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12 results for “Network attacks”
Defeating Adversarial Attacks Againt Adversarial attacks in Network Security
<p>We investigate if the feature randomization approach to improve the robustness of forensic detectors to targeted attacks in network security, can be extended to detectors based on deep learning features. In particular, we study the transferability of adversarial examples targeting an original CNN image manipulation detector to other detectors that rely on a random subset of the features extracted from the flatten layer of the original network. The results we got by considering, two original network architectures and different classes of attacks, show that feature randomization helps to hinder attack transferability, even if, in some cases, simply changing the architecture of the detector, or even retraining the detector is enough to prevent the transferability of the attacks.</p>
HIKARI-2021: Generating Network Intrusion Detection Dataset Based on Real and Encrypted Synthetic Attack Traffic
<p>Available datasets from the paper Generating Encrypted Network Traffic for Intrusion Detection Datasets.</p> <p>To produce the dataset follow the technical detail in <a href="https://github.com/andreysfc/generating-encrypted-network">github</a></p>
Dataset for Advanced Persistent Threat (APT) Attacks on Power Substation Networks via GOOSE Protocol Exploitation
<p>This dataset captures network traffic from a simulated Advanced Persistent Threat (APT) campaign targeting a power substation's communication network. The attacker maintains a prolonged presence within the network, conducting low-profile scans using Nmap to stealthily discover the network configuration. The focus is on the communication between the Remote Terminal Unit (RTU), the Programmable Logic Controller (PLC), and the Bay Protection Unit, all of which utilize the Generic Object Oriented Substation Event (GOOSE) protocol for critical operations.</p>
LoRaWAN Network Attacks
<p>This dataset includes a dump of messages seen on a LoRaWAN network collected during validation of the GUARD project. It includes plain traffic from real devices installed on city bus and some attacks artificially generated by replicating or altering messages.</p>
Application of hyperbolic geometry of multiplex networks under layer link-based attacks
<p>As real multilayer networks, we consider four networks. The multilayer networks are converted to multiplex networks by assuming that all layers have the same number of nodes (the maximum number of nodes of all layers). Explanation of these networks is as follow:</p> <ol> <li><em>CS-Aarhus_multiplex [1]</em> : The first network used in this study is a 5-layer multiplex network, named CS-Aarhus_multiplex, which has 61 nodes and 620 edges. The multiplex social network consists of five kinds of online and offline relationships (Facebook, Leisure, Work, Co-authorship, Lunch) between the employees of the Computer Science department at Aarhus.</li> <li><em>Data_malaria_PLOSCompBiology_2013 [2]</em>: The second network is a 9-layer multiplex network, which consists of 307 nodes and 35306 edges. Networks of recombinant antigen genes from the human malaria parasite P. falciparum. Each of the 9 networks shares the same set of vertices but has different edges, corresponding to the 9 highly variable regions (HVRs) in the DBLa domain of the var protein. Nodes are var genes, and two genes are connected if they share a substring whose length is statistically significant.</li> <li>VICKERS CHAN 7th-GRADERS [3] : The third network is a 3-layer multiplex network, called VICKERS CHAN 7th-GRADERS, which includes 29 nodes and 740 edges. The data were collected by Vickers from 29 seventh-grade students in a school in Victoria, Australia. Students were asked to nominate their classmates on several relations including the three layers.</li> </ol> <p> 4. FAO MULTIPLEX TRADE NETWORK [4]: The fourth network is a 364-layer multiplex network, which contains 214 nodes and 318346 edges. We consider different types of trade relationships among countries, obtained from FAO (Food and Agriculture Organization of the United Nations)</p>
Cyber4OT: ICS network traces containing normal activity and full attack traffic
<p>The <em><strong>Cyber4OT</strong></em> dataset contains prepared in the test-bed environment packet traces from normal activity of OT network, as well as, full network attack. During recorded activity, the attacker performs full network reconnaissance, later disconnects legal Modbus TCP connection and performs PLC device hijacking.</p> <p>The dataset contains 96 files with more than 4,25 millions of packets.</p> <p><em><strong>ReadMe.txt</strong></em> file contains short description of each trace file content.</p> <p>Detailed description of the test bed, where data was prepared, is provided in the <em><strong>Cyber4OT_testbed_description.pdf</strong></em> file.</p>
FAN-GHETS24: A Flying Ad Hoc Network Dataset for Early Time Series Classification of Grey Hole Attacks
<p>Flying ad-hoc networks (FANETs) consist of multiple unmanned aerial vehicles (UAVs) that rely on multi-hop routes for communication. These routes are particularly susceptible to grey hole attacks, necessitating swift and accurate defense to preserve the network's quality of service. This novel dataset, FAN-GHETS24, is designed for early time series classification of various grey hole attack scenarios. The dataset is derived from sequences of packet interactions between UAVs within the network, generated through multiple simulations. These sequences undergo post-processing via two methods: firstly, an anonymization procedure that replaces IP addresses with standard string variables, allowing for offline model training and universal deployment across UAVs; and secondly, the application of feature engineering techniques to format the data for machine learning model integration.</p> <div> <div>The dataset is split across several zip files, combine and extract them by issuing these command:</div> </div> <div> <div>$ zip -FF fan-ghets24.zip --out fan-ghets24-combined.zip</div> <div>$ unzip fan-ghets24-combined.zip</div> </div>
Terrorist Attack Network Datasets
<p>## TNA_NETS</p> <p>Terrorist Attack Network Datasets</p> <p>This collection consists of annotated networks developed from documents related to various terrorist attacks in India. Each dataset is named after a specific case and visualizes relationships and interactions among individual entities involved in these incidents. These networks are instrumental for analyzing key actors, hierarchies, and patterns within terrorist organizations. Below is an overview of each file:</p> <ul> <li><strong>TerroristData1</strong> : Captures the network associated with the 2001 Parliament attack in India. This dataset outlines connections between key individuals, detailing both direct and inferred associations critical for understanding the network structure behind this event.</li> <li><strong>TerroristData2</strong>: Represents the network surrounding the 1996 Dausa blast, in which a bomb exploded in a Rajasthan State Transport Corporation (RSTC) bus traveling from Agra to Bikaner. The explosion occurred at Samleti village in Dausa district, Rajasthan, killing 14 people and injuring 37. The incident took place a day after a similar blast in Delhi’s Lajpat Nagar. This dataset captures the interactions among involved individuals and entities, shedding light on the logistical and operational aspects of this tragic event.</li> <li><strong>TerroristData3</strong> : Documents the network based on the 2008 Mumbai 26/11 attacks. It highlights the coordination among individuals and entities involved in the planning and execution, giving insights into the operational dynamics of the group.</li> <li><strong>TerroristData4</strong>: Visualizes the network behind the assassination of former Prime Minister of India, Mr. Rajiv Gandhi. The dataset details the connections and hierarchy within the network involved in the plot, showcasing the organizational structure and chain of command.</li> <li><strong>TerroristData5</strong>: Maps the network involved in the 1993 Bombay blasts, displaying connections between perpetrators, facilitators, and key locations involved in the coordinated attacks across Mumbai.</li> </ul> <p>These datasets are a resource for studying the structure and influence of clandestine networks in terrorist operations. They can support research on network centrality, influence analysis, and resilience, offering insights into the organizational dynamics of terror groups</p>
Mitigating opinion polarization in social networks using adversarial attacks
Open the record for dataset details and reuse information.
Defensive symbiont genotype distributions are linked to parasitoid attack networks - Dataset and scripts
<p>This R project includes data and scripts for paper - Defensive symbiont genotype distributions are linked to parasitoid attack networks</p> <p>Script for analysis:</p> <p> Run all the scripts in order, to get all analyses and results in this paper. </p> <p> Script_0_package_install_load.R: <br> Installs and loads the necessary R packages required for all subsequent scripts.<br> This R project</p> <p> Script_1_Extract_from_Table_S2.R<br> Extracts information from:<br> Table_S2.csv (located in /Rawdata/) Data_4_Aphid_sequences.fas<br> Generates 10 files for further analyses:<br> Aphid relatedness distance:<br> Aphid_phylogenetic_relatedness_16species.csv<br> Aphid_phylogenetic_relatedness_22species.csv<br> Aphid_phylogenetic_relatedness_31species.csv<br> Hamiltonella - Aphid Matrices:<br> Hamiltonella_Aphid_matrix_16species.csv<br> Hamiltonella_Aphid_matrix_22species.csv<br> Hamiltonella_Aphid_matrix_31species.csv<br> Parasitoid - Aphid Matrices:<br> Para_Aphid_matrix_16species.csv<br> Para_Aphid_matrix_22species.csv<br> Plant - Aphid Matrices:<br> Plant_Aphid_matrix_16species.csv<br> Plant_Aphid_matrix_31species.csv</p> <p><br> Script_2_BarPlot_Fig1.R: <br> Generates Fig. 1 using: <br> Data_11_Parasitoid_genus_aphid_22_species_Fig.1.csv Data_12_Plant_genus_aphid_31_species_Fig.1.csv<br> The bar order in Fig. 1 is arranged by proportion, which is not directly supported in the ggplot2 package in R. Therefore, we need manually reordered if using the first plot code; or, manually colour it using the second plot code. </p> <p><br> Script_3_MMRR_analysis.R<br> Uses 10 distance/distribution matrices of Parasitoid, Plant, Hamiltonella, and Aphid relationships to compute matrix correlations with the MMRR (Multiple Matrix Regression with Randomization) model.</p> <p><br> Script_4_Species_linkage_Fig2_FigS4.R<br> Uses 7 distribution matrices of Parasitoid, Plant, and Hamiltonella relationships with Aphids (excluding Aphid genetic distance matrices) to generate the species linkage diagrams for Fig. 2 and Fig. S4, For aesthetic purposes, unconnected sample points have been moved.</p> <p><br> Script_5_MMRR_Fig3_FigS5.R<br> Plots the MMRR correlations using the 10 distance/distribution matrices for Parasitoid, Plant, Hamiltonella, and Aphid relationships (Fig. 3 & Fig. S5). </p> <p> Script_6_parasitoid_specialization_Fig4.R<br> Computes specialization levels using the H2 Index for:<br> Parasitoid-Aphid<br> Aphid-Hamiltonella<br> Parasitoid-Hamiltonella relationships <br> Generates Figure 4.</p> <p> Script_7_Ecologicial_indices&plots_Table1_FigS3.R<br> Uses 7 distribution matrices of Parasitoid, Plant, and Hamiltonella relationships with Aphids (excluding Aphid genetic distance matrices) to:<br> Calculate ecological indices (Richness, Shannon Index, Simpson Index).<br> Use linear models to analyze the relationships between Parasitoid/Plant-Aphid and Aphid-Hamiltonella communities. <br> Outputs results for Table 1 and Figure S3.</p> <p><br> Script_8_Bubble_plot_FigS2.R<br> This script using <br> Hamiltonella_Aphid_matrix_31species.csv <br> and two phylogeny trees <br> Data_8_Aphid_species_phylogeny.txt & <br> Data_9_Hamiltonella_phylogeny.txt<br> To create a Cophylogeny tree of Hamiltonella strains and aphid species that we identified in this study and previously known strains. (Fig. S2)</p> <p> Script_9_DADA2_pipeline.R<br> This script is the DADA2 pipeline used to processing the raw COI sequencing data into ASV (Amplicon Sequence Variant) count files. After processing, the ASV files required manual curation to link each ASV with a distinct parasitoid and aphid species name based on BLAST results. In our updated version, we have provided both the raw data (BioProject PRJNA1139364) as well as the manually curated files (Data_1 and Data_2) which includes the ASV species names. All subsequent analyses can be reproduced using the curated datasets (Data_1 and Data_2), ensuring clarity and replicability for users.<br> We have incorporated the SRA Toolkit to streamline the process. This allows users to download raw SRR sequencing files directly and convert them into FASTQ files, which can then be input into the DADA2 pipeline. The pipeline produces two ASV count files, one for each sequencing run.</p> <p> </p> <p>Raw data and inofrmation:</p> <p> Table_S2.csv<br> The Dataset from Supplementary Table S2: After manual pooling the Parasitoid/Aphid species based on the Data_1,2 and 6.<br> No: the number of this sample<br> City: The city that collected this sample<br> Location: the location of sample collection<br> Sample category: Aphid mummies or alive aphids<br> Sample code: the name of sample (self defined)<br> Collection year: when this sample was collected<br> Aphid taxa / Wasp taxa / Plant species /Hamiltonella strain: The Aphid/Parasitoid/Plant/Hamiltonella species <br> Sequencing method: Illumina or Sanger sequencing <br> Sequencing date: the date of sending this sample to sequencing<br> Data source: From this study or from Wu et al. 2022: Local adaptation to hosts and parasitoids shape Hamiltonella defensa genotypes across aphid species</p> <p> Data_1_Deep_sequencing_data_block_1.xlsx & Data_2_Deep_sequencing_data_block_2.xlsx <br> Sample: The sample name from Illumina sequencing, which can later be found on GenBank (BioProject PRJNA1139364).<br> Aphid: The detected aphid species for each sample.<br> Parasitoid: The detected parasitoid species.<br> The remaining columns contain ASV (Amplicon Sequence Variant) data derived from high-throughput barcoding sequencing.<br> Data1 and Data_2 are the manually curated versions of the BioProject PRJNA1139364 with added species names for each ASV. </p> <p> Data_3_Parasitoid sequences.fas & <br> Data_4_Aphid sequences.fas & <br> Data_5_Hamiltonella sequences.fas,<br> FASTA files for:<br> Parasitoid species<br> Aphid species<br> Hamiltonella strains<br> These sequences were used for phylogenetic reconstruction and subsequent analyses.</p> <p> Data_6_Parasitoid_Aphid_pooled_table.xlsx <br> Contains the OTU (Operational Taxonomic Unit) manual pooling results for Parasitoid and Aphid species based on 99% (4 base pair) sequence similarity:<br> Parasitoid pooling together group & Aphid pooling together group: Original names from Illumina sequencing.<br> Original name: Representative sequences for each group.<br> Pooled species name: Final species names used in all analyses.<br> Pooled sequences: Final representative sequences used in all analyses.<br> The result of manual pooling see the Table_S2.csv</p> <p> Data_7_Parasitoid_species_phylogeny.txt & Data_8_Aphid_species_phylogeny.txt & Data_9_Hamiltonella_phylogeny.txt <br> Phylogenetic trees for:<br> Parasitoid species<br> Aphid species<br> Hamiltonella strains<br> These trees were generated using the PhyML tool on the ATGC Montpellier platform, original fasta file was Data_3, 4 & 5.</p> <p> Data_10_aphid_host_info.csv <br> Provides aphid host information for generating Figure 2 and Figure S4:<br> Aphid: Names of aphid species included in this study.<br> Host: Host categories:<br> 1: Herb aphids<br> 2: Grass aphids<br> 3: Tree aphids<br> Host_category: Detailed descriptions of host categories.</p> <p> Data_11_Parasitoid_genus_aphid_22_species_Fig.1.csv & Data_12_Plant_genus_aphid_31_species_Fig.1.csv <br> Reduced matrices used for Figure 1, created by merging data from:<br> OTUs associated with the same parasitoid species.<br> Plant species belonging to the same genus.</p> <p> 7 Distribution Matrices (from Script_1_Extract_from_Table_S2.R)<br> Hamiltonella-Aphid Matrices:<br> Hamiltonella_Aphid_matrix_16species.csv<br> Hamiltonella_Aphid_matrix_22species.csv<br> Hamiltonella_Aphid_matrix_31species.csv<br> Parasitoid-Aphid Matrices:<br> Para_Aphid_matrix_16species.csv<br> Para_Aphid_matrix_22species.csv<br> Plant-Aphid Matrices:<br> Plant_Aphid_matrix_16species.csv<br> Plant_Aphid_matrix_31species.csv<br> Structure:<br> Rows represent aphid species.<br> Columns represent Hamiltonella strains, parasitoid species, or host plant species linked to each aphid species.<br> Usage: These matrices were used to generate Figures 2, 3, 4, Figures S2, S3, S4, S5, and for MMRR tests, ecological indices, and H2 index calculations.</p> <p> 3 Aphid phylogenetic relatedness matrices derived from Script_1_Extract_from_Table_S2.R<br> Aphid_phylogenetic_relatedness_16species.csv<br> Aphid_phylogenetic_relatedness_22species.csv<br> Aphid_phylogenetic_relatedness_31species.csv<br> These matrices provide phylogenetic distance information, showing pairwise genetic distances between the 31 aphid species included in this study.</p> <p> SraRunTable.csv<br> The BioProject (PRJNA1139364) information downloaded directly from Genbank. </p>
UDP Flood Attack Pattern on Internet of Things Network Dataset
<p><strong>Investigating UDP Flood Attack Pattern on Internet of Things Network</strong></p> <p><em>status: on review</em></p> <p>Abstract: UDP does not have mechanism for retransmission when a transmitting error happens, it makes this protocol to be used as a DDoS attack tool against Internet of Things (IoTs) networks. This research work attempts to analyze the UDP Flood attacks packets dataset captured from an Io|T testbed network by Wireshark.</p> <p>A feature extraction process on generated CSV file was performed and then the feature extraction result are examined to find patterns of UDP flood attack packet. Lastly, the patterns are visualized to provide easy pattern recognition.</p>
Ping Flood Attack Pattern Recognition on Internet of Things Network Dataset
<p><strong>Ping Flood Attack Pattern Recognition using K-Means Algorithm in Internet of Things (IoT) Network</strong> <br> <em>status: on repository</em></p> <p>Abstract — This work investigates ping flood attack pattern recognition on Internet of Things (IoT) network. Experiments are conducted on WiFi communication with three different scenarios: normal traffic, attack traffic, and normal-attack combination traffic to create normal dataset, attack dataset, and normal attack (combined) dataset. The datasets are grouped into two clusters i.e.: (i) normal cluster and (ii) attack cluster. Clustering results using implemented K-Means algorithm show the average number of packets on the cluster of attack in total is 95,931 packets, and the average packets on normal cluster in total is 4,068 packets.</p> <p>Accuracy level of the clustering results then is calculated using confusion matrix equation. Based on the confusion matrix calculation, accuracy of clustering using implemented K-Means algorithm was 99.94%. The true negative rate reaches up to 98.62%, true positive rate is 100%, the false negative rate is 0%, and the false positive rate reaches 1.38%.</p>
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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)
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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.