A comprehensive survey on deep learning based malware detection techniques

M Gopinath, SC Sethuraman - Computer Science Review, 2023 - Elsevier
Recent theoretical and practical studies have revealed that malware is one of the most
harmful threats to the digital world. Malware mitigation techniques have evolved over the …

Internet of things (iot) security intelligence: a comprehensive overview, machine learning solutions and research directions

IH Sarker, AI Khan, YB Abushark, F Alsolami - Mobile Networks and …, 2023 - Springer
Abstract The Internet of Things (IoT) is one of the most widely used technologies today, and
it has a significant effect on our lives in a variety of ways, including social, commercial, and …

Ai-driven cybersecurity: an overview, security intelligence modeling and research directions

IH Sarker, MH Furhad, R Nowrozy - SN Computer Science, 2021 - Springer
Artificial intelligence (AI) is one of the key technologies of the Fourth Industrial Revolution (or
Industry 4.0), which can be used for the protection of Internet-connected systems from cyber …

Deep learning approach for intelligent intrusion detection system

R Vinayakumar, M Alazab, KP Soman… - Ieee …, 2019 - ieeexplore.ieee.org
Machine learning techniques are being widely used to develop an intrusion detection
system (IDS) for detecting and classifying cyberattacks at the network-level and the host …

Automation of human behaviors and its prediction using machine learning

H Jupalle, S Kouser, AB Bhatia, N Alam… - Microsystem …, 2022 - Springer
Prediction is a method of detecting a person's behavior toward online buying by evaluating
publically available evaluations on the web. Understanding expressive human …

Robust intelligent malware detection using deep learning

R Vinayakumar, M Alazab, KP Soman… - IEEE …, 2019 - ieeexplore.ieee.org
Security breaches due to attacks by malicious software (malware) continue to escalate
posing a major security concern in this digital age. With many computer users, corporations …

Blockchain technology in supply chain management: an empirical study of the factors affecting user adoption/acceptance

M Alazab, S Alhyari, A Awajan, AB Abdallah - Cluster Computing, 2021 - Springer
Blockchain overcomes numerous complicated problems related to confidentiality, integrity,
availability of fast and secure distributed systems. Using data from a cross-sectoral survey of …

COVID-19 prediction and detection using deep learning

M Alazab, A Awajan, A Mesleh… - International Journal of …, 2020 - cspub-ijcisim.org
Currently, the detection of coronavirus disease 2019 (COVID-19) is one of the main
challenges in the world, given the rapid spread of the disease. Recent statistics indicate that …

Cybersecurity threats and their mitigation approaches using Machine Learning—A Review

M Ahsan, KE Nygard, R Gomes… - … of Cybersecurity and …, 2022 - mdpi.com
Machine learning is of rising importance in cybersecurity. The primary objective of applying
machine learning in cybersecurity is to make the process of malware detection more …

Intelligent mobile malware detection using permission requests and API calls

M Alazab, M Alazab, A Shalaginov, A Mesleh… - Future Generation …, 2020 - Elsevier
Malware is a serious threat that has been used to target mobile devices since its inception.
Two types of mobile malware attacks are standalone: fraudulent mobile apps and injected …