ADVANCED SUPPORT VECTOR MACHINE BASED AGGREGATION METHOD FOR NETWORK ANOMALY DETECTION

Abstract

This study introduces the Advanced Support Vector Machine Based Aggregation Method (ASVAM) for network anomaly detection, addressing the critical need for robust cybersecurity measures in increasingly complex network environments. ASVAM combines the power of Support Vector Machines (SVMs) with novel aggregation techniques to enhance the accuracy and efficiency of anomaly detection. We evaluate ASVAM's performance using a comprehensive dataset of network traffic, comparing it against traditional anomaly detection methods and other machine learning approaches. Results demonstrate that ASVAM significantly outperforms existing methods in terms of detection accuracy, false positive rates, and computational efficiency. The aggregation component of ASVAM proves particularly effective in handling diverse types of network anomalies, including zero-day attacks. Furthermore, ASVAM shows remarkable adaptability to evolving network conditions, making it suitable for real-time threat detection in dynamic network environments. This research contributes to the field of network security by providing a more reliable and scalable approach to anomaly detection, potentially improving organizations' ability to defend against a wide range of cyber threats. Future work will focus on optimizing ASVAM for specific network architectures and exploring its applicability in cloud and IoT environments.