[HTML][HTML] Uniting cyber security and machine learning: Advantages, challenges and future research
Abstract Machine learning (ML) is a subset of Artificial Intelligence (AI), which focuses on the
implementation of some systems that can learn from the historical data, identify patterns and …
implementation of some systems that can learn from the historical data, identify patterns and …
[HTML][HTML] Multi-aspect rule-based AI: Methods, taxonomy, challenges and directions toward automation, intelligence and transparent cybersecurity modeling for critical …
Critical infrastructure (CI) typically refers to the essential physical and virtual systems, assets,
and services that are vital for the functioning and well-being of a society, economy, or nation …
and services that are vital for the functioning and well-being of a society, economy, or nation …
“real attackers don't compute gradients”: bridging the gap between adversarial ml research and practice
Recent years have seen a proliferation of research on adversarial machine learning.
Numerous papers demonstrate powerful algorithmic attacks against a wide variety of …
Numerous papers demonstrate powerful algorithmic attacks against a wide variety of …
Sok: Explainable machine learning for computer security applications
Explainable Artificial Intelligence (XAI) aims to improve the transparency of machine
learning (ML) pipelines. We systematize the increasingly growing (but fragmented) …
learning (ML) pipelines. We systematize the increasingly growing (but fragmented) …
Generalizing intrusion detection for heterogeneous networks: A stacked-unsupervised federated learning approach
The constantly evolving digital transformation imposes new requirements on our society.
Aspects relating to reliance on the networking domain and the difficulty of achieving security …
Aspects relating to reliance on the networking domain and the difficulty of achieving security …
Enhancing ransomware attack detection using transfer learning and deep learning ensemble models on cloud-encrypted data
Ransomware attacks on cloud-encrypted data pose a significant risk to the security and
privacy of cloud-based businesses and their consumers. We present RANSOMNET+, a state …
privacy of cloud-based businesses and their consumers. We present RANSOMNET+, a state …
[HTML][HTML] A comprehensive survey on cyber deception techniques to improve honeypot performance
Honeypot technologies are becoming increasingly popular in cybersecurity as they offer
valuable insights into adversary behavior with a low rate of false detections. By diverting the …
valuable insights into adversary behavior with a low rate of false detections. By diverting the …
Review of cyberattack implementation, detection, and mitigation methods in cyber-physical systems
With the rapid proliferation of cyber-physical systems (CPSs) in various sectors, including
critical infrastructure, transportation, healthcare, and the energy industry, there is a pressing …
critical infrastructure, transportation, healthcare, and the energy industry, there is a pressing …
Malware detection with artificial intelligence: A systematic literature review
In this survey, we review the key developments in the field of malware detection using AI and
analyze core challenges. We systematically survey state-of-the-art methods across five …
analyze core challenges. We systematically survey state-of-the-art methods across five …
Spacephish: The evasion-space of adversarial attacks against phishing website detectors using machine learning
Existing literature on adversarial Machine Learning (ML) focuses either on showing attacks
that break every ML model, or defenses that withstand most attacks. Unfortunately, little …
that break every ML model, or defenses that withstand most attacks. Unfortunately, little …