Ransomware reloaded: Re-examining its trend, research and mitigation in the era of data exfiltration

T McIntosh, T Susnjak, T Liu, D Xu, P Watters… - ACM Computing …, 2024 - dl.acm.org
Ransomware has grown to be a dominant cybersecurity threat by exfiltrating, encrypting, or
destroying valuable user data and causing numerous disruptions to victims. The severity of …

Cybersecurity data science: an overview from machine learning perspective

IH Sarker, ASM Kayes, S Badsha, H Alqahtani… - Journal of Big …, 2020 - Springer
In a computing context, cybersecurity is undergoing massive shifts in technology and its
operations in recent days, and data science is driving the change. Extracting security …

Ransomware mitigation in the modern era: A comprehensive review, research challenges, and future directions

T McIntosh, ASM Kayes, YPP Chen, A Ng… - ACM Computing …, 2021 - dl.acm.org
Although ransomware has been around since the early days of personal computers, its
sophistication and aggression have increased substantially over the years. Ransomware, as …

Improving performance of autoencoder-based network anomaly detection on nsl-kdd dataset

W Xu, J Jang-Jaccard, A Singh, Y Wei… - IEEE Access, 2021 - ieeexplore.ieee.org
Network anomaly detection plays a crucial role as it provides an effective mechanism to
block or stop cyberattacks. With the recent advancement of Artificial Intelligence (AI), there …

Deep Q-learning based reinforcement learning approach for network intrusion detection

H Alavizadeh, H Alavizadeh, J Jang-Jaccard - Computers, 2022 - mdpi.com
The rise of the new generation of cyber threats demands more sophisticated and intelligent
cyber defense solutions equipped with autonomous agents capable of learning to make …

Ae-mlp: A hybrid deep learning approach for ddos detection and classification

Y Wei, J Jang-Jaccard, F Sabrina, A Singh, W Xu… - IEEE …, 2021 - ieeexplore.ieee.org
Distributed Denial-of-Service (DDoS) attacks are increasing as the demand for Internet
connectivity massively grows in recent years. Conventional shallow machine learning-based …

A few-shot meta-learning based siamese neural network using entropy features for ransomware classification

J Zhu, J Jang-Jaccard, A Singh, I Welch, ALS Harith… - Computers & …, 2022 - Elsevier
Ransomware defense solutions that can quickly detect and classify different ransomware
classes to formulate rapid response plans have been in high demand in recent years …

[HTML][HTML] Applying staged event-driven access control to combat ransomware

T McIntosh, ASM Kayes, YPP Chen, A Ng… - Computers & Security, 2023 - Elsevier
The advancement of modern Operating Systems (OSs), and the popularity of personal
computing devices with Internet connectivity, have facilitated the proliferation of ransomware …

The inadequacy of entropy-based ransomware detection

T McIntosh, J Jang-Jaccard, P Watters… - … Conference, ICONIP 2019 …, 2019 - Springer
Many state-of-the-art anti-ransomware implementations monitoring file system activities
choose to monitor file entropy-based changes to determine whether the changes may have …

Differential area analysis for ransomware attack detection within mixed file datasets

SR Davies, R Macfarlane, WJ Buchanan - Computers & Security, 2021 - Elsevier
The threat from ransomware continues to grow both in the number of affected victims as well
as the cost incurred by the people and organisations impacted in a successful attack. In the …