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[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 …
[HTML][HTML] Multi-aspect rule-based AI: Methods, taxonomy, challenges and directions towards 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 …
[HTML][HTML] Explainable AI for cybersecurity automation, intelligence and trustworthiness in digital twin: Methods, taxonomy, challenges and prospects
Digital twins (DTs) are an emerging digitalization technology with a huge impact on today's
innovations in both industry and research. DTs can significantly enhance our society and …
innovations in both industry and research. DTs can significantly enhance our society and …
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 …
[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] Evolving techniques in cyber threat hunting: A systematic review
In the rapidly changing cybersecurity landscape, threat hunting has become a critical
proactive defense against sophisticated cyber threats. While traditional security measures …
proactive defense against sophisticated cyber threats. While traditional security measures …
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 …
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) …
[HTML][HTML] 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 …