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MATLAB Code for "Joint Image Processing with Learning-Driven Data Representation and Model Behavior for Non-Intrusive Anemia Diagnosis in Pediatric Patients"
<p>This MATLAB code is part of the study titled <em>"Joint Image Processing with Learning-Driven Data Representation and Model Behavior for Non-Intrusive Anemia Diagnosis in Pediatric Patients"</em>, which has been accepted for publication in the <em>Journal of Imaging (MDPI)</em>. The code supports image processing, feature extraction, and deep learning model training (including LSTM and RexNet) to classify pediatric patients as anemic or non-anemic based on palm, conjunctival, and fingernail images. Full study details are available in this paper:</p> <p>Berghout T. Joint Image Processing with Learning-Driven Data Representation and Model Behavior for Non-Intrusive Anemia Diagnosis in Pediatric Patients. <em>Journal of Imaging</em>. 2024; 10(10):245. <a href="https://doi.org/10.3390/jimaging10100245">https://doi.org/10.3390/jimaging10100245 </a></p> <p>The datsets use in this work are:</p> <p>Asare, J. W., Appiahene, P. & Donkoh, E. (2022). Anemia Detection using Palpable Palm Image Datasets from Ghana. Mendeley Data. https://doi.org/10.17632/ccr8cm22vz.1<br>Asare, J. W., Appiahene, P. & Donkoh, E. (2023). CP-AnemiC (A Conjunctival Pallor) Dataset from Ghana. Mendeley Data. https://doi.org/10.17632/m53vz6b7fx.1<br>Asare, J. W., Appiahene, P. & Donkoh, E. (2020). Detection of Anemia using Colour of the Fingernails Image Datasets from Ghana. Mendeley Data. https://doi.org/10.17632/2xx4j3kjg2.1</p>
Data from: Male and female Rufous-and-white Wrens do not match song types with same-sex rivals during simulated territorial intrusions
In birds with song repertoires, song-type matching occurs when an individual responds to another individual's song by producing the same song type. Song-type matching has been described in multiple bird species and a growing body of evidence suggests that song-type matching may serve as a conventional signal of aggression, particularly in male birds in the temperate zone. Few studies have investigated song-type matching in tropical birds or female birds, in spite of the fact that avian biodiversity is highest in the tropics, that female song is widespread in the tropics, and that female song is the ancestral state among songbirds. In this study of rufous-and-white wrens (Thryophilus rufalbus), a resident neotropical songbird where both sexes sing, we presented territorial males and females with playback that simulated a territorial rival producing shared and unshared songs. In response, both males and females sang matched song types at levels statistically equal to levels expected by chance. Furthermore, males and females exhibited similar levels of aggression and similar vocal behaviours in response to playback of both shared and unshared songs. These results indicate that rufous-and-white wrens do not use song-type matching in territorial conflicts as a conventional signal of aggression. We discuss alternative hypotheses for the function of song-type sharing in tropical birds. In particular, we point out that shared songs may play an important role in intra-pair communication, especially for birds where males and females combine their songs in vocal duets, and this may supersede the function of song-type matching in some tropical birds.
Data from: Sex-specific responses to territorial intrusions in a communication network: evidence from radio-tagged great tits
Signals play a key role in the ecology and evolution of animal populations, influencing processes such as sexual selection and conflict resolution. In many species, sexually selected signals have a dual function: attracting mates and repelling rivals. Yet, to what extent males and females under natural conditions differentially respond to such signals remains poorly understood, due to a lack of field studies that simultaneously track both sexes. Using a novel spatial tracking system, we tested whether or not the spatial behavior of male and female great tits (Parus major) changes in relation to the response of a territorial male neighbor to an intruder. We tracked the spatial behavior of male and female great tits (N = 44), 1 hr before and 1 hr after simulating territory intrusions, employing automatized Encounternet radio-tracking technology. We recorded the spatial and vocal response of the challenged males and quantified attraction and repulsion of neighboring males and females to the intrusion site. We additionally quantified the direct proximity network of the challenged male. The strength of a male's vocal response to an intruder induced sex-dependent movements in the neighborhood, via female attraction and male repulsion. Stronger vocal responders were older and in better body condition. The proximity networks of the male vocal responders, including the number of sex-dependent connections and average time spent with connections, did not change directly following the intrusion. The effects on neighbor movements suggest that the strength of a male's vocal response can provide relevant social information to both the males and the females in the neighborhood, resulting in both sexes adjusting their spatial behavior in contrasting ways, while the social proximity network remained stable. Moreover, our study underlines the importance of the "silent" eavesdroppers within communication networks for studying the dual functioning and evolution of sexually selected signals.
MITgcm model setup and output for "Antarctic Slope Current modulates ocean heat intrusions towards Totten Glacier"
<p>MITgcm model setup and output for "Antarctic Slope Current modulates ocean heat intrusions towards Totten Glacier</p> <p>Here, it contains the results of the East Antarctic simulation from 1992-2016. Model grid is lat-lon similar to LLC1080 grid resolution roughly 3-4 km in the region. See Nakayama et al., submitted to GRL for detail. </p> <p><strong>(Contents)</strong><br> code.zip (code to run this simulation)<br> input.zip (input file required for this simulation)<br> results_zenodo.zip (due to size limit of 50GB, please check <a href="https://ecco.jpl.nasa.gov/drive/files/ECCO2/LatLon_East_Antartic">https://ecco.jpl.nasa.gov/drive/files/ECCO2/LatLon_East_Antarctic</a> for complete model output. Complete datasets can also be obtained by rerunning the simulation.)</p> <p><strong>(How to build and run)</strong><br> mkdir build<br> ./../../tools/genmake2 -of ../../../tools/build_options/linux_amd64_ifort+mpi_ice_nas -mpi -mods ../code/<br> make depend<br> make -j 16<br> cd ..<br> mkdir test<br> cd test<br> ln -sf ../input/* .<br> ln -sf /nobackup/hzhang1/forcing/era_xx/ .<br> cp ../build/mitgcm_uv .<br> qsub run_omp_high_t1.pbs</p>
DNP3 Intrusion Detection Dataset
<p>1.Introduction</p> <p>In the digital era of the Industrial Internet of Things (IIoT), the conventional Critical Infrastructures (CIs) are transformed into smart environments with multiple benefits, such as pervasive control, self-monitoring and self-healing. However, this evolution is characterised by several cyberthreats due to the necessary presence of insecure technologies. DNP3 is an industrial communication protocol which is widely adopted in the CIs of the US. In particular, DNP3 allows the remote communication between Industrial Control Systems (ICS) and Supervisory Control and Data Acquisition (SCADA). It can support various topologies, such as Master-Slave, Multi-Drop, Hierarchical and Multiple-Server. Initially, the architectural model of DNP3 consists of three layers: (a) Application Layer, (b) Transport Layer and (c) Data Link Layer. However, DNP3 can be now incorporated into the Transmission Control Protocol/Internet Protocol (TCP/IP) stack as an application-layer protocol. However, similarly to other industrial protocols (e.g., Modbus and IEC 60870-5-104), DNP3 is characterised by severe security issues since it does not include any authentication or authorisation mechanisms. More information about the DNP3 security issue is provided in [1-3]. This dataset contains labelled Transmission Control Protocol (TCP) / Internet Protocol (IP) network flow statistics (Common-Separated Values - CSV format) and DNP3 flow statistics (CSV format) related to 9 DNP3 cyberattacks. These cyberattacks are focused on DNP3 unauthorised commands and Denial of Service (DoS). The network traffic data are provided through Packet Capture (PCAP) files. Consequently, this dataset can be used to implement Artificial Intelligence (AI)-powered Intrusion Detection and Prevention (IDPS) systems that rely on Machine Learning (ML) and Deep Learning (DL) techniques.</p> <p>2.Instructions</p> <p>This DNP3 Intrusion Detection Dataset was implemented following the methodological frameworks of A. Gharib et al. in [4] and S. Dadkhah et al in [5], 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) eight industrial entities, (b) one Human Machine Interfaces (HMI) and (c) three cyberattackers was used to implement this DNP3 Intrusion Detection Dataset. In particular, the following cyberattacks were implemented.</p> <ul> <li>On Thursday, May 14, 2020, the <strong>DNP3 Disable Unsolicited Messages Attack</strong> was executed for 4 hours.</li> <li>On Friday, May 15, 2020, the <strong>DNP3 Cold Restart Message Attack</strong> was executed for 4 hours.</li> <li>On Friday, May 15, 2020, the <strong>DNP3 Warm Restart Message Attack</strong> was executed for 4 hours.</li> <li>On Saturday, May 16, 2020, the <strong>DNP3 Enumerate Attack</strong> was executed for 4 hours.</li> <li>On Saturday, May 16, 2020, the <strong>DNP3 Info Attack</strong> was executed for 4 hours.</li> <li>On Monday, May 18, 2020, the <strong>DNP3 Initialisation Attack</strong> was executed for 4 hours.</li> <li>On Monday, May 18, 2020, the <strong>Man In The Middle (MITM)-DoS Attack</strong> was executed for 4 hours.</li> <li>On Monday, May 18, 2020, the <strong>DNP3</strong> <strong>Replay Attack</strong> was executed for 4 hours.</li> <li>On Tuesday, May 19, 2020, the <strong>DNP3 Stop Application Attack</strong> was executed for 4 hours.</li> </ul> <p>The aforementioned DNP3 cyberattacks were executed, utilising penetration testing tools, such as Nmap and Scapy. For each attack, a relevant folder is provided, including the network traffic and the network flow statistics for each entity. In particular, for each cyberattack, a folder is given, providing (a) the pcap files for each entity, (b) the Transmission Control Protocol (TCP)/ Internet Protocol (IP) network flow statistics for 120 seconds in a CSV format and (c) the DNP3 flow statistics for each entity (using different timeout values in terms of second (such as 45, 60, 75, 90, 120 and 240 seconds)). The TCP/IP network flow statistics were produced by using the CICFlowMeter, while the DNP3 flow statistics were generated based on a Custom DNP3 Python Parser, taking full advantage of Scapy.</p> <p>3. Dataset Structure</p> <p>The dataset consists of the following folders:</p> <ul> <li><strong>20200514_DNP3_Disable_Unsolicited_Messages_Attack</strong>: It includes the pcap and CSV files related to the DNP3 Disable Unsolicited Message attack.</li> <li><strong>20200515_DNP3_Cold_Restart_Attack</strong>: It includes the pcap and CSV files related to the DNP3 Cold Restart attack.</li> <li><strong>20200515_DNP3_Warm_Restart_Attack</strong>: It includes the pcap and CSV files related to DNP3 Warm Restart attack.</li> <li><strong>20200516_DNP3_Enumerate</strong>: It includes the pcap and CSV files related to the DNP3 Enumerate attack.</li> <li><strong>20200516_DNP3_Ιnfo</strong>: It includes the pcap and CSV files related to the DNP3 Info attack.</li> <li><strong>20200518_DNP3_Initialize_Data_Attack</strong>: It includes the pcap and CSV files related to the DNP3 Data Initialisation attack.</li> <li><strong>20200518_DNP3_MITM_DoS</strong>: It includes the pcap and CSV files related to the DNP3 MITM-DoS attack.</li> <li><strong>20200518_DNP3_Replay_Attack</strong>: It includes the pcap and CSV files related to the DNP3 replay attack.</li> <li><strong>20200519_DNP3_Stop_Application_Attack</strong>: It includes the pcap and CSV files related to the DNP3 Stop Application attack.</li> <li><strong>Training_Testing_Balanced_CSV_Files</strong>: It includes balanced CSV files from CICFlowMeter and the Custom DNP3 Python Parser that could be utilised for training ML and DL methods. Each folder includes different sub-folder for the corresponding flow timeout values used by the DNP3 Python Custom Parser. For CICFlowMeter, only the timeout value of 120 seconds was used.</li> </ul> <p>Each folder includes respective subfolders 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 DNP3 network traffic (pcap file) related to this entity/device during each attack, (b) the TCP/IP network flow statistics (CSV file) generated by CICFlowMeter for the timeout value of 120 seconds and finally (c) the DNP3 flow statistics (CSV file) from the Custom DNP3 Python Parser. Finally, it is noteworthy that the network flows from both CICFlowMeter and Custom DNP3 Python Parser in each CSV file are <strong>labelled</strong> based on the DNP3 cyberattacks executed for the generation of this dataset. The description of these attacks is provided in the following section, while the various features from CICFlowMeter and Custom DNP3 Python Parser are presented in Section 5.</p> <p>4.Testbed & DNP3 Attacks</p> <p>The following figure shows the testbed utilised for the generation of this dataset. It is composed of eight industrial entities that play the role of the DNP3 outstations/slaves, such as Remote Terminal Units (RTUs) and Intelligent Electron Devices (IEDs). Moreover, there is another workstation which plays the role of the Master station like a Master Terminal Unit (MTU). For the communication between, the DNP3 outstations/slaves and the master station, opendnp3 was used.</p> <p> </p> <p>Table 1: DNP3 Attacks Description</p> <table> <tbody> <tr> <td> <p><strong>DNP3 Attack </strong></p> </td> <td> <p><strong>Description</strong></p> </td> <td> <p><strong>Dataset Folder</strong></p> </td> </tr> <tr> <td> <p>DNP3 Disable Unsolicited Message Attack</p> </td> <td> <p>This attack targets a DNP3 outstation/slave, establishing a connection with it, while acting as a master station. The false master then transmits a packet with the DNP3 Function Code 21, which requests to disable all the unsolicited messages on the target.</p> </td> <td> <p>20200514_DNP3_Disable_Unsolicited_Messages_Attack</p> </td> </tr> <tr> <td> <p>DNP3 Cold Restart Attack</p> </td> <td> <p>The malicious entity acts as a master station and sends a DNP3 packet that includes the “Cold Restart” function code. When the target receives this message, it initiates a complete restart and sends back a reply with the time window before the restart process.</p> </td> <td> <p>20200515_DNP3_Cold_Restart_Attack</p> </td> </tr> <tr> <td> <p>DNP3 Warm Restart Attack</p> </td> <td> <p>This attack is quite similar to the “Cold Restart Message”, but aims to trigger a partial restart, re-initiating a DNP3 service on the target outstation.</p> </td> <td> <p>20200515_DNP3_Warm_Restart_Attack</p> </td> </tr> <tr> <td> <p>DNP3 Enumerate Attack</p> </td> <td> <p>This reconnaissance attack aims to discover which DNP3 services and functional codes are used by the target system.</p> </td> <td> <p>20200516_DNP3_Enumerate</p> </td> </tr> <tr> <td> <p>DNP3 Info Attack</p> </td> <td> <p>This attack constitutes another reconnaissance attempt, aggregating various DNP3 diagnostic information related the DNP3 usage.</p> </td> <td> <p>20200516_DNP3_Ιnfo</p> </td> </tr> <tr> <td> <p>Data Initialisation Attack</p> </td> <td> <p>This cyberattack is related to Function Code 15 (Initialize Data). It is an unauthorised access attack, which demands from the slave to re-initialise possible configurations to their initial values, thus changing potential values defined by legitimate masters</p> </td> <td> <p>20200518_Initialize_Data_Attack</p> </td> </tr> <tr> <td> <p>MITM-DoS Attack</p> </td> <td> <p>In this cyberattack, the cyberattacker is placed between a DNP3 master and a DNP3 slave device, dropping all the messages coming from the DNP3 master or the DNP3 slave.</p> </td> <td> <p>20200518_MITM_DoS</p> </td> </tr> <tr> <td> <p>DNP3 Replay Attack</p> </td> <td> <p>This cyberattack replays DNP3 packets coming from a legitimate DNP3 master or DNP3 slave.</p> </td> <td> <p>20200518_DNP3_Replay_Attack</p> </td> </tr> <tr> <td> <p>DNP3 Step Application Attack</p> </td> <td> <p>This attack is related to the Function Code 18 (Stop Application) and demands from the slave to stop its function so that the slave cannot receive messages from the master.</p> </td> <td> <p>20200519_DNP3_Stop_Application_Attack</p> </td> </tr> </tbody> </table> <p> </p> <p>5. Features</p> <p>The TCP/IP network flow statistics generated by CICFlowMeter are summarised below. <strong>T</strong><strong>he TCP/IP network flows and their statistics generated by </strong><strong>CICFlowMeter are labelled based on the DNP3 attacks described above, thus allowing the training of ML/DL models. Finally, it is worth mentioning that these statistics are generated when the flow timeout value is equal with 120 seconds.</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 DNP3 flow statistics generated by the DNP3 Python Parser are summarised below. <strong>T</strong><strong>he DNP3 flows and their statistics generated by the DNP3 Python Parser are labelled based on the DNP3 attacks described above, thus allowing the training of ML/DL models. </strong><strong>Finally, it is worth mentioning that these statistics are available for various flow timeout values, such as 45, 60, 75, 90, 120 and 240 seconds.</strong></p> <p>Table 3: DNP3 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>source IP</p> </td> <td> <p>Source IP address</p> </td> </tr> <tr> <td> <p>destination IP</p> </td> <td> <p>Destination IP address</p> </td> </tr> <tr> <td> <p>source port</p> </td> <td> <p>Source TCP/UDP Port</p> </td> </tr> <tr> <td> <p>destination 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>date</p> </td> <td> <p>Flow timestamp</p> </td> </tr> <tr> <td> <p>TotalFwdPkts</p> </td> <td> <p>The total number of the DNP3 packets in the forward direction</p> </td> </tr> <tr> <td> <p>TotalBwdPkts</p> </td> <td> <p>The total number of the DNP3 packets in the backyard direction</p> </td> </tr> <tr> <td> <p>TotLenfwdDL</p> </td> <td> <p>The total size of the DNP3 payload at the link layer in the forward direction</p> </td> </tr> <tr> <td> <p>TotLenfwdTR</p> </td> <td> <p>The total size of the DNP3 payload at the transport layer in the forward direction</p> </td> </tr> <tr> <td> <p>TotLenfwdAPP</p> </td> <td> <p>The total size of the DNP3 payload at the application layer in the forward direction</p> </td> </tr> <tr> <td> <p>TotLenbwdDL</p> </td> <td> <p>The total size of the DNP3 payload at the link layer in the backyard direction</p> </td> </tr> <tr> <td> <p>TotLenbwdTR</p> </td> <td> <p>The total size of the DNP3 payload at the transport layer in the backyard direction</p> </td> </tr> <tr> <td> <p>TotLenbwdAPP</p> </td> <td> <p>The total size of the DNP3 payload at the application layer in the backyard direction</p> </td> </tr> <tr> <td> <p>DLfwdPktLenMAX</p> </td> <td> <p>The maximum size of the DNP3 payload at the link layer in the forward direction</p> </td> </tr> <tr> <td> <p>DLfwdPktLenMIN</p> </td> <td> <p>The minimum size of the DNP3 payload at the link layer in the forward direction</p> </td> </tr> <tr> <td> <p>DLfwdPktLenMEAN</p> </td> <td> <p>The mean of the DNP3 payload at the link layer in the forward direction</p> </td> </tr> <tr> <td> <p>DLfwdPktLenSTD</p> </td> <td> <p>The standard deviation of the DNP3 payload at the link layer in the forward direction</p> </td> </tr> <tr> <td> <p>TRfwdPktLenMAX</p> </td> <td> <p>The maximum size of the DNP3 payload at the transport layer in the forward direction</p> </td> </tr> <tr> <td> <p>TRfwdPktLenMIN</p> </td> <td> <p>The minimum size of the DNP3 payload at the transport layer in the forward direction</p> </td> </tr> <tr> <td> <p>TRfwdPktLenMEAN</p> </td> <td> <p>The mean of the DNP3 payload at the transport layer in the forward direction</p> </td> </tr> <tr> <td> <p>TRfwdPktLenSTD</p> </td> <td> <p>The standard deviation of the DNP3 payload at the transport layer in the forward direction</p> </td> </tr> <tr> <td> <p>APPfwdPktLenMAX</p> </td> <td> <p>The maximum size of the DNP3 payload at the application layer in the backyard direction</p> </td> </tr> <tr> <td> <p>APPfwdPktLenMIN</p> </td> <td> <p>The minimum size of the DNP3 payload at the application layer in the backyard direction</p> </td> </tr> <tr> <td> <p>APPfwdPktLenMEAN</p> </td> <td> <p>The mean of the DNP3 payload at the application layer in the backyard direction</p> </td> </tr> <tr> <td> <p>APPfwdPktLenSTD</p> </td> <td> <p>The standard deviation of the DNP3 payload at the application layer in the backyard direction</p> </td> </tr> <tr> <td> <p>DLbwdPktLenMAX</p> </td> <td> <p>The maximum size of the DNP3 payload at the link layer in the backyard direction</p> </td> </tr> <tr> <td> <p>DLbwdPktLenMIN</p> </td> <td> <p>The minimum size of the DNP3 payload at the link layer in the backyard direction</p> </td> </tr> <tr> <td> <p>DLbwdPktLenMEAN</p> </td> <td> <p>The mean of the DNP3 payload at the link layer in the backyard direction</p> </td> </tr> <tr> <td> <p>DLbwdPktLenSTD</p> </td> <td> <p>The standard deviation of the DNP3 payload at the link layer in the backyard direction</p> </td> </tr> <tr> <td> <p>TRbwdPktLenMAX</p> </td> <td> <p>The maximum size of the DNP3 payload at the transport layer in the backyard direction</p> </td> </tr> <tr> <td> <p>TRbwdPktLenMIN</p> </td> <td> <p>The minimum size of the DNP3 payload at the transport layer in the backyard direction</p> </td> </tr> <tr> <td> <p>TRbwdPktLenMEAN</p> </td> <td> <p>The mean of the DNP3 payload at the transport layer in the backyard direction</p> </td> </tr> <tr> <td> <p>TRbwdPktLenSTD</p> </td> <td> <p>The standard deviation of the DNP3 payload at the transport layer in the backyard direction</p> </td> </tr> <tr> <td> <p>APPbwdPktLenMAX</p> </td> <td> <p>The maximum size of the DNP3 payload at the application layer in the backyard direction</p> </td> </tr> <tr> <td> <p>APPbwdPktLenMIN</p> </td> <td> <p>The minimum size of the DNP3 payload at the application layer in the backyard direction</p> </td> </tr> <tr> <td> <p>APPbwdPktLenMEAN</p> </td> <td> <p>The mean of the DNP3 payload at the application layer in the backyard direction</p> </td> </tr> <tr> <td> <p>APPbwdPktLenSTD</p> </td> <td> <p>The standard deviation of the DNP3 payload at the application layer in the backyard direction</p> </td> </tr> <tr> <td> <p>DLflowBytes/sec</p> </td> <td> <p>How many bytes of the DNP3 link-layer were transmitted per second</p> </td> </tr> <tr> <td> <p>TRflowBytes/sec</p> </td> <td> <p>How many bytes of the DNP3 transport layer were transmitted per second</p> </td> </tr> <tr> <td> <p>APPflowBytes/sec</p> </td> <td> <p>How many bytes of the DNP3 application layer were transmitted per second</p> </td> </tr> <tr> <td> <p>FlowPkts/sec</p> </td> <td> <p>How many DNP3 packets were transmitted per second</p> </td> </tr> <tr> <td> <p>FlowIAT_MEAN</p> </td> <td> <p>The mean of the DNP3 packets interarrival time</p> </td> </tr> <tr> <td> <p>FlowIAT_STD</p> </td> <td> <p>The standard deviation of the DNP3 packets interarrival time</p> </td> </tr> <tr> <td> <p>FlowIAT_MAX</p> </td> <td> <p>The maximum value of the DNP3 packets interarrival time</p> </td> </tr> <tr> <td> <p>FlowIAT_MIN</p> </td> <td> <p>The minimum value of the DNP3 packets interarrival time</p> </td> </tr> <tr> <td> <p>TotalFwdIAT</p> </td> <td> <p>The sum of the DNP3 packets interarrival time in the forward direction</p> </td> </tr> <tr> <td> <p>fwdIAT_MEAN</p> </td> <td> <p>The mean of the DNP3 packets interarrival time in the forward direction</p> </td> </tr> <tr> <td> <p>fwdIAT_STD</p> </td> <td> <p>The standard deviation of the DNP3 packets interarrival time in the forward direction</p> </td> </tr> <tr> <td> <p>fwdIAT_MAX</p> </td> <td> <p>The maximum value of the DNP3 packets interarrival time in the forward direction</p> </td> </tr> <tr> <td> <p>fwdIAT_MIN</p> </td> <td> <p>The minimum value of the DNP3 packets interarrival time in the forward direction</p> </td> </tr> <tr> <td> <p>TotalBwdIAT</p> </td> <td> <p>The sum of the DNP3 packets interarrival time in the backyard direction</p> </td> </tr> <tr> <td> <p>bwdIAT_MEAN</p> </td> <td> <p>The mean of the DNP3 packets interarrival time in the backyard direction</p> </td> </tr> <tr> <td> <p>bwdIAT_STD</p> </td> <td> <p>The standard deviation of the DNP3 packets interarrival time in the backyard direction</p> </td> </tr> <tr> <td> <p>bwdIAT_MAX</p> </td> <td> <p>The maximum value of the DNP3 packets interarrival time in the backyard direction</p> </td> </tr> <tr> <td> <p>bwdIAT_MIN</p> </td> <td> <p>The minimum value of the DNP3 packets interarrival time in the backyard direction</p> </td> </tr> <tr> <td> <p>DLfwdHdrLen</p> </td> <td> <p>The sum of the DNP3 headers at the link layer in the forward direction</p> </td> </tr> <tr> <td> <p>TRfwdHdrLen</p> </td> <td> <p>The sum of the DNP3 headers at the transport layer in the forward direction</p> </td> </tr> <tr> <td> <p>APPfwdHdrLen</p> </td> <td> <p>The sum of the DNP3 headers at the application layer in the forward direction</p> </td> </tr> <tr> <td> <p>DLbwdHdrLen</p> </td> <td> <p>The sum of the DNP3 headers at the link layer in the backyard direction</p> </td> </tr> <tr> <td> <p>TRbwdHdrLen</p> </td> <td> <p>The sum of the DNP3 headers at the transport layer in the backyard direction</p> </td> </tr> <tr> <td> <p>APPbwdHdrLen</p> </td> <td> <p>The sum of the DNP3 headers at the</p> <p>application layer in the backyard direction</p> </td> </tr> <tr> <td> <p>fwdPkts/sec</p> </td> <td> <p>How many DNP3 packets per second in the forward direction</p> </td> </tr> <tr> <td> <p>bwdPkts/sec</p> </td> <td> <p>How many DNP3 packets per second in the backyard direction</p> </td> </tr> <tr> <td> <p>DLpktLenMEAN</p> </td> <td> <p>The mean of the bytes at the DNP3 link layer</p> </td> </tr> <tr> <td> <p>DLpktLenMIN</p> </td> <td> <p>The minimum value of the bytes at the DNP3 link layer</p> </td> </tr> <tr> <td> <p>DLpktLenMAX</p> </td> <td> <p>The maximum value of the bytes at the DNP3 link layer</p> </td> </tr> <tr> <td> <p>DLpktLenSTD</p> </td> <td> <p>The standard deviation of the bytes at the DNP3 link layer</p> </td> </tr> <tr> <td> <p>DLpktLenVAR</p> </td> <td> <p>The variance of the bytes at the DNP3 link layer</p> </td> </tr> <tr> <td> <p>TRpktLenMEAN</p> </td> <td> <p>The mean of the bytes at the DNP3 transport layer</p> </td> </tr> <tr> <td> <p>TRpktLenMIN</p> </td> <td> <p>The minimum value of the bytes at the DNP3 transport layer</p> </td> </tr> <tr> <td> <p>TRpktLenMAX</p> </td> <td> <p>The maximum value of the bytes at the DNP3 transport layer</p> </td> </tr> <tr> <td> <p>TRpktLenSTD</p> </td> <td> <p>The standard deviation of the bytes at the DNP3 transport layer</p> </td> </tr> <tr> <td> <p>TRpktLenVAR</p> </td> <td> <p>The variance of the bytes at the DNP3 transport layer</p> </td> </tr> <tr> <td> <p>APPpktLenMEAN</p> </td> <td> <p>The mean of the bytes at the DNP3 application layer</p> </td> </tr> <tr> <td> <p>APPpktLenMIN</p> </td> <td> <p>The minimum value of the bytes at the DNP3 application layer</p> </td> </tr> <tr> <td> <p>APPpktLenMAX</p> </td> <td> <p>The maximum value of the bytes at the DNP3 application layer</p> </td> </tr> <tr> <td> <p>APPpktLenSTD</p> </td> <td> <p>The standard deviation of the bytes at the DNP3 application layer</p> </td> </tr> <tr> <td> <p>APPpktLenVAR</p> </td> <td> <p>The variance of the bytes at the DNP3 application layer</p> </td> </tr> <tr> <td> <p>ActiveMEAN</p> </td> <td> <p>The time-mean where the flow was active</p> </td> </tr> <tr> <td> <p>ActiveSTD</p> </td> <td> <p>The time standard deviation where the flow was active</p> </td> </tr> <tr> <td> <p>ActiveMAX</p> </td> <td> <p>The maximum value of the time where the flow is active</p> </td> </tr> <tr> <td> <p>ActiveMIN</p> </td> <td> <p>The maximum value of the time where the flow is idle.</p> </td> </tr> <tr> <td> <p>IdleMEAN</p> </td> <td> <p>The time-mean where the flow was idle before becoming active</p> </td> </tr> <tr> <td> <p>IdleSTD</p> </td> <td> <p>The standard deviation of the time where the flow was idle before becoming active</p> </td> </tr> <tr> <td> <p>IdleMAX</p> </td> <td> <p>The maximum value of the time where the flow was idle before becoming active</p> </td> </tr> <tr> <td> <p>IdleMIN</p> </td> <td> <p>The minimum value of the time where the flow was idle before becoming active</p> </td> </tr> <tr> <td> <p>frameSrc</p> </td> <td> <p>The source MAC address</p> </td> </tr> <tr> <td> <p>frameDst</p> </td> <td> <p>The destination MAC address</p> </td> </tr> <tr> <td> <p>TotPktsInFlow</p> </td> <td> <p>The total number of the DNP3 packets</p> </td> </tr> <tr> <td> <p>firstPacketDIR</p> </td> <td> <p>Whether the flow was initiated by a DNP3 master device or DNP3 slave device</p> </td> </tr> <tr> <td> <p>mostCommonREQ_FUNC_CODE</p> </td> <td> <p>The DNP3 function code which was used mostly in the DNP3 request packets</p> </td> </tr> <tr> <td> <p>mostCommonRESP_FUNC_CODE</p> </td> <td> <p>The DNP3 function code which was used mostly in the DNP3 response packets</p> </td> </tr> <tr> <td> <p>corruptConfigFragments</p> </td> <td> <p>How many responses were sent by the slave, setting the corruptConfig bit in the IIN value</p> </td> </tr> <tr> <td> <p>deviceTroubleFragments</p> </td> <td> <p>How many responses were sent by the slave, setting the deviceTrouble bit in the IIN value</p> </td> </tr> <tr> <td> <p>deviceRestartFragments</p> </td> <td> <p>How many responses were sent by the slave, setting the deviceRestart bit in the IIN value</p> </td> </tr> <tr> <td> <p>pktsFromMASTER</p> </td> <td> <p>How many packets that transmitted by a DNP3 master device</p> </td> </tr> <tr> <td> <p>pktsFromSLAVE</p> </td> <td> <p>How many packets that transmitted by a DNP3 slave device</p> </td> </tr> <tr> <td> <p>Label</p> </td> <td> <p>Attack label</p> </td> </tr> </tbody> </table> <p>6.Citation</p> <p>The users of this dataset are kindly asked to cite the following papers as follows.</p> <p>V. Kelli et al., "Attacking and Defending DNP3 ICS/SCADA Systems", 2022 18th International Conference on Distributed Computing in Sensor Systems (DCOSS), 2022, pp. 183-190, doi: 10.1109/DCOSS54816.2022.00041.</p> <p>V. Kelli, P. Radoglou-Grammatikis, T. Lagkas, E. K. Markakis and P. Sarigiannidis, "Risk Analysis of DNP3 Attacks", 2022 IEEE International Conference on Cyber Security and Resilience (CSR), 2022, pp. 351-356, doi: 10.1109/CSR54599.2022.9850291.</p> <p>P. Radoglou-Grammatikis, P. Sarigiannidis, G. Efstathopoulos, P.-A.Karypidis, and A. Sarigiannidis, "Diderot: An intrusion detection and prevention system for dnp3-based scada systems", in Proceedings of the15th International Conference on Availability, Reliability and Security, ser. ARES ’20.New York, NY, USA: Association for Computing Machinery, 2020, doi: 10.1145/3407023.3409314.</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>V. Kelli et al., "Attacking and Defending DNP3 ICS/SCADA Systems", 2022 18th International Conference on Distributed Computing in Sensor Systems (DCOSS), 2022, pp. 183-190, doi: 10.1109/DCOSS54816.2022.00041.</li> <li>V. Kelli, P. Radoglou-Grammatikis, T. Lagkas, E. K. Markakis and P. Sarigiannidis, "Risk Analysis of DNP3 Attacks", 2022 IEEE International Conference on Cyber Security and Resilience (CSR), 2022, pp. 351-356, doi: 10.1109/CSR54599.2022.9850291.</li> <li>P. Radoglou-Grammatikis, P. Sarigiannidis, G. Efstathopoulos, P.-A.Karypidis, and A. Sarigiannidis, "Diderot: An intrusion detection and prevention system for dnp3-based scada systems", in Proceedings of the15th International Conference on Availability, Reliability and Security, ser. ARES ’20.New York, NY, USA: Association for Computing Machinery, 2020, doi: 10.1145/3407023.3409314.</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> <li>S. Dadkhah, H. Mahdikhani, P. K. Danso, A. Zohourian, K. A. Truong and A. A. Ghorbani, "Towards the Development of a Realistic Multidimensional IoT Profiling Dataset", 2022 19th Annual International Conference on Privacy, Security & Trust (PST), 2022, pp. 1-11, doi: 10.1109/PST55820.2022.9851966.</li> </ol>
A non-intrusive nuclear data uncertainty propagation study for the ARC fusion reactor design
<p>Dataset of perturbed nuclear data files employed in the Serpent Monte Carlo code for the nuclear data uncertainty propagation analysis of the Affordable, Robust and Compact (ARC) fusion reactor. </p>
5GC PFCP Intrusion Detection Dataset
<p>The 5GC PFCP Intrusion Detection Dataset was implemented following relevant methodological frameworks, 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. A 5GC architecture was emulated, including the Network Slice Selection Function (NSSF), the Network Exposure Function (NEF), the Network Repository Function (NRF), the Policy Control Function (PCF), the User Data Management (UDM), the Access and Mobility Management Function (AF), the Authentication Server Function (AUSF), the Access Management Function (AMF), SMF, and UPF, in addition to a virtualised UE device, a virtualised gNodeB (gNB), and a cyberattacker impersonating a maliciously instantiated SMF. In particular, the following cyberattacks were performed:</p> <ul> <li>On Wednesday, October 05, 2022, the PFCP Session Establishment DoS Attack was implemented for 4 hours.</li> <li>On Thursday, October 13, 2022, the PFCP Session Deletion DoS Attack was implemented for four hours.</li> <li>On Tuesday, November 01, 2022, the PFCP Session Modification DoS Attack (DROP Apply Action Field Flags) was implemented for 4 hours.</li> <li>On Tuesday, November 22, 2022, the PFCP Session Modification DoS Attack (DUPL Apply Action Field Flag) was implemented for 4 hours.</li> </ul> <p>The previous PFCP-related cyberattacks were executed, utilising penetration testing tools, such as Scapy. For each attack, a relevant folder is provided, including the network traffic and the network flow statistics for each entity. In particular, for each cyberattack, a folder is given, providing (a) the pcap files for each entity, (b) the Transmission Control Protocol (TCP)/ Internet Protocol (IP) network flow statistics for 120 seconds in a Comma-Separated Values (CSV) format and (c) the PFCP flow statistics for each entity (using different timeout values in terms of second (such as 45, 60, 75, 90, 120 and 240 seconds)). The TCP/IP network flow statistics were produced by using the CICFlowMeter, while the PFCP flow statistics were generated based on a Custom PFCP Flow Generator, taking full advantage of Scapy.</p> <p><strong>The users of this dataset are kindly asked to cite the following paper(s).</strong></p> <p><em>G. Amponis, P. Radoglou-Grammatikis, T. Lagkas, W. Mallouli, A. Cavalli, D. Klonidis, E. Markakis, and P. Sarigiannidis, “Threatening the 5G core via PFCP DOS attacks: The case of blocking UAV Communications”, EURASIP Journal on Wireless Communications and Networking, vol. 2022, no. 1, 2022, doi: 10.1186/s13638-022-02204-5.</em></p>
IoMT-TrafficData: A Dataset for Benchmarking Intrusion Detection in IoMT
<h3><strong>Article Information<br></strong></h3> <p>The work involved in developing the dataset and benchmarking its use of machine learning is set out in the article ‘IoMT-TrafficData: Dataset and Tools for Benchmarking Intrusion Detection in Internet of Medical Things’. DOI: 10.1109/ACCESS.2024.3437214.</p> <p>Please do cite the aforementioned article when using this dataset. </p> <h3><strong>Abstract</strong></h3> <p>The increasing importance of securing the Internet of Medical Things (IoMT) due to its vulnerabilities to cyber-attacks highlights the need for an effective intrusion detection system (IDS). In this study, our main objective was to develop a Machine Learning Model for the IoMT to enhance the security of medical devices and protect patients’ private data. To address this issue, we built a scenario that utilised the Internet of Things (IoT) and IoMT devices to simulate real-world attacks. We collected and cleaned data, pre-processed it, and provided it into our machine-learning model to detect intrusions in the network. Our results revealed significant improvements in all performance metrics, indicating robustness and reproducibility in real-world scenarios. This research has implications in the context of IoMT and cybersecurity, as it helps mitigate vulnerabilities and lowers the number of breaches occurring with the rapid growth of IoMT devices. The use of machine learning algorithms for intrusion detection systems is essential, and our study provides valuable insights and a road map for future research and the deployment of such systems in live environments. By implementing our findings, we can contribute to a safer and more secure IoMT ecosystem, safeguarding patient privacy and ensuring the integrity of medical data.</p> <h3><strong>ZIP Folder Content</strong></h3> <p>The ZIP folder comprises two main components: <strong>Captures</strong> and <strong>Datasets</strong>. Within the captures folder, we have included all the captures used in this project. These captures are organized into separate folders corresponding to the type of network analysis: BLE or IP-Based. Similarly, the datasets folder follows a similar organizational approach. It contains datasets categorized by type: <strong>BLE</strong>, <strong>IP-Based Packet</strong>, and <strong>IP-Based Flows</strong>.</p> <p>To cater to diverse analytical needs, the datasets are provided in two formats: CSV (Comma-Separated Values) and pickle. The CSV format facilitates seamless integration with various data analysis tools, while the pickle format preserves the intricate structures and relationships within the dataset.</p> <p>This organization enables researchers to easily locate and utilize the specific captures and datasets they require, based on their preferred network analysis type or dataset type. The availability of different formats further enhances the flexibility and usability of the provided data.</p> <h3><strong>Datasets' Content</strong></h3> <p>Within this dataset, three sub-datasets are available, namely <strong>BLE, IP-Based Packet, and IP-Based Flows</strong>. Below is a table of the features selected for each dataset and consequently used in the evaluation model within the provided work.</p> <p>Identified Key Features Within Bluetooth Dataset</p> <table> <tbody> <tr> <td><strong>Feature</strong></td> <td><strong>Meaning</strong></td> </tr> <tr> <td>btle.advertising_header</td> <td>BLE Advertising Packet Header</td> </tr> <tr> <td>btle.advertising_header.ch_sel</td> <td>BLE Advertising Channel Selection Algorithm</td> </tr> <tr> <td>btle.advertising_header.length</td> <td>BLE Advertising Length</td> </tr> <tr> <td>btle.advertising_header.pdu_type</td> <td>BLE Advertising PDU Type</td> </tr> <tr> <td>btle.advertising_header.randomized_rx</td> <td>BLE Advertising Rx Address</td> </tr> <tr> <td>btle.advertising_header.randomized_tx</td> <td>BLE Advertising Tx Address</td> </tr> <tr> <td>btle.advertising_header.rfu.1</td> <td>Reserved For Future 1</td> </tr> <tr> <td>btle.advertising_header.rfu.2</td> <td>Reserved For Future 2</td> </tr> <tr> <td>btle.advertising_header.rfu.3</td> <td>Reserved For Future 3</td> </tr> <tr> <td>btle.advertising_header.rfu.4</td> <td>Reserved For Future 4</td> </tr> <tr> <td>btle.control.instant</td> <td>Instant Value Within a BLE Control Packet</td> </tr> <tr> <td>btle.crc.incorrect</td> <td>Incorrect CRC</td> </tr> <tr> <td>btle.extended_advertising</td> <td>Advertiser Data Information</td> </tr> <tr> <td>btle.extended_advertising.did</td> <td>Advertiser Data Identifier</td> </tr> <tr> <td>btle.extended_advertising.sid</td> <td>Advertiser Set Identifier</td> </tr> <tr> <td>btle.length</td> <td>BLE Length</td> </tr> <tr> <td>frame.cap_len</td> <td>Frame Length Stored Into the Capture File</td> </tr> <tr> <td>frame.interface_id</td> <td>Interface ID</td> </tr> <tr> <td>frame.len</td> <td>Frame Length Wire</td> </tr> <tr> <td>nordic_ble.board_id</td> <td>Board ID</td> </tr> <tr> <td>nordic_ble.channel</td> <td>Channel Index</td> </tr> <tr> <td>nordic_ble.crcok</td> <td>Indicates if CRC is Correct</td> </tr> <tr> <td>nordic_ble.flags</td> <td>Flags</td> </tr> <tr> <td>nordic_ble.packet_counter</td> <td>Packet Counter</td> </tr> <tr> <td>nordic_ble.packet_time</td> <td>Packet time (start to end)</td> </tr> <tr> <td>nordic_ble.phy</td> <td>PHY</td> </tr> <tr> <td>nordic_ble.protover</td> <td>Protocol Version</td> </tr> </tbody> </table> <p> </p> <p>Identified Key Features Within IP-Based Packets Dataset</p> <table> <tbody> <tr> <td><strong>Feature</strong></td> <td><strong>Meaning</strong></td> </tr> <tr> <td>http.content_length</td> <td>Length of content in an HTTP response</td> </tr> <tr> <td>http.request</td> <td>HTTP request being made</td> </tr> <tr> <td>http.response.code</td> <td>Sequential number of an HTTP response</td> </tr> <tr> <td>http.response_number</td> <td>Sequential number of an HTTP response</td> </tr> <tr> <td>http.time</td> <td>Time taken for an HTTP transaction</td> </tr> <tr> <td>tcp.analysis.initial_rtt</td> <td>Initial round-trip time for TCP connection</td> </tr> <tr> <td>tcp.connection.fin</td> <td>TCP connection termination with a FIN flag</td> </tr> <tr> <td>tcp.connection.syn</td> <td>TCP connection initiation with SYN flag</td> </tr> <tr> <td>tcp.connection.synack</td> <td>TCP connection establishment with SYN-ACK flags</td> </tr> <tr> <td>tcp.flags.cwr</td> <td>Congestion Window Reduced flag in TCP</td> </tr> <tr> <td>tcp.flags.ecn</td> <td>Explicit Congestion Notification flag in TCP</td> </tr> <tr> <td>tcp.flags.fin</td> <td>FIN flag in TCP</td> </tr> <tr> <td>tcp.flags.ns</td> <td>Nonce Sum flag in TCP</td> </tr> <tr> <td>tcp.flags.res</td> <td>Reserved flags in TCP</td> </tr> <tr> <td>tcp.flags.syn</td> <td>SYN flag in TCP</td> </tr> <tr> <td>tcp.flags.urg</td> <td>Urgent flag in TCP</td> </tr> <tr> <td>tcp.urgent_pointer</td> <td>Pointer to urgent data in TCP</td> </tr> <tr> <td>ip.frag_offset</td> <td>Fragment offset in IP packets</td> </tr> <tr> <td>eth.dst.ig</td> <td>Ethernet destination is in the internal network group</td> </tr> <tr> <td>eth.src.ig</td> <td>Ethernet source is in the internal network group</td> </tr> <tr> <td>eth.src.lg</td> <td>Ethernet source is in the local network group</td> </tr> <tr> <td>eth.src_not_group</td> <td>Ethernet source is not in any network group</td> </tr> <tr> <td>arp.isannouncement</td> <td>Indicates if an ARP message is an announcement</td> </tr> </tbody> </table> <p> </p> <p>Identified Key Features Within IP-Based Flows Dataset</p> <table> <tbody> <tr> <td><strong>Feature</strong></td> <td><strong>Meaning</strong></td> </tr> <tr> <td>proto</td> <td>Transport layer protocol of the connection</td> </tr> <tr> <td>service</td> <td>Identification of an application protocol</td> </tr> <tr> <td>orig_bytes</td> <td>Originator payload bytes</td> </tr> <tr> <td>resp_bytes</td> <td>Responder payload bytes</td> </tr> <tr> <td>history</td> <td>Connection state history</td> </tr> <tr> <td>orig_pkts</td> <td>Originator sent packets</td> </tr> <tr> <td>resp_pkts</td> <td>Responder sent packets</td> </tr> <tr> <td>flow_duration</td> <td>Length of the flow in seconds</td> </tr> <tr> <td>fwd_pkts_tot</td> <td>Forward packets total</td> </tr> <tr> <td>bwd_pkts_tot</td> <td>Backward packets total</td> </tr> <tr> <td>fwd_data_pkts_tot</td> <td>Forward data packets total</td> </tr> <tr> <td>bwd_data_pkts_tot</td> <td>Backward data packets total</td> </tr> <tr> <td>fwd_pkts_per_sec</td> <td>Forward packets per second</td> </tr> <tr> <td>bwd_pkts_per_sec</td> <td>Backward packets per second</td> </tr> <tr> <td>flow_pkts_per_sec</td> <td>Flow packets per second</td> </tr> <tr> <td>fwd_header_size</td> <td>Forward header bytes</td> </tr> <tr> <td>bwd_header_size</td> <td>Backward header bytes</td> </tr> <tr> <td>fwd_pkts_payload</td> <td>Forward payload bytes</td> </tr> <tr> <td>bwd_pkts_payload</td> <td>Backward payload bytes</td> </tr> <tr> <td>flow_pkts_payload</td> <td>Flow payload bytes</td> </tr> <tr> <td>fwd_iat</td> <td>Forward inter-arrival time</td> </tr> <tr> <td>bwd_iat</td> <td>Backward inter-arrival time</td> </tr> <tr> <td>flow_iat</td> <td>Flow inter-arrival time</td> </tr> <tr> <td>active</td> <td>Flow active duration</td> </tr> </tbody> </table>
Discrete Element Method model results of fracturing and displacements above laccolith intrusions
<p>This data set contains scripts and data in support of the publication: Morand A., Poppe S., Harnett C., Cornillon A., Heap M., Mège D. (2023). Fracturing and dome-shaped surface displacements above laccolith intrusions: Insights from Discrete Element Method modeling. <em>submitted</em>, Journal of Geophysical Research: Solid Earth, July 2023.</p> <p>The data set is composed of results of two-dimensional (2D) Discrete Element Method (DEM) modeling performed by Morand et al., with the licensed commercial <em>Particle Flow Code </em>2D version 7.0 (PFC2D 7.0) from Itasca Consulting Group, Ltd. The DEM modeling simulates a new injection of magma into a preexisting laccolith intrusion and tracks the effect of host rock toughness, stiffness and the source depth. The data set corresponds to exported DEM particle and crack properties of the model results into ASCII files (.txt) and processed particle maximum finite shear strain calculated from particle displacements.</p>
Investigating the velocity of magmatic intrusions and its relation with rock fracture toughness: insights from laboratory experiments and numerical models
<p>This repository provides compressed folders containing the velocity profiles recorded during our oil-filled crack propagation experiments and the code used to simulate those experiments. In particular, the files in compressed folders <strong>10ml</strong>, <strong>30ml</strong>, <strong>50ml</strong> and <strong>others_ml</strong> contain two columns corresponding to the tracked cracks' depth [m] and velocity [m/s]. The two folders <strong>DYKE-CODE_constant-Ef</strong> and <strong>DYKE-CODE_variable-Ef</strong> contain the Fortran90 code, the input and output files, and all the scripts needed to reproduce the simulations and the plots displayed in Figure 3 and Figure 4 of the article <em>"</em>Investigating the velocity of magmatic intrusions and its relation with rock fracture toughness: insights from laboratory experiments and numerical models<em>"</em><strong><em> </em></strong> by A. Gaete, F. Maccaferri, S. Furst, and V. Pinel.</p>
The Role of Expectations in the Development of Intrusive Memories
ClinicalTrials.gov study NCT03950869. IPD Sharing: NO. Countries: 1. Publications: 3.
Tackling Intrusive Traumatic Memories After Childbirth
ClinicalTrials.gov study NCT05381155. IPD Sharing: YES. Countries: 1. Publications: 3.
Hydrocortisone in the Treatment of Intrusions in Patients With Posttraumatic Stress Disorder
ClinicalTrials.gov study NCT01108146. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Methods for Managing Intrusive Thoughts
ClinicalTrials.gov study NCT03416504. IPD Sharing: NO. Countries: 1. Publications: 1.
Improving Attentional and Cognitive Control in the Psychological Treatment of Intrusive Thoughts
ClinicalTrials.gov study NCT04225624. IPD Sharing: NO. Countries: 1. Publications: 0.
The Role of Cognitive Control in the Transdiagnostic Conceptualization of "Intrusive Thoughts"
ClinicalTrials.gov study NCT03414619. IPD Sharing: NO. Countries: 1. Publications: 1.
Consequence of Injectable Platelets Rich Fibrin Versus Microosteopeforation on Root Resorption During Orthodontic Intrusion of Incisor Teeth
ClinicalTrials.gov study NCT05958498. IPD Sharing: NO. Countries: 1. Publications: 2.
Molar Intrusion Using High Pull Headgear
ClinicalTrials.gov study NCT02951286. IPD Sharing: NO. Countries: 1. Publications: 3.
Maxillary Versus Bi-Maxillary Posterior Segments Intrusion Adult Subjects With Skeletal Open Bite
ClinicalTrials.gov study NCT04713280. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Computer Assisted Lessening of Intrusive Memories in the Emergency Department
ClinicalTrials.gov study NCT04769999. IPD Sharing: NO. Countries: 1. Publications: 0.
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.