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 …
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 …
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
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 …
which helps to efficiently analyze large numbers of malware variants. Methods based on …
HIT4Mal: Hybrid image transformation for malware classification
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 …
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
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 …
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 …
system operation. Malware can easily escape from existing dynamic analysis methods …
A convolutional transformation network for malware classification
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 …
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 …
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 …
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 …
is the most common means of network attack, which threatens user information and property …