This dataset contains multimodal sensor data collected from side-channels while printing several types of objects on an Ultimaker 3 3D printer. Our related research paper titled "Sabotage Attack Detection for Additive Manufacturing Systems" can be found here: https://doi.org/10.1109/ACCESS.2020.2971947. In our work, we demonstrate that this sensor data can be used with machine learning algorithms to detect sabotage attacks on the 3D printer.

The DroneDetect dataset consists of 7 different models of popular Unmanned Aerial Systems (UAS) including the new DJI Mavic 2 Air S, DJI Mavic Pro, DJI Mavic Pro 2, DJI Inspire 2, DJI Mavic Mini, DJI Phantom 4 and the Parrot Disco. Recordings were collected using a Nuand BladeRF SDR and using open source software GNURadio. There are 4 subsets of data included in this dataset, the UAS signals in the presence of Bluetooth interference, in the presence of Wi-Fi signals, in the presence of both and with no interference.

iot dataset

Dataset used in the article "The Reverse Problem of Keystroke Dynamics: Guessing Typed Text with Keystroke Timings". CSV files with dataset results summaries, the evaluated sentences, detailed results, and scores. Results data contains training and evaluation ARFF files for each user, containing features of synthetic and legitimate samples as described in the article. The source data comes from three free text keystroke dynamics datasets used in previous studies, by the authors (LSIA) and two other unrelated groups (KM, and PROSODY, subdivided in GAY, GUN, and REVIEW).

Dataset including over 40,000 generated images of malicious binaries for malware classification in machine learning as outlined in NARAD - A Novel Auto-learn Real-time Fuzzy Machine Learning Anomaly Detection and Classification System.

This dataset supports researchers in the validation process of solutions such as Intrusion Detection Systems (IDS) based on artificial intelligence and machine learning techniques for the detection and categorization of threats in Cyber Physical Systems (CPS). To that aim, data have been acquired from a Secure Water Treatment (SWaT) hardware-in-the-loop testbed which emulates water passage between nine tanks via solenoid-valves, pumps, pressure and flow sensors. The testbed is composed by a real partition which is virtually connected to a simulated one.

Supplemental material for paper "Energy Efficiency Analysis of Post-Quantum Cryptographic Algorithms."

·       9/11 hijackers network dataset [20]: The 9/11 hijackers network incorporates 61 nodes (each node is a terrorist involved in 9/11 bombing at World Trade Centers in 2011). Dataset was prepared based on some news report, and ties range from ‘at school with’ to ‘on the same plane’. The Data consists of a mode matrix with 19*19 terrorist by terrorist having trusted prior contacts with 1 mode matrix of 61 edges of other involved associates.

The S3 dataset contains the behaviour (sensors, statistics of applications, and voice) of 21 volunteers interacting with their smartphones for more than 60 days. The type of users is diverse, males and females in the age range from 18 until 70 have been considered in the dataset generation. The wide range of age is a key aspect, due to the impact of age in terms of smartphone usage. To generate the dataset the volunteers installed a prototype of the smartphone application in on their Android mobile phones.