Security

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 necessity for strong security measures to fend off cyberattacks has increased due to the growing use of Industrial Internet of Things (IIoT) technologies. This research introduces IoTForge Pro, a comprehensive security testbed designed to generate a diverse and extensive intrusion dataset for IIoT environments. The testbed simulates various IIoT scenarios, incorporating network topologies and communication protocols to create realistic attack vectors and normal traffic patterns.

This document presents the probability calculation for distinguishing Hamming weight distributions. Given two distinct Hamming weight scenarios (HW = 0 and HW = 1), we derive the probability that an observed value \(x\) originates from the HW = 0 distribution. Utilizing Bayes' theorem, we calculate the conditional probability and provide the final expression for \(P(HW = 0 \mid x)\).

We propose a deep learning-based dataset for smart contract vulnerability detection, combining three public datasets to support and facilitate blockchain security research. This comprehensive dataset includes a variety of common types of smart contract vulnerabilities, such as re-entrancy attacks, integer overflows, and improper access controls.

By consolidating and uniformly annotating the data, we provide detailed vulnerability information and classification tags for each smart contract. The main features and contributions of the dataset are as follows: