"What Is the Problem Space?" Defining Host-Space Adversarial Perturbations against Network Intrusion Detection Systems

Network Intrusion Detection Systems NIDS are now increasingly leveraging Machine Learning ML techniques to detect malicious network activities. Numerous papers have scrutinized the security of ML-based NIDS ML-NIDS by testing them against various attacks involving adversarial perturbations. The...

Human Vulnerability Assessment in Cybersecurity: A Systematic Literature Review of Methods, Models, and Instruments

In cybersecurity, vulnerability assessment has typically focused on identifying and measuring vulnerabilities within digital assets and technical infrastructures. However, there is growing recognition that this approach alone is inadequate without a structured examination of the human factor, whi...

A Systematic Literature Review for Transformer-Based Software Vulnerability Detection

Context: Software vulnerabilities pose significant security threats to software systems, especially as software is increasingly used across many areas of daily life, including health, government, and finance. Recently, transformer-based models have demonstrated promising results in automatic...

A LINDDUN-Based Privacy Threat Modeling Framework for GenAI

As generative AI GenAI systems become increasingly prevalent across various technological stacks, the question of how such systems handle sensitive and personal data flows becomes increasingly important. Specifically, both the ability to harness and process large swaths of information as well as...

True Random Number Generators on IQM Spark

Random number generation is fundamental for many modern applications including cryptography, simulations and machine learning. Traditional pseudo-random numbers may offer statistical unpredictability, but are ultimately deterministic. On the other hand, True Random Number Generation TRNG offers...

Digital Sovereignty Control Framework for Military AI-Based Cyber Security

In today's evolving threat landscape, ensuring digital sovereignty has become mandatory for military organizations, especially given their increased development and investment in AI-driven cyber security solutions. To this end, a multi-angled framework is proposed in this article in order to defi...

Developing a Risk Identification Framework for Foundation Model Uses

As foundation models grow in both popularity and capability, researchers have uncovered a variety of ways that the models can pose a risk to the model's owner, user, or others. Despite the efforts of measuring these risks via benchmarks and cataloging them in AI risk taxonomies, there is little...

Comparative Analysis of AI-Driven Security Approaches in DevSecOps: Challenges, Solutions, and Future Directions

The integration of security within DevOps, known as DevSecOps, has gained traction in modern software development to address security vulnerabilities while maintaining agility. Artificial Intelligence AI and Machine Learning ML have been increasingly leveraged to enhance security automation, thre...