Data Collection & Requirements

Open-Source Cybersecurity and AI Security Datasets This project provides a comprehensive collection of open-source datasets focused on cybersecurity threats and AI security vulnerabilities. The datasets are carefully selected to align with specific security threats, such as: Data Exfiltration Data Poisoning Model Manipulation Adversarial Examples Model Inversion Model Extraction Spoofing Attacks Unauthorized Access Supply Chain Compromise Dataset Collection Each dataset includes a detailed description, source type, purpose, and direct access links for easy retrieval. Cybersecurity & AI Security Datasets Comprehensive, Multi-Source Cyber-Security EventsAccess Here: https://csr.lanl.gov/data/Description: 58 days of de-identified LANL network data (authentication, process events, DNS, network flow, red team).Format: Text filesUpdate Frequency: StaticUse Cases: Cybersecurity Event Analysis DARPA Intrusion Detection Data SetsAccess Here: https://archive.ll.mit.edu/ideval/data/Description: Simulated network traffic with intrusion scenarios.Format: PCAP filesUpdate Frequency: StaticUse Cases: IDS Training MITRE ATT&CK Framework DataAccess Here: https://attack.mitre.org/Description: Adversary TTPs (tactics, techniques, procedures) in a globally accessible knowledge base.Format: JSON/STIXUpdate Frequency: QuarterlyUse Cases: Threat Intelligence National Vulnerability Database (NVD)Access Here: https://nvd.nist.gov/Description: CVEs with severity scores and descriptions.Format: XML/JSONUpdate Frequency: DailyUse Cases: Vulnerability Management LANL Unified Host and Network DatasetAccess Here: https://csr.lanl.gov/data/Description: Enterprise-scale dataset with network and host logs, including real-world red-team attacks.Format: Text filesUpdate Frequency: StaticUse Cases: Insider Threat Detection CIC-IDS2017 (Intrusion Detection Dataset)Access Here: https://www.unb.ca/cic/datasets/ids-2017.htmlDescription: Network traffic dataset with multiple attack types (DDoS, brute-force, infiltration).Format: PCAP, CSVUpdate Frequency: StaticUse Cases: Intrusion Detection CIC IoV CAN Bus Dataset 2024Access Here: https://www.unb.ca/cic/datasets/Description: Vehicle CAN bus data, including spoofing and DoS attack traces.Format: CSV, PCAPUpdate Frequency: StaticUse Cases: Automotive Security ASVspoof 2019 (Voice Spoofing Dataset)Access Here: https://datashare.ed.ac.uk/handle/10283/3336Description: Evaluates automatic speaker verification systems under spoofing attacks.Format: WAV filesUpdate Frequency: StaticUse Cases: Voice Security ToN_IoT DatasetsAccess Here: https://research.unsw.edu.au/projects/toniot-datasetsDescription: Federated IoT data sources, including telemetry, OS logs, and network traffic.Format: CSV, JSONUpdate Frequency: OngoingUse Cases: Threat Intelligence ADFA Intrusion Detection DatasetsAccess Here: https://research.unsw.edu.au/projects/adfa-ids-datasetsDescription: Host-based intrusion detection datasets for Windows and Linux.Format: CSV, JSONUpdate Frequency: StaticUse Cases: Host Intrusion Detection Security Datasets ProjectAccess Here: https://github.com/OTRF/Security-DatasetsDescription: A community-driven initiative sharing security datasets for research.Format: JSON, CSVUpdate Frequency: OngoingUse Cases: Threat Intelligence CIC-BCCC-NRC Tabular IoT Attack Dataset (2024)Access Here: https://www.yorku.ca/research/bccc/ucs-technical/cybersecurity-datasets-cds/Description: A comprehensive IoT network attack dataset for AI-based cybersecurity research.Format: CSVUpdate Frequency: OngoingUse Cases: IoT Security Awesome Cybersecurity DatasetsAccess Here: https://github.com/shramos/Awesome-Cybersecurity-DatasetsDescription: A curated list of publicly available datasets for cybersecurity research.Format: VariesUpdate Frequency: MultipleUse Cases: General Cybersecurity Research CSE-CIC-IDS2018Access Here: https://www.unb.ca/cic/datasets/ids-2018.htmlDescription: Enterprise-scale dataset with 7 modern attack scenarios (Botnet, Brute Force, DDoS, Web attacks, Infiltration).Format: PCAP, CSVUpdate Frequency: StaticUse Cases: Intrusion Detection UNSW-NB15Access Here: https://research.unsw.edu.au/projects/unsw-nb15-datasetDescription: Realistic lab traffic with normal and malicious flows across 9 attack families.Format: PCAP, CSV, Bro/ArgusUpdate Frequency: StaticUse Cases: IDS development, adversarial testing Bot-IoT DatasetAccess Here: https://research.unsw.edu.au/projects/bot-iot-datasetDescription: IoT botnet traffic dataset generated in a realistic testbed.Format: PCAP, Argus, CSVUpdate Frequency: StaticUse Cases: IoT security, botnet detection CTU-13 Botnet DatasetAccess Here: https://www.stratosphereips.org/datasets-ctu13Description: 13 labeled scenarios of botnet, normal, and background traffic.Format: PCAP, NetFlows, WebLogsUpdate Frequency: StaticUse Cases: Botnet detection, anomaly detection CERT Insider Threat Dataset (CMU/SEI)Access Here: https://kilthub.cmu.edu/articles/dataset/Insider_Threat_Test_Dataset/12841247Description: Synthetic enterprise insider-threat traces (background + malicious users).Format: CSV, LogsUpdate Frequency: StaticUse Cases: Insider Threat Detection TrojAI Dataset (NIST)Access Here: https://pages.nist.gov/trojai/Description: Dataset of AI models with and without injected backdoors.Format: ML Models + metadataUpdate Frequency: By roundUse Cases: AI poisoning, supply-chain attack research ImageNet-AAccess Here: https://www.tensorflow.org/datasets/catalog/imagenet_aDescription: 7,500 natural adversarial images that reliably fool ImageNet classifiers.Format: JPEGUpdate Frequency: StaticUse Cases: Adversarial robustness evaluation FGI-SpoofRepo (GNSS Spoofing Repository)Access Here: https://etsin.fairdata.fi/dataset/367379a8-7d78-4b08-91f0-8027ce7a621bDescription: GNSS I/Q recordings under spoofing scenarios (synchronous, async, meaconing).Format: I/Q binaryUpdate Frequency: StaticUse Cases: GPS spoofing detection TEXBAT (Texas Spoofing Test Battery)Access Here: https://radionavlab.ae.utexas.edu/texbat/Description: Canonical GPS spoofing datasets for evaluating resilience.Format: RF recordingsUpdate Frequency: StaticUse Cases: GNSS spoofing detection LiDAR Shadow Attack DatasetAccess Here: https://zenodo.org/records/15120571Description: Experimental LiDAR point clouds with adversarial shadow perturbations.Format: PCDUpdate Frequency: StaticUse Cases: LiDAR adversarial attack evaluation TartanAir DatasetAccess Here: https://theairlab.org/tartanair-dataset/Description: Synthetic dataset for SLAM with stereo, depth, optical flow, segmentation, LiDAR.Format: PNG, PCD, JSONUpdate Frequency: OngoingUse Cases: SLAM robustness, AI sensor fusion ANISENSE Datasets CarlaScenes DatasetAccess Here: https://github.com/CarlaScenes/CarlaSenceDescription: Synthetic dataset for odometry in autonomous driving using the CARLA simulator.Format: PNG, PLY, BAGUpdate Frequency: OngoingUse Cases: Autonomous Driving, SLAM, Odometry, AI Security Realistic Vehicle Trajectories using CARLAAccess Here: https://ieee-dataport.org/documents/realistic-vehicle-trajectories-and-driving-parameters-carla-autonomous-driving-simulatorDescription: Realistic vehicle trajectories in CARLA’s simulated urban environments.Format: CSV, JSONUpdate Frequency: OngoingUse Cases: Autonomous Driving, Traffic Simulation, AI Security KITTI Vision Benchmark SuiteAccess Here: http://www.cvlibs.net/datasets/kitti/Description: Comprehensive dataset for training ML models in stereo, odometry, 3D detection, and segmentation.Format: PNG, BIN, TXTUpdate Frequency: OngoingUse Cases: Autonomous Driving, 3D Object Detection, Semantic Segmentation OUSTER DatasetAccess Here: https://ouster.com/downloads/sample-lidar-dataDescription: LiDAR data captured by Ouster sensors for super-resolution and 3D mapping.Format: Point CloudsUpdate Frequency: OngoingUse Cases: LiDAR Super-Resolution, 3D Mapping, AI Security SemanticPOSSAccess Here: http://www.poss.pku.edu.cn/Description: LiDAR dataset with instance-level annotations for dynamic objects (pedestrians, riders, vehicles).Format: PCD, PNGUpdate Frequency: StaticUse Cases: 3D Semantic Segmentation, Autonomous Driving, AI Security Siemens Dataset IPIN23 Indoor Positioning Challenge DatasetAccess Here: https://ipin-conference.org/2023/competition/Description: Multi-sensor dataset (WiFi, BLE, IMU, magnetometer) for indoor positioning and navigation.Format: CSV, Sensor logsUpdate Frequency: StaticUse Cases: Indoor localization, positioning security