Deep learning: systematic review, models, challenges, and research directions

T Talaei Khoei, H Ould Slimane… - Neural Computing and …, 2023 - Springer
The current development in deep learning is witnessing an exponential transition into
automation applications. This automation transition can provide a promising framework for …

[HTML][HTML] Advancing IoT security: A systematic review of machine learning approaches for the detection of IoT botnets

A Nazir, J He, N Zhu, A Wajahat, X Ma, F Ullah… - Journal of King Saud …, 2023 - Elsevier
Abstract The Internet of Things (IoT) has transformed many aspects of modern life, from
healthcare and transportation to home automation and industrial control systems. However …

Unifying the perspectives of nlp and software engineering: A survey on language models for code

Z Zhang, C Chen, B Liu, C Liao, Z Gong, H Yu… - arxiv preprint arxiv …, 2023 - arxiv.org
In this work we systematically review the recent advancements in software engineering with
language models, covering 70+ models, 40+ evaluation tasks, 180+ datasets, and 900 …

[HTML][HTML] Explainability in AI-based behavioral malware detection systems

A Galli, V La Gatta, V Moscato, M Postiglione… - Computers & …, 2024 - Elsevier
Nowadays, our security and privacy are strongly threatened by malware programs which
aim to steal our confidential data and make our systems out of service, among other things …

A review of state-of-the-art malware attack trends and defense mechanisms

J Ferdous, R Islam, A Mahboubi, MZ Islam - IEEe Access, 2023 - ieeexplore.ieee.org
The increasing sophistication of malware threats has led to growing concerns in the anti-
malware community, as malware poses a significant danger to online users despite the …

[HTML][HTML] Android malware detection and identification frameworks by leveraging the machine and deep learning techniques: A comprehensive review

SK Smmarwar, GP Gupta, S Kumar - Telematics and Informatics Reports, 2024 - Elsevier
The ever-increasing growth of online services and smart connectivity of devices have posed
the threat of malware to computer system, android-based smart phones, Internet of Things …

Network anomaly intrusion detection based on deep learning approach

YC Wang, YC Houng, HX Chen, SM Tseng - Sensors, 2023 - mdpi.com
The prevalence of internet usage leads to diverse internet traffic, which may contain
information about various types of internet attacks. In recent years, many researchers have …

Artificial intelligence-based malware detection, analysis, and mitigation

A Djenna, A Bouridane, S Rubab, IM Marou - Symmetry, 2023 - mdpi.com
Malware, a lethal weapon of cyber attackers, is becoming increasingly sophisticated, with
rapid deployment and self-propagation. In addition, modern malware is one of the most …

Machine learning aided malware detection for secure and smart manufacturing: a comprehensive analysis of the state of the art

S Rani, K Tripathi, A Kumar - International Journal on Interactive Design …, 2023 - Springer
In the last decade, the number of computer malware has grown rapidly. Currently,
cybercriminals typically use malicious software (malware) as a means of attacking industrial …

[HTML][HTML] A deep learning-based innovative technique for phishing detection in modern security with uniform resource locators

EA Aldakheel, M Zakariah, GA Gashgari, FA Almarshad… - Sensors, 2023 - mdpi.com
Organizations and individuals worldwide are becoming increasingly vulnerable to
cyberattacks as phishing continues to grow and the number of phishing websites grows. As …