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6 results for “Distributed attacks”
Dataset for KIOS CoE Sandboxing use-case SUC5 corresponding to cyber attacks affecting the control of active distribution grids and microgrids
<p>These datasets <span> illustrate two primary scenarios (S1-S2) concerning the operation of the sandboxing use case SUC5 corresponding to cyber attacks affecting the control of active distribution grids and microgrids. These scenarios examine the functioning of an active distribution grid and microgrid system, along with the effects of certain cyber-attacks in this context. The demonstration of each scenario is detailed in selected time-series plots which were described in detail in Section </span><span>1.3 of the supporting document of SUC5 (</span><span>accompanied by an in-depth analysis of the processes and an impact assessment). A</span><span>ll data captured during the execution of each scenario was collected, including electrical measurements, reference and set-point signals. </span></p> <ul> <li><span><span><strong>SUC5/S1 datasets/<span>MITM with FDI</span> cyber-attack</strong><span><strong> in an active distribution grid (grid-connected)</strong>: This dataset is related to the operation of the fifth sandboxing use case (SUC5) of the KIOS CoE Sandboxing environment for cyber-physical analysis of EPES, which examines the operation of an active distribution grid, when the distribution grid is interconnected with the main grid. Specifically, this dataset corresponds to the first scenario (S1) of SUC5, where a MITM with FDI cyber-attack is virtually conducted within the sandboxing environment to introduce an offset deviation to the active power set-point allocated to BSS inverter controller from the secondary controller. More details about the scenario related to this dataset can be found in Section 1.3 of the supporting document. The dataset includes electrical measurements of the active power generated by the BSS inverter (connected at bus 2), and the active power set-point before and after the attack. The dataset is provided in the form of time-series measurements available as MATLAB (.mat) and CSV (.csv) files. The measurements were recorded from the real time simulator using the “OpWrite” block of the RT-LAB, with 1-millisecond time resolution. <br></span></span></span></li> <li><span><span><span><strong>SUC5/S2 datasets/MITM with FDI cyber-attack in a microgrid (islanding mode)</strong>: This dataset is related to the operation of the fifth sandboxing use case (SUC5) which investigates the operation of a microgrid during islanding mode. This dataset corresponds to the second scenario (S2) of SUC5, where a MITM with FDI cyber-attack is virtually conducted within the sandboxing environment to introduce an offset deviation to the frequency reference signal, exchanged between the higher-level controller (tertiary controller) and the microgrid local controller (secondary V-f controller). More details about the scenario related to this dataset can be found in Section 1.3 of this supporting document. The dataset includes electrical measurements of the microgrid frequency, the reference frequency value generated by the tertiary controller, as well as the attacked frequency reference value. The dataset is provided in the form of time-series measurements available as MATLAB (.mat) and CSV (.csv) files. The measurements were recorded from the real time simulator using the “OpWrite” block of the RT-LAB, with 1-millisecond time resolution. <br></span></span></span></li> </ul>
Datasets for Deep Learning Based Radio Frequency Side-Channel Attack on Quantum Key Distribution
<p>The dataset contains measurements of radio-frequency electromagnetic emissions from a home-built sender module for BB84 quantum key distribution. The goal of these measurements was to evaluate information leakage through this side-channel. This dataset supplements our <a href="https://link.aps.org/doi/10.1103/PhysRevApplied.20.054040">publication</a> and allows to reproduce our results together with the source code hosted at <a href="https://github.com/XQP-Munich/EmissionSecurityQKD">GitHub</a> (and also on <a href="https://doi.org/10.5281/zenodo.7965628">Zenodo</a> via integration with GitHub).<br><br>The measurements are performed using a magnetic near-field probe, an amplifier and an oscilloscope. The dataset contains raw measured data in the file format output by the oscilloscope. Use our source code to make use of it. Detailed descriptions of measurement procedure can be found in our paper and in the metadata JSON files found within the dataset.</p> <p><strong>Commented list of datasets</strong></p> <p>This file lists the datasets that were analyzed and reported on in the paper. The datasets in the list refer to directories here. Note that most of the datasets contain additional files with metadata, which detail where and how the measurements were performed. The mentioned Jupyter notebooks refer to the source code repository https://github.com/XQP-Munich/EmissionSecurityQKD (not included in this dataset). Most of those notebooks output JSON files storing results. The processed JSON files are also included in the source code repository.</p> <p>In naming of datasets,</p> <ul> <li><em>Antenna</em> refers to the log-periodic dipole antenna. All datasets that do not contain `Antenna` in their name are recorded with the magnetic near-field probe.</li> <li><em>Rev1</em> refers to the initial electronics design, while `rev2` refers to the revised electronics design which contains countermeasures aiming to reduce emissions.</li> <li><em>Shielding</em> refers to measurements where the device is enclosed in a metallic shielding and the measurement takes place outside the shielding.</li> <li><em>Rotation</em> refers to orientation of the magnetic near-field probe at the same spacial location</li> </ul> <p><strong>Datasets collected with near-field probe for Rev1 electronics</strong></p> <ul> <li><strong>Rev1Distance</strong>: contains measurements at different distances from the Rev1 electronics performed above the FPGA. The deep learning attack is analyzed in `TEMPEST_ATTACK.ipynb`. The amplitude is analyzed in `get_raw_data_RMS_amplitude.ipynb`.</li> <li><strong>Rev12D</strong>: different locations on a 2d grid at a constant distance from the electronics. The deep learning attack is analyzed in `TEMPEST_ATTACK.ipynb`.</li> <li><strong>Rev130meas2.5cm</strong>: 30 measurements above the FPGA at a hight of 2.5cm. Used to evaluate how much amount of training data affects neural network performance. The deep learning attack is analyzed in notebooks `TEMPEST_ATTACK*.ipynb`. In particular, `TEMPEST_ATTACK_VARY_TRAINING_DATA.ipynb` is used on this dataset.</li> <li><strong>Rev1Rotation10deg</strong> contains a measurement for varying orientation of the probe at the same location. This is not mentioned in the paper and is only included for completeness. The deep learning attack is analyzed in notebooks `TEMPEST_ATTACK*.ipynb`.</li> <li><strong>Rev1TEMPESTShieldingFPGA</strong> Measurements with and without shielding at 4cm above the FPGA.</li> <li>- <strong>Rev1TEMPESTShieldingUSBHole</strong> Measurements with shielding in front of a hole of size about 2cm x 2cm. The deep learning attack is analyzed in `TEMPEST_ATTACK*.ipynb`.</li> </ul> <p><strong>Datasets collected with near-field probe for Rev2 electronics</strong></p> <ul> <li><strong>Rev2Distance</strong> contains measurements at different distances from the Rev2 electronics performed above the FPGA.</li> <li><strong>Rev22D</strong> and <strong>Rev22Dstart_7_0</strong> contain measurements on a 2d grid performed on the revised electronics. The dataset is split in two directories because the measurement procedure crashed in the middle. This split structure was kept in order to maintain consistency with the automatic metadata.</li> <li><strong>Rev230meas2.5cm</strong> 30 measurements above the FPGA at a hight of 2.5cm. Used to evaluate how much amount of training data affects neural network performance. The deep learning attack is analyzed in notebooks `TEMPEST_ATTACK*.ipynb`. In particular, `TEMPEST_ATTACK_VARY_TRAINING_DATA.ipynb` is used on this dataset.</li> </ul> <p><strong>Other datasets</strong></p> <ul> <li><strong>BackgroundTuesday</strong> background measurement (QKD device is not powered at all) performed with near-field probe on 2022 June 21st.</li> <li><strong>BackgroundSaturday</strong> background measurement (QKD device is not powered at all) performed with near-field probe on 2022 June 11th.</li> <li><strong>AntennaSpectra</strong> Dataset of spectra directly recorded by the oscilloscope. Used to demonstrate ability of telling apart the situation of sending QKD key (standard operation) and having the device turned on but not sending any key at a distance. Analyzed in notebook `Comparing_KeyNokey_Measurements.ipynb`.</li> <li><strong>Rev2ShieldingAntenna</strong> Raw amplitude measurements with log-periodic dipole antenna on Rev2 electronics including shielding enclosure, collected at various distances. None of our attacks against this scenario were successful. The dataset represents a challenge to test more advanced attacks using improved data processing.</li> </ul> <p> </p>
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>
Dataset of "Exploring the Landscape and Severity of Distributed Denial of Service Attacks"
<p>The Internet of Things and Operational Technology technologies allow the Internet to be connected to a wide range of devices. The use of the Internet not only enables the management of these connected devices and the transmission of information through various devices but also exposes them to security threats. These threats include, but are not limited to, Denial of Service (DoS) attacks. Some attacks target specific types of devices, services, or web server versions. In this paper, we have enumerated and compared the most well-known (D)DoS attacks. Our paper provides a wide area of (D)DoS attacks sorted by ISO/OSI layers. Each listed attack is described, detection and mitigation techniques are presented, and a simulation tool capable of generating attacks is also presented. In total, we have focused on 46 attacks. In addition, a simulation of selected DoS, DDoS, and slow DoS attacks is carried out in an experimental setup workstation, and the impacts are compared.</p>
Data from: Colonization of weakened trees by mass-attacking bark beetles: no penalty for pioneers, scattered initial distributions and final regular patterns
Bark beetles use aggregation pheromones to promote group foraging, thus increasing the chances of an individual to find a host and, when relevant, to overwhelm the defences of healthy trees. When a male beetle finds a suitable host, it releases pheromones that attract potential mates as well as other "spying" males, which results in aggregations on the new host. To date, most studies have been concerned with the use of aggregation pheromones by bark beetles to overcome the defences of living, well-protected trees. How insects behave when facing undefended or poorly defended hosts remains largely unknown. The spatio-temporal pattern of resource colonization by the European eight-toothed spruce bark beetle, Ips typographus, was quantified when weakly defended hosts (fallen trees) were attacked. In many of the replicates, colonization began with the insects rapidly scattering over the available surface and then randomly filling the gaps until a regular distribution was established, which resulted in a constant decrease in nearest-neighbour distances to a minimum below which attacks were not initiated. The scattered distribution of the first attacks suggested that the trees were only weakly defended. A minimal theoretical distance of 2.5 cm to the earlier settlers (corresponding to a density of 3.13 attacks/dm²) was calculated, but the attack density always remained lower, between 0.4 and 1.2 holes/dm², according to our observations.
Data from: Colonization of weakened trees by mass-attacking bark beetles: no penalty for pioneers, scattered initial distributions and final regular patterns
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