The age of ransomware: A survey on the evolution, taxonomy, and research directions

S Razaulla, C Fachkha, C Markarian… - IEEE …, 2023 - ieeexplore.ieee.org
The proliferation of ransomware has become a significant threat to cybersecurity in recent
years, causing significant financial, reputational, and operational damage to individuals and …

A survey of android malware detection with deep neural models

J Qiu, J Zhang, W Luo, L Pan, S Nepal… - ACM Computing Surveys …, 2020 - dl.acm.org
Deep Learning (DL) is a disruptive technology that has changed the landscape of cyber
security research. Deep learning models have many advantages over traditional Machine …

Deep learning based attack detection for cyber-physical system cybersecurity: A survey

J Zhang, L Pan, QL Han, C Chen… - IEEE/CAA Journal of …, 2021 - ieeexplore.ieee.org
With the booming of cyber attacks and cyber criminals against cyber-physical systems
(CPSs), detecting these attacks remains challenging. It might be the worst of times, but it …

Software vulnerability detection using deep neural networks: a survey

G Lin, S Wen, QL Han, J Zhang… - Proceedings of the …, 2020 - ieeexplore.ieee.org
The constantly increasing number of disclosed security vulnerabilities have become an
important concern in the software industry and in the field of cybersecurity, suggesting that …

[HTML][HTML] Security and privacy in 6G networks: New areas and new challenges

M Wang, T Zhu, T Zhang, J Zhang, S Yu… - Digital Communications …, 2020 - Elsevier
With the deployment of more and more 5g networks, the limitations of 5g networks have
been found, which undoubtedly promotes the exploratory research of 6G networks as the …

Adversarial examples: A survey of attacks and defenses in deep learning-enabled cybersecurity systems

M Macas, C Wu, W Fuertes - Expert Systems with Applications, 2024 - Elsevier
Over the last few years, the adoption of machine learning in a wide range of domains has
been remarkable. Deep learning, in particular, has been extensively used to drive …

Adversarial deep ensemble: Evasion attacks and defenses for malware detection

D Li, Q Li - IEEE Transactions on Information Forensics and …, 2020 - ieeexplore.ieee.org
Malware remains a big threat to cyber security, calling for machine learning based malware
detection. While promising, such detectors are known to be vulnerable to evasion attacks …

Deep learning for android malware defenses: a systematic literature review

Y Liu, C Tantithamthavorn, L Li, Y Liu - ACM Computing Surveys, 2022 - dl.acm.org
Malicious applications (particularly those targeting the Android platform) pose a serious
threat to developers and end-users. Numerous research efforts have been devoted to …

Adversarial machine learning: A multilayer review of the state-of-the-art and challenges for wireless and mobile systems

J Liu, M Nogueira, J Fernandes… - … Surveys & Tutorials, 2021 - ieeexplore.ieee.org
Machine Learning (ML) models are susceptible to adversarial samples that appear as
normal samples but have some imperceptible noise added to them with the intention of …

Event detection in online social network: Methodologies, state-of-art, and evolution

X Hu, W Ma, C Chen, S Wen, J Zhang, Y **ang… - Computer Science …, 2022 - Elsevier
Online social network such as Twitter, Facebook and Instagram are increasingly becoming
the go-to medium for users to acquire information and discuss what is happening globally …