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 …

Adversarial attacks against Windows PE malware detection: A survey of the state-of-the-art

X Ling, L Wu, J Zhang, Z Qu, W Deng, X Chen… - Computers & …, 2023‏ - Elsevier
Malware has been one of the most damaging threats to computers that span across multiple
operating systems and various file formats. To defend against ever-increasing and ever …

IMCFN: Image-based malware classification using fine-tuned convolutional neural network architecture

D Vasan, M Alazab, S Wassan, H Naeem, B Safaei… - Computer Networks, 2020‏ - Elsevier
The volume, type, and sophistication of malware is increasing. Deep convolutional neural
networks (CNNs) have lately proven their effectiveness in malware binary detection through …

Image-Based malware classification using ensemble of CNN architectures (IMCEC)

D Vasan, M Alazab, S Wassan, B Safaei, Q Zheng - Computers & Security, 2020‏ - Elsevier
Both researchers and malware authors have demonstrated that malware scanners are
unfortunately limited and are easily evaded by simple obfuscation techniques. This paper …

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 …

Detection of malicious code variants based on deep learning

Z Cui, F Xue, X Cai, Y Cao, G Wang… - IEEE Transactions on …, 2018‏ - ieeexplore.ieee.org
With the development of the Internet, malicious code attacks have increased exponentially,
with malicious code variants ranking as a key threat to Internet security. The ability to detect …

Malware detection by eating a whole exe

E Raff, J Barker, J Sylvester, R Brandon… - arxiv preprint arxiv …, 2017‏ - arxiv.org
In this work we introduce malware detection from raw byte sequences as a fruitful research
area to the larger machine learning community. Building a neural network for such a …

[HTML][HTML] Early-stage malware prediction using recurrent neural networks

M Rhode, P Burnap, K Jones - computers & security, 2018‏ - Elsevier
Static malware analysis is well-suited to endpoint anti-virus systems as it can be conducted
quickly by examining the features of an executable piece of code and matching it to …

Analysis of ResNet and GoogleNet models for malware detection

RU Khan, X Zhang, R Kumar - Journal of Computer Virology and Hacking …, 2019‏ - Springer
We have utilized two distinct models to identify the obscure or new sort of malware in this
paper. GoogleNet and ResNet models are researched and tried which belong to two …

Malicious code detection based on CNNs and multi-objective algorithm

Z Cui, L Du, P Wang, X Cai, W Zhang - Journal of Parallel and Distributed …, 2019‏ - Elsevier
An increasing amount of malicious code causes harm on the internet by threatening user
privacy as one of the primary sources of network security vulnerabilities. The detection of …