Ransomware reloaded: Re-examining its trend, research and mitigation in the era of data exfiltration
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 …
destroying valuable user data and causing numerous disruptions to victims. The severity of …
Cybersecurity data science: an overview from machine learning perspective
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 …
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
Although ransomware has been around since the early days of personal computers, its
sophistication and aggression have increased substantially over the years. Ransomware, as …
sophistication and aggression have increased substantially over the years. Ransomware, as …
Improving performance of autoencoder-based network anomaly detection on nsl-kdd dataset
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 …
block or stop cyberattacks. With the recent advancement of Artificial Intelligence (AI), there …
Deep Q-learning based reinforcement learning approach for network intrusion detection
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 …
cyber defense solutions equipped with autonomous agents capable of learning to make …
Ae-mlp: A hybrid deep learning approach for ddos detection and classification
Distributed Denial-of-Service (DDoS) attacks are increasing as the demand for Internet
connectivity massively grows in recent years. Conventional shallow machine learning-based …
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
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 …
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
The advancement of modern Operating Systems (OSs), and the popularity of personal
computing devices with Internet connectivity, have facilitated the proliferation of ransomware …
computing devices with Internet connectivity, have facilitated the proliferation of ransomware …
The inadequacy of entropy-based ransomware detection
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 …
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
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 …
as the cost incurred by the people and organisations impacted in a successful attack. In the …