Hybrid Intrusion Detection Alert System using a Highly Scalable Framework on Commodity Hardware Server

Abstract

This abstract presents a novel approach to developing a Hybrid Intrusion Detection Alert System (HIDS) designed for deployment on commodity hardware servers, while maintaining high scalability and real-time performance. Traditional Intrusion Detection Systems (IDS) often struggle with the increasing volume and sophistication of cyber threats, requiring significant computational resources and often leading to scalability issues or high false positive rates. Our proposed HIDS integrates both signature-based and anomaly-based detection mechanisms to provide a comprehensive and robust security solution. The system leverages a highly scalable framework, utilizing distributed processing paradigms to efficiently analyze large volumes of network traffic and system logs. By distributing the detection workload across multiple commodity hardware nodes, the system achieves linear scalability, allowing for cost-effective expansion as network size and data throughput increase. The anomaly detection component utilizes machine learning algorithms trained on normal system behavior, enabling the identification of previously unknown threats. Furthermore, the system incorporates an intelligent alert correlation engine to reduce alert fatigue and provide actionable insights to security analysts. This framework aims to offer a practical, efficient, and economically viable solution for organizations seeking enhanced network security without incurring prohibitive infrastructure costs.