environments by Means of a Robotic Swarm - Digital Appendix
<p>Digital Appendix for my thesis Change Detection in Dynamic Environments by Means of a Robotic Swarm.</p> <p>In the zip-file you find a README that guides you through the structure and where to find the plots for the respective experiments.</p> <p><strong>Change Detection of Dynamic Environments by Means of a Robotic Swarm - Digital Appendix</strong></p> <p>The following folder structure holds all research data of my conducted experiments(h5-logfiles and plots). The Python-Script "show_h5.py" can be used to read out the logfile in h5-format (<em>$python3 show_h5.py expample_logfilename.h5</em>). However, this shouldn't be necessary because all plots are already generated.</p> <p>To find the results you want to see, this is a small guide through the structure:</p> <ol> <li> <p>First the trials are divided into the respective methods (BOCPD, PELT, DBB, DBBCPD). In the folders you find the experiments for the specific method.</p> </li> <li> <p>In the folders of BOCPD and PELT you find the results for the different feedback types and their combinations. The id for each feedback is noted in parentheses (e.g. XX_(id)_feedback_description). Feedback combinations have their ids added up (e.g. XX_(id1+...+idn)_feedback_description).</p> </li> <li> <p>In the folder to each feedback type the different test trials can be found. This means varying environment difficulties and parameter settings. In the name of the folders this information can be found (e.g. XX_method_environmentdifficulty_parametersetting).</p> </li> </ol> <p>All experiments follow the same procedure as long as it is stated otherwise. Each trial consists of 20 individual runs with a duration of 6000 seconds. At half time (3000 s) a change to the opposite fill ratio occurs (fill ratio of 1.0 defines a completely white and one of 0.0 a completely black environment).</p> <p><strong>Environment difficulty</strong></p> <ul> <li> <p>0901 --> easy environment, fill ratio changed from 0.9 to 0.1</p> </li> <li> <p>0703 --> easy environment, fill ratio changed from 0.7 to 0.3</p> </li> <li> <p>0604 --> easy environment, fill ratio changed from 0.6 to 0.4</p> </li> <li> <p>055045 --> easy environment, fill ratio changed from 0.55 to 0.45</p> </li> </ul> <p><strong>Parameter Setting</strong></p> <p>The setting is in the name of the folder composed of: feedbackID: intervalLength amountNeighbors</p> <ul> <li> <p>3c:50s3n --> feedback 3c with a 50s interval and 3 neighbors</p> </li> </ul> <p>In these folders all plots of the respective runs can be found showing a Boxplot of all 20 runs and for each run the swarm belief, the decision distribution and the reset histogram (before/after the change)</p>
Cherry Tree Disease Detection Dataset
<p>1. Introduction</p> <p>This cherry tree disease detection dataset is a multimodal, multi-angle dataset which was constructed for monitoring the growth of cherry trees, including stress analysis and prediction. An orchard of cherry trees is considered in the area of Western Macedonia, where 577 cherry trees were recorded in a full crop season starting from Jul. 2021 to Jul. 2022. The dataset includes a) aerial / Unmanned Aerial Vehicle (UAV) images, b) ground RGB images/photos, and c) ground multispectral images/photos. Two agronomist experts annotated the dataset by identifying a stress, which in this case is a common disease in cherry trees known as Armillaria [1][2].</p> <p>2. Citation</p> <p>Please cite the following papers when using this dataset:</p> <p>C. Chaschatzis, C. Karaiskou, E. Mouratidis, E. Karagiannis, and P. Sarigiannidis, “Detection and Characterization of Stressed Sweet Cherry Tissues Using Machine Learning”, Drones, vol. 6, no. 1, 2022.</p> <p>P. Radoglou-Grammatikis, P. Sarigiannidis, T. Lagkas, & I. Moscholios, “A compilation of UAV applications for precision agriculture,” Computer Networks, vol. 172, no. 107148, 2020.</p> <p>A. Lytos, T. Lagkas, P. Sarigiannidis, M. Zervakis, & G. Livanos, “Towards smart farming: Systems, frameworks and exploitation of multiple sources,” Computer Networks, vol. 172, no. 107147, 2020.</p> <p>3. Cherry tree mapping</p> <p>In this dataset, an orchard of cherry trees is considered in the area of Western Macedonia, where 577 cherry trees were recorded in a full crop season starting from Jul. 2021 to Jul. 2022. The tree mapping within the orchard is depicted in Fig. 1. (please refer to the ReadMe file), where each circle represents a cherry tree. Labels on the circles (green, red etc) will be elaborated in the following Sections. The five time periods, where the orchard was recorded are: 8th of Jul. 2021, 16th of Sep. 2021, 3rd of Nov. 2021, 26th of May 2022, and 13th of Jul. 2022, providing data to a full year of life cycle.</p> <p>4. Dataset Modalities</p> <p>The dataset includes a) aerial / Unmanned Aerial Vehicle (UAV) images, b) ground RGB images/photos, and c) ground multispectral images/photos. Two agronomist experts annotated the dataset by identifying a stress, which in this case is a common disease in cherry trees known as Armillaria [1][2]. In particular, the following modalities are featured in the dataset:</p> <p>Ground RGB images</p> <p>Ground multispectral images</p> <p>UAV/Aerial images (RGB, multispectral, and NDVI).</p> <p>These modalities represent the cherry tree cultivation in many levels. Each modality describes the same object (cherry tree) within the dataset, i.e., for each tree within. For example, Fig. 2 (please refer to the ReadMe file) show RGB images, Fig. 3 (please refer to the ReadMe file) illustrates multispectral images, and Fig. 4 (please refer to the ReadMe file) provides UAV images. All images show the same cherry trees under three (RGB, multispectral, and UAV) aspects.</p> <p>5. Dataset Collection & Annotation</p> <p>This dataset was annotated by two agronomist experts in terms of disease stage (Armillaria). In particular, they annotated each cherry tree, one by one, in four levels of disease stage:</p> <p>Healthy: the cherry tree is completely healthy;</p> <p>Stage1: Armillaria is present in light form in the cherry tree;</p> <p>Stage2: Armillaria is present in advanced form;</p> <p>Stage3: the cherry tree is killed due to Armillaria.</p> <p>The annotation process was considered by each one of the underlying modalities (RGB, multispectral and UAV/aerial).</p> <p>5.1 Image Collection</p> <p>The image collection is depicted in the following image (please refer to the ReadMe file) in terms of the three modalities (aerial / Unmanned Aerial Vehicle (UAV) images, ground RGB images/photos, and ground multispectral images/photos).</p> <p>5.2 Dataset Overview</p> <p>The dataset overview is depicted in Table 1 (please refer to the ReadMe file).</p> <p>6. Structure and Format</p> <p>6.1 Dataset Structure</p> <p>The provided dataset has the following structure (please refer to the ReadMe file).</p> <p>6.2 Guide to edit the *.tif files</p> <p>The Aerial/UAV images contain images obtained from the UAV camera in the .tif format. To open these images, you will need the QGIS or other relevant program, or load them by using the corresponding python libraries. Please follow the steps below:</p> <p>Open QGIS</p> <p>Locate the browser window in QGIS</p> <p>Navigate to the folder that contains the images and select all the images in the layer.</p> <p>Once you have selected the images, select Add Layer to Project, and the selected image will be added to your map.</p> <p>For accessing the Image data with the OpenCV python library the following code example is provided (please refer to the ReadMe file).</p> <p>7. Acknowledgment</p> <p>This work was co‐financed by the European Regional Development Fund of the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH – CREATE – INNOVATE (project code: Τ1EDK-04759).</p> <p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreements No. 957406 (TERMINET).</p> <p>References</p> <p>[1] Devkota, P.; Iezzoni, A.; Gasic, K.; Reighard, G.; Hammerschmidt, R. Evaluation of the susceptibility of Prunus rootstock genotypes to Armillaria and Desarmillaria species. Eur. J. Plant Pathol. 2020, 158, 177–193.</p> <p>[2] Devkota, P.; Hammerschmidt, R. “The infection process of Armillaria mellea and Armillaria solidipes”. Physiol. Mol. Plant Pathol. 2020, 112, 101543.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.