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 …

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 …

MalFCS: An effective malware classification framework with automated feature extraction based on deep convolutional neural networks

G Xiao, J Li, Y Chen, K Li - Journal of Parallel and Distributed Computing, 2020 - Elsevier
Identifying the family of malware can determine their malicious intent and attack patterns,
which helps to efficiently analyze large numbers of malware variants. Methods based on …

HIT4Mal: Hybrid image transformation for malware classification

DL Vu, TK Nguyen, TV Nguyen… - Transactions on …, 2020 - Wiley Online Library
Modern malware evolves various detection avoidance techniques to bypass the state‐of‐the‐
art detection methods. An emerging trend to deal with this issue is the combination of image …

Multiclass malware classification via first-and second-order texture statistics

V Verma, SK Muttoo, VB Singh - Computers & Security, 2020 - Elsevier
The generally increasing volume of malware poses a challenge to the predominantly used
static or dynamic analysis, which requires complex disassembly or time-intensive execution …

A malware classification method based on memory dump grayscale image

Y Dai, H Li, Y Qian, X Lu - Digital Investigation, 2018 - Elsevier
Effective analysis of malware is of great significance in guaranteeing the reliability of the
system operation. Malware can easily escape from existing dynamic analysis methods …

A convolutional transformation network for malware classification

DL Vu, TK Nguyen, TV Nguyen… - … on information and …, 2019 - ieeexplore.ieee.org
Modern malware evolves various detection avoidance techniques to bypass the state-of-the-
art detection methods. An emerging trend to deal with this issue is the combination of image …

Android malware familial classification based on dex file section features

Y Fang, Y Gao, FAN Jing, LEI Zhang - IEEE Access, 2020 - ieeexplore.ieee.org
The rapid proliferation of Android malware is challenging the classification of the Android
malware family. The traditional static method for classification is easily affected by the …

From data and model levels: Improve the performance of few-shot malware classification

Y Chai, J Qiu, L Yin, L Zhang… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Existing malware classification methods cannot handle the open-ended growth of new or
unknown malware well because it only focuses on pre-defined malware classes with …

Malicious code classification based on opcode sequences and textCNN network

Q Wang, Q Qian - Journal of Information Security and Applications, 2022 - Elsevier
A malicious code classification problem is essential for the network security. Malicious code
is the most common means of network attack, which threatens user information and property …