Autonomous Defending Approaches Based on Machine Learning for Android Malware Detection DOI
Khalid Alemerien, Mohammad Almseidin, Enshirah Altarawneh

et al.

Advances in computational intelligence and robotics book series, Journal Year: 2025, Volume and Issue: unknown, P. 325 - 374

Published: April 23, 2025

The exponential growth in Android device usage has led to a surge malware targeting this platform, necessitating robust security mechanisms safeguard user data and system integrity. Traditional signature-based methods for detection often lag behind the ever-evolving threat landscape. This chapter explores autonomous defending approaches leveraging advanced Machine Learning (ML) techniques. outlines unique challenges detection, such as diverse app ecosystems obfuscation techniques employed by developers. Through analysis of real-world datasets performance benchmarks, demonstrates practical effectiveness ML-based limitations. Finally, potential integration systems with broader cybersecurity frameworks future research directions are discussed. aims serve comprehensive resource researchers practitioners field cybersecurity.

Language: Английский

Autonomous Defending Approaches Based on Machine Learning for Android Malware Detection DOI
Khalid Alemerien, Mohammad Almseidin, Enshirah Altarawneh

et al.

Advances in computational intelligence and robotics book series, Journal Year: 2025, Volume and Issue: unknown, P. 325 - 374

Published: April 23, 2025

The exponential growth in Android device usage has led to a surge malware targeting this platform, necessitating robust security mechanisms safeguard user data and system integrity. Traditional signature-based methods for detection often lag behind the ever-evolving threat landscape. This chapter explores autonomous defending approaches leveraging advanced Machine Learning (ML) techniques. outlines unique challenges detection, such as diverse app ecosystems obfuscation techniques employed by developers. Through analysis of real-world datasets performance benchmarks, demonstrates practical effectiveness ML-based limitations. Finally, potential integration systems with broader cybersecurity frameworks future research directions are discussed. aims serve comprehensive resource researchers practitioners field cybersecurity.

Language: Английский

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