Adapting to Evasive Tactics through Resilient Adversarial Machine Learning for Malware Detection

This paper presents the Adaptive Resilience-based Convolutional Network (ARCNet), a sophisticated machine learning framework specifically designed to detect advanced, evasive malware. ARCNet combines convolutional and recurrent neural networks, making it highly adaptable to changing cyber threats. I...

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Bibliographic Details
Published in2024 11th International Conference on Computing for Sustainable Global Development (INDIACom) pp. 1735 - 1741
Main Authors Krishna, G Bala, Sravan Kumar, G, Ramachandra, Mummadi, Sampurnima Pattem, K, Rani, D Sandhya, Kakarla, Geeta
Format Conference Proceeding
LanguageEnglish
Published Bharati Vidyapeeth, New Delhi 28.02.2024
Subjects
Adaptation models
Adaptive systems
Adversarial machine learning
Block chain
Blockchains
Challenges of Block chain
Consensus algorithm
Convolutional neural networks
Data privacy
Digital signatures
Distributed ledger
Faces
Malware
Organizations
Recurrent neural networks
Smart contracts
Support vector machines
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Summary:This paper presents the Adaptive Resilience-based Convolutional Network (ARCNet), a sophisticated machine learning framework specifically designed to detect advanced, evasive malware. ARCNet combines convolutional and recurrent neural networks, making it highly adaptable to changing cyber threats. Its core components, the Adversarial Learning Module (ALM), Predictive Analysis Engine (PAE), and Dynamic Adaptation System (DAS), significantly boost its detection power. Tests using a synthetic dataset show ARCNet's superiority over traditional models like the Support Vector Machine (SVM). It achieved 95.2% accuracy under normal conditions (compared to SVM's 89.4%) and maintained 92.5% accuracy even during adversarial attacks (against SVM's 80.3%). Notably, ARCNet's detection rates improved from 78.5% to 86.7% in five months after integrating the DAS. These results confirm ARCNet's efficiency in tackling complex malware challenges, contributing greatly to cybersecurity. The study underscores the importance of evolving and enhancing machine learning methods to keep pace with the rapidly changing landscape of cyber threats.
DOI:10.23919/INDIACom61295.2024.10498313