The explosive growth of vulnerabilities poses a significant threat to the security software systems. While various deep-learning-based vulnerability detection methods have emerged, they primarily rely on semantic features extracted from single code representation structure, which limits their ability detect hidden deep within code. To address this limitation, we propose S2FVD, short for Sequenc...

Detecting source code vulnerabilities is an essential issue today. In this paper, to improve the efficiency of detecting in software written C/C++, we propose use a combination Deep Graph Convolutional Neural Network (DGCNN) and property graph (CPG). Specifically, 3 main proposed phases research method include: phase 1: building feature profiles code. At step, suggest using analysis techniques ...

Most existing surveys and reviews on web application vulnerability detection (WAVD) approaches focus comparing summarizing the approaches’ technical details. Although some studies have analyzed efficiency effectiveness of specific methods, there is a lack comprehensive systematic analysis various WAVD approaches. We conducted literature review (SLR) their effectiveness. identified 105 primary o...

Phishing risk is a growing area of concern for corporations, governments, and individuals. Given the evidence that users vary widely in their vulnerability to phishing attacks, we demonstrate an approach for assessing the benefits and costs of interventions that target the most vulnerable users. Our approach uses Monte Carlo simulation to (1) identify which users were most vulnerable, in signal...

This paper introduces a new analysis method for early detection and prevention of power system cascading events. It uses the vulnerability index (VI) and margin index (MI) to evaluate the vulnerability and security of the individual system parts, as well as the whole system during an operating state. It identifies the vulnerable parts of the power system using the topology processing and operat...