Cybersecurity

The smart TV ecosystem is rapidly expanding, allowing developers to publish their applications on TV markets to provide a wide array of services to TV users. However, this open nature can lead to significant cybersecurity concerns by bringing unauthorized access to home networks or leaking sensitive information.

The PermGuard dataset is a carefully crafted Android Malware dataset that maps Android permissions to exploitation techniques, providing valuable insights into how malware can exploit these permissions. It consists of 55,911 benign and 55,911 malware apps, creating a balanced dataset for analysis. APK files were sourced from AndroZoo, including applications scanned between January 1, 2019, and July 1, 2024. A novel construction method extracts Android permissions and links them to exploitation techniques, enabling a deeper understanding of permission misuse.

The security of Internet of Things (IoT) networks has become a major concern in recent years, as the number of connected objects continues to grow, thereby opening up more potential for malicious attacks. Supervised Machine Learning (ML) algorithms, which require a labeled dataset for training, are increasingly employed to detect attacks in IoT networks. However, existing datasets focus only on specific types of attacks, resulting in ML-based solutions that struggle to generalize effectively.

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.