The evolution of the Industrial Internet of Things (IIoT) introduces several benefits, such as real-time monitoring, pervasive control and self-healing. However, despite the valuable services, security and privacy issues still remain given the presence of legacy and insecure communication protocols like IEC 60870-5-104. IEC 60870-5-104 is an industrial protocol widely applied in critical infrastructures, such as the smart electrical grid and industrial healthcare systems.


SCVIC-CIDS-2021 was created using the raw data in CIC-IDS-2018*, while this new dataset, SCVIC-CIDS-2022 is formed from NDSec-1** meta-data by following a similar procedure.

 This dataset has been used in the following work:

J. Liu, M. Simsek, B. Kantarci, M. Bagheri, P. Djukic, "Bridging Networks and Hosts via Machine Learning-Based Intrusion Detection"; under review in IEEE Transactions on Dependable and Secure Computing.



hysically unclonable functions (PUFs) are hardware security primitives that utilize non-reproducible manufacturing variations to provide device-specific challenge-response pairs (CRPs). Such primitives are desirable for applications such as communication and intellectual property protection. PUFs have been gaining considerable interest from both the academic and industrial communities because of their simplicity and stability. However, many recent studies have exposed PUFs to machine-learning (ML) modeling attacks.


This abstract is based on the work described in the paper (submitted for IEEE Access review) titled "Evaluation of Machine Learning Model Improvement for Hardware Trojan Early Detection on Register Transfer Level Design Using Verilog/VHDL Code Branching Features". The submited dateset consist of 32 different trojan-inserted IP cores design. For each IP core design, the dataset provides some files that contain source code written in VHDL or Verilog design language. Some of the functions inside those files are malicious and some of them are clean.


This file contains all the MATLAB codes in this paper and an example of QAR data used,At the same time, a detailed instruction of MATLAB code is included in a word document.


GAN-generated faces look challenging to distinguish from genuine human faces. As a result, because synthetic images are presently being used as profile photos for fake identities on social media, they may have serious social consequences. Iris pattern anomalies might expose GAN-generated facial photos. When photographs are printed and scanned, it becomes more difficult to distinguish between genuine and counterfeit since fraudulent images lose some of their qualities.


SCVIC-CIDS-2021 is a novel dataset that combines network- and host-based data.


SCVIC-CIDS-2021 is derived from the meta-data (i.e., network packets, system logs and labeling information) from the well-known benchmark dataset, CIC-IDS-2018 (Iman Sharafaldin, Arash Habibi Lashkari, and Ali A. Ghorbani, “Toward Generating a New Intrusion Detection Dataset and Intrusion Traffic Characterization”, 4th International Conference on Information Systems Security and Privacy (ICISSP), Portugal, January 2018 ) .



This dataset is used for the identification of video in the internet traffic. The dataset was prepared by using Wireshark. It comprises of two types of traffic data, VPN (Virtual Private Network) or encrypted traffic data and Non-VPN or unencrypted traffic. The dataset consist of the data streams (.pcap) of 43 videos. Each video is played 50 times in both VPN and Non-VPN mode. The streams were obtained by setting-up a dummy client on a PC which plays a YouTube video and Wireshark is used to capture the internet traffic.


ReMouse dataset contains the mouse dynamics information of 100 users of mixed nationality, residing in diverse geographical regions and using different devices (hardware and software components). The dataset contains dozens of ‘repeat sessions’ per each user, where ‘repeat sessions’ are sessions during which the user is asked to complete the same logical task in a guided online environment (e.g., play an online game involving the same sequence of steps and intermediate objectives).