Deep learning for zero-day malware detection and classification: A survey

F Deldar, M Abadi - ACM Computing Surveys, 2023 - dl.acm.org
Zero-day malware is malware that has never been seen before or is so new that no anti-
malware software can catch it. This novelty and the lack of existing mitigation strategies …

[HTML][HTML] Deep neural networks in the cloud: Review, applications, challenges and research directions

KY Chan, B Abu-Salih, R Qaddoura, AZ Ala'M… - Neurocomputing, 2023 - Elsevier
Deep neural networks (DNNs) are currently being deployed as machine learning technology
in a wide range of important real-world applications. DNNs consist of a huge number of …

A survey on malware detection with graph representation learning

T Bilot, N El Madhoun, K Al Agha, A Zouaoui - ACM Computing Surveys, 2024 - dl.acm.org
Malware detection has become a major concern due to the increasing number and
complexity of malware. Traditional detection methods based on signatures and heuristics …

[HTML][HTML] Adversarial machine learning in IoT from an insider point of view

F Aloraini, A Javed, O Rana, P Burnap - Journal of Information Security and …, 2022 - Elsevier
With the rapid progress and significant successes in various applications, machine learning
has been considered a crucial component in the Internet of Things ecosystem. However …

[HTML][HTML] Machine learning for android malware detection: mission accomplished? a comprehensive review of open challenges and future perspectives

A Guerra-Manzanares - Computers & Security, 2024 - Elsevier
The extensive research in machine learning based Android malware detection showcases
high-performance metrics through a wide range of proposed solutions. Consequently, this …

Parallel Deep Learning with a hybrid BP-PSO framework for feature extraction and malware classification

MN Al-Andoli, SC Tan, KS Sim, CP Lim, PY Goh - Applied Soft Computing, 2022 - Elsevier
Malicious software (Malware) is a key threat to security of digital networks and systems.
While traditional machine learning methods have been widely used for malware detection …

A holistic review of machine learning adversarial attacks in IoT networks

H Khazane, M Ridouani, F Salahdine, N Kaabouch - Future Internet, 2024 - mdpi.com
With the rapid advancements and notable achievements across various application
domains, Machine Learning (ML) has become a vital element within the Internet of Things …

An ensemble-based parallel deep learning classifier with PSO-BP optimization for malware detection

MN Al-Andoli, KS Sim, SC Tan, PY Goh, CP Lim - IEEE Access, 2023 - ieeexplore.ieee.org
Digital networks and systems are susceptible to malicious software (malware) attacks. Deep
learning (DL) models have recently emerged as effective methods to classify and detect …

A method for automatic android malware detection based on static analysis and deep learning

M İbrahim, B Issa, MB Jasser - IEEE Access, 2022 - ieeexplore.ieee.org
The computers nowadays are being replaced by the smartphones for the most of the internet
users around the world, and Android is getting the most of the smartphone systems' market …

Enimanal: Augmented cross-architecture IoT malware analysis using graph neural networks

L Deng, H Wen, M Xin, H Li, Z Pan, L Sun - Computers & Security, 2023 - Elsevier
IoT malware analysis is crucial for understanding the behavior and purpose of malware
samples. While deep learning methods have been applied to IoT malware analysis using …