The increasing integration of cyber-physical systems in industrial environments has under scored the critical need for robust security measures to counteract evolving cyber threats. In response to this need, this work introduces an open-source dataset designed to enhance the development and evaluation of cybersecurity solutions for smart industries. The dataset comprises a traffic capture of an industrial control system (ICS) subjected to a variety of simulated cyber-attacks, including but not limited to denial of service (DoS), man-in-the-middle (MITM), and malware infiltration.


Smart grids are nowadays featured by distributed energy resources, both renewables, traditional sources and storage systems. Generally, these components are characterized by different control technologies that interact with the generators through smart inverters. This exposes them to a variety of cyber threats. In this context, there is a need to develop datasets of attacks on these systems, in order to evaluate the risks and allow researchers to develop proper monitoring algorithms.


5G Network slicing is one of the key enabling technologies that offer dedicated logical resources to different applications on the same physical network. However, a Denial-of-Service (DoS) or Distributed Denial-of-Service (DDoS) attack can severely damage the performance and functionality of network slices. Furthermore, recent DoS/DDoS attack detection techniques are based on the available data sets which are collected from simulated 5G networks rather than from 5G network slices.


You don’t need to be a cybersecurity expert to know that the world of application security is changing at an alarming pace. The tools and techniques that attackers use are becoming more sophisticated, and it’s difficult for even the most well-resourced organizations to keep up with them.


The security testing focuses on evaluating the security of the web, mobile, networks, API, SaaS, blockchain & cloud applications by methodically validating & verifying the effectiveness of security controls. The process involves an active analysis of any application for any available weaknesses, technical flaws, or vulnerabilities.


The security audit scope of work will include:


The IEEE 24 bus system is modelled in Digsilent’s Power factory, and normal operating data points are generated under quasi dynamic simulation and N-2 contingencies.  An optimization problem to generate false data injection attack vectors are then modelled using MATLAB. This data contains normal, contingency and attack data points for the necessary variables in a power network. The attack data has 5 parts, with each part generated by a different type of attack.


This dataset is composed of side channel information (e.g., temperatures, voltages, utilization rates) from computing systems executing benign and malicious code.  The intent of the dataset is to allow aritificial intelligence tools to be applied to malware detection using side channel information.


The dataset contains:
1. We conducted a A 24-hour recording of ADS-B signals at DAB on 1090 MHz with USRP B210 (8 MHz sample rate). In total, we got the signals from more than 130 aircraft.
2. An enhanced gr-adsb, in which each message's digital baseband (I/Q) signals and metadata (flight information) are recorded simultaneously. The output file path can be specified in the property panel of the ADS-B decoder submodule.
3. Our GnuRadio flow for signal reception.
4. Matlab code of the paper, wireless device identification using the zero-bias neural network.


Presented here is a dataset used for our SCADA cybersecurity research. The dataset was built using our SCADA system testbed described in our paper below [*]. The purpose of our testbed was to emulate real-world industrial systems closely. It allowed us to carry out realistic cyber-attacks.