Hybrid Android Malware Detection: A Review of Heuristic-Based Approach

RA Yunmar, SS Kusumawardani, F Mohsen - IEEE Access, 2024 - ieeexplore.ieee.org
Over the last decade, numerous research efforts have been dedicated to countering
malicious mobile applications. Given its market share, Android OS has been the primary …

Deep learning-powered malware detection in cyberspace: a contemporary review

A Redhu, P Choudhary, K Srinivasan, TK Das - Frontiers in Physics, 2024 - frontiersin.org
This article explores deep learning models in the field of malware detection in cyberspace,
aiming to provide insights into their relevance and contributions. The primary objective of the …

An empirical study of efficient malware detection analysis on android mobile phones using machine learning

PM Shimpi, NN Pise - 2023 International Conference on …, 2023 - ieeexplore.ieee.org
Nowadays, smartphones occupy a significant role in everyone's life; in the present
circumstances, android mobile applications as well as their security threats were improved …

A Study of Different Approaches for Malware Detection in Smartphones

N Agnihotri - 2023 7th International Conference on Intelligent …, 2023 - ieeexplore.ieee.org
Countless smartphone devices carrying a lot of sensitive data have emerged as a
consequence of the increasing use of smartphone applications and high-speed wireless …

Securing ML-based Android Malware Detectors: A Defensive Feature Selection Approach against Backdoor Attacks

B Marek, K Pieniążek, F Ratajczak… - 2024 IEEE 24th …, 2024 - ieeexplore.ieee.org
This work investigates the vulnerability of ML-based Android malware detectors to backdoor
attacks, and proposes a novel feature selection method to reduce system vulnerability. We …

MoAT: Meta-Evaluation of Anti-Malware Trustworthiness

S Lin, C Paar - NeurIPS ML Safety Workshop - openreview.net
Many studies have proposed methods for the automated detection of malware. The
benchmarks used for evaluating these methods often vary, hindering a trustworthy …