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IET Cyber-Physical Systems: Theory &Applications, Volume 10, Issue 1, January/December 2025.The article proposes a novel concept of autonomous device protection based on behavioural profiling by continuously monitoring internal resource usage and exploiting a large language model to distinguish between benign and malicious behaviour. Abstract Demand for autonomous protection in computing devices cannot go unnoticed, considering the rapid ...
Sandeep Gupta, Bruno Crispo
wiley +1 more source
Challenges in Android Malware Analysis.
ERCIM News, 2016 The best protection against malware is to execute it: a security paradox.
Viet Triem Tong, Valérie +2 more
openaire +2 more sources
ANFIS-AMAL: Android Malware Threat Assessment Using Ensemble of ANFIS and GWO
Cybernetics and Information TechnologiesThe Android malware has various features and capabilities. Various malware has distinctive characteristics. Ransomware threatens financial loss and system lockdown.
Nwasra Nedal +2 more
doaj +1 more source
AMALGAN: Image‐Based Android Malware Classification Using Generative Adversarial Network
The Journal of EngineeringThe Android malware detection process requires analysing numerous files to ensure system security. Malware can also be embedded in media files and images.
Zahid Hussain Qaisar +2 more
doaj +1 more source
HTTP behavior characteristics generation and extraction approach for Android malware
Dianxin kexue, 2016 Growing of Android malware，not only seriously endangered the security of the Android market，but also brings challenges for detection.A generation and extraction approach of automatic Android malware behavioral signatures was proposed based on HTTP ...
Yaling LUO, Wenwei LI, Xin SU
doaj +2 more sources
Mağallaẗ Al-kūfaẗ Al-handasiyyaẗAndroid malware has grown steadily into a major internet threat. Despite efforts to identify and categorize malware in seemingly safe Android apps, addressing this issue is still lacking.
abdullah alsraratee, Ahmed Al-Azawei
doaj +1 more source
IEEE AccessWith an increase in the number and complexity of malware, traditional malware detection methods such as heuristic-based and signature-based ones have become less adequate, leaving user applications vulnerable.
Abdurraheem Joomye +4 more
doaj +1 more source
Not so Crisp, Malware! Fuzzy Classification of Android Malware Classes
2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2018 Mobile devices have been spreading at great rate in recent years. Not only smartphone, but also tablets and IoT devices, are gaining an increasingly place in our everyday lives. This is the reason why attackers are developing more and more aggressive techniques with the aim to exfiltrate our sensitive and private information.
Mercaldo F., Saracino A.
openaire +4 more sources
A3CM: Automatic Capability Annotation for Android Malware
IEEE Access, 2019 Android malware poses serious security and privacy threats to the mobile users. Traditional malware detection and family classification technologies are becoming less effective due to the rapid evolution of the malware landscape, with the emerging of so ...
Junyang Qiu +6 more
doaj +1 more source
Data Drift in Android Malware Detection
2024 International Conference on Machine Learning and Cybernetics (ICMLC)Android malware detectors are now widely implemented with machine learning algorithms, trained on large datasets of goodware and malware applications gathered at a fixed moment in time. However, as recent work showed, this domain is not stationary, causing detectors to show degrading performance over time.
Minnei, Luca +5 more
openaire +2 more sources