Results 91 to 100 of about 3,386 (193)

A BERT and PSO framework for Android malware detection using real permissions and API calls

Discover Computing
The rapid expansion of mobile connectivity and the global reliance on smartphones have positioned Android as the leading platform, driven by its affordability and open source framework.
Abhinandan Banik, Jyoti Prakash Singh
doaj   +1 more source

Adversarial Robustness of Deep Learning-Based Malware Detectors via (De)Randomized Smoothing

IEEE Access
Deep learning-based malware detectors have been shown to be susceptible to adversarial malware examples, i.e. malware examples that have been deliberately manipulated in order to avoid detection.
Daniel Gibert, Giulio Zizzo, Quan Le, Jordi Planes   +3 more
doaj   +1 more source

Behaviour-based malware detection on Android phones

, 2020
With the proliferation of Android malware, the demand for an effective and efficient malware detection system is on the rise. The existing device-end learning based solutions tend to extract limited syntax features, such as permissions and API calls, to ...
Lim, Jing Qiang
core  

Machine Learning-Based Malware Detection for Android Applications: History Matters! [PDF]

, 2014
Machine Learning-based malware detection is a promis- ing scalable method for identifying suspicious applica- tions. In particular, in today’s mobile computing realm where thousands of applications are daily poured into markets, such a technique ...
Jacques Klein, Yves Le Traon, LE TRAON, Yves, BISSYANDE, Tegawendé François D Assise, ALLIX, Kevin, KLEIN, Jacques, Kevin Allix, Tegawende ́ F. Bissyandé   +7 more
core  

A state-of-the-art survey of malware detection approaches using data mining techniques

, 2018
Data mining techniques have been concentrated for malware detection in the recent decade. The battle between security analyzers and malware scholars is everlasting as innovation grows.
Rahil Hosseini, Alireza Souri
core   +1 more source

Binary Code Analysis for Cybersecurity: A Systematic Review of Forensic Techniques in Vulnerability Detection and Anti-Evasion Strategies

IEEE Access
Binary code analysis is essential in modern cybersecurity, examining compiled program outputs to identify vulnerabilities, detect malware, and ensure software security compliance.
Haseeb Javed, Farman Ali, Babar Shah, Daehan Kwak   +3 more
doaj   +1 more source

Deep learning fusion for effective malware detection: leveraging visual features

Malware has become a formidable threat as it has grown exponentially in number and sophistication. Thus, it is imperative to have a solution that is easy to implement, reliable, and effective.
Johny J. A., Conti M., Asmitha K. A., Radhamani G., Rehiman K. A. R., Vinod P.   +5 more
core   +1 more source

OPTISTACK: A Hybrid Ensemble Learning and XAI-Based Approach for Malware Detection in Compressed Files

IEEE Access
The increasing reliance on compressed file formats for data storage and transmission has made them attractive vectors for malware propagation, as their structural complexity enables evasion of conventional detection mechanisms.
Khaled Mahmud Sujon, Rohayanti Binti Hassan, M. Abdullah-Al-Wadud, Jia Uddin   +3 more
doaj   +1 more source

MADLIRA -a tool for Android malware detection

, 2021
International audienceToday, there are more threats to Android users since malware writers are changing their target to explore the weakness of Android devices, in order to generate malicious behaviors.
Dam, Khanh Huu The, Touili, Tayssir
core  

Evaluating Realistic Adversarial Attacks against Machine Learning Models for Windows PE Malware Detection

Future Internet
During the last decade, the cybersecurity literature has conferred a high-level role to machine learning as a powerful security paradigm to recognise malicious software in modern anti-malware systems.
Muhammad Imran, Annalisa Appice, Donato Malerba   +2 more
doaj   +1 more source