A survey on machine learning-based malware detection in executable files

J Singh, J Singh - Journal of Systems Architecture, 2021 - Elsevier
In last decade, a proliferation growth in the development of computer malware has been
done. Nowadays, cybercriminals (attacker) use malware as a weapon to carry out the …

An efficient densenet-based deep learning model for malware detection

J Hemalatha, SA Roseline, S Geetha, S Kadry… - Entropy, 2021 - mdpi.com
Recently, there has been a huge rise in malware growth, which creates a significant security
threat to organizations and individuals. Despite the incessant efforts of cybersecurity …

Intelligent vision-based malware detection and classification using deep random forest paradigm

SA Roseline, S Geetha, S Kadry, Y Nam - IEEE Access, 2020 - ieeexplore.ieee.org
Malware is a rapidly increasing menace to modern computing. Malware authors continually
incorporate various sophisticated features like code obfuscations to create malware variants …

Experimental analysis of CPV/T solar dryer with nano-enhanced PCM and prediction of drying parameters using ANN and SVM algorithms

MO Karaağaç, A Ergün, Ü Ağbulut, AE Gürel, I Ceylan - Solar Energy, 2021 - Elsevier
In this paper, a concentrated photovoltaic-thermal solar dryer (CPV/TSD) using nano-
enhanced PCM (Al 2 O 3-Paraffin wax) is experimentally studied. A comprehensive …

[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 …

[图书][B] Introduction to machine learning with applications in information security

M Stamp - 2022 - taylorfrancis.com
Introduction to Machine Learning with Applications in Information Security, Second Edition
provides a classroom-tested introduction to a wide variety of machine learning and deep …

A multi-perspective malware detection approach through behavioral fusion of api call sequence

E Amer, I Zelinka, S El-Sappagh - Computers & Security, 2021 - Elsevier
The widespread development of the malware industry is considered the main threat to our e-
society. Therefore, malware analysis should also be enriched with smart heuristic tools that …

Identification of malware families using stacking of textural features and machine learning

S Kumar, B Janet, S Neelakantan - Expert Systems with Applications, 2022 - Elsevier
The growing rate of malware and its complexity demands a new approach to detecting
evolving malware instead of relying only on high-level features such as opcodes, API calls …

An effectiveness analysis of transfer learning for the concept drift problem in malware detection

DE García, N DeCastro-García… - Expert Systems with …, 2023 - Elsevier
Malware classification is a task that has acquired importance due to the increase in malware
distribution. In the literature, the application of machine learning techniques is proposed to …

An adaptive behavioral-based incremental batch learning malware variants detection model using concept drift detection and sequential deep learning

AA Darem, FA Ghaleb, AA Al-Hashmi… - IEEE …, 2021 - ieeexplore.ieee.org
Malware variants are the major emerging threats that face cybersecurity due to the potential
damage to computer systems. Many solutions have been proposed for detecting malware …