[HTML][HTML] A comprehensive survey on cyber deception techniques to improve honeypot performance

A Javadpour, F Ja'fari, T Taleb, M Shojafar… - Computers & …, 2024‏ - Elsevier
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 …

[HTML][HTML] Multi-aspect rule-based AI: Methods, taxonomy, challenges and directions towards automation, intelligence and transparent cybersecurity modeling for critical …

IH Sarker, H Janicke, MA Ferrag, A Abuadbba - Internet of Things, 2024‏ - Elsevier
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 …

“real attackers don't compute gradients”: bridging the gap between adversarial ml research and practice

G Apruzzese, HS Anderson, S Dambra… - … IEEE conference on …, 2023‏ - ieeexplore.ieee.org
Recent years have seen a proliferation of research on adversarial machine learning.
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

IH Sarker, H Janicke, A Mohsin, A Gill, L Maglaras - ICT Express, 2024‏ - Elsevier
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 …

Malware detection with artificial intelligence: A systematic literature review

MG Gaber, M Ahmed, H Janicke - ACM Computing Surveys, 2024‏ - dl.acm.org
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 …

[HTML][HTML] Uniting cyber security and machine learning: Advantages, challenges and future research

M Wazid, AK Das, V Chamola, Y Park - ICT express, 2022‏ - Elsevier
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 …

[HTML][HTML] Evolving techniques in cyber threat hunting: A systematic review

A Mahboubi, K Luong, H Aboutorab, HT Bui… - Journal of Network and …, 2024‏ - Elsevier
In the rapidly changing cybersecurity landscape, threat hunting has become a critical
proactive defense against sophisticated cyber threats. While traditional security measures …

Generalizing intrusion detection for heterogeneous networks: A stacked-unsupervised federated learning approach

G de Carvalho Bertoli, LAP Junior, O Saotome… - Computers & …, 2023‏ - Elsevier
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 …

Sok: Explainable machine learning for computer security applications

A Nadeem, D Vos, C Cao, L Pajola… - 2023 IEEE 8th …, 2023‏ - ieeexplore.ieee.org
Explainable Artificial Intelligence (XAI) aims to improve the transparency of machine
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

A Singh, Z Mushtaq, HA Abosaq, SNF Mursal, M Irfan… - Electronics, 2023‏ - mdpi.com
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 …