Integrating Behavioral Analytics and Intrusion Detection Systems to Protect Critical Infrastructure and Smart Cities

Abstract

In an age notable by growing digitization and relatedness, protecting critical infrastructure and smart cities against cyber threats is a biggest obstacle. This abstract examines the combination of Behavioural Analytics (BA) and Intrusion Detection Systems (IDS) as a active and best plan to boost cybersecurity defences. Behavioural Analytics uses machine learning algorithms and statistical models to notice usual entities behaviour patterns inside networks, empowering the identification of anomalies that indicate possible security infringements. This approach is improved through modern techniques that includes Statistical Anomaly Detection, which measures divergence and Long Short-Term Memory (LSTM) networks, skilled at grabbing temporal dependencies in data flow of network pursuit. Cross-Event Correlation methodologies and approaches improve the abilities of IDS by finding similarity between disparate events, giving a broad aspect of possible threats across inter related systems. Entity Behaviour Analysis (EBA) enhance these works by building thorough behaviour profiles and allocating risk scores based on divergence, improving targeted response plans. Network-Based IDS (NIDS) lengthen defence by observing whole networks for unusual activities, while Cyber Threat Intelligence (CTI) devices gives findings into progressing threats, enabling defensive security scales. Convolutional Neural Networks (CNNs) plays a part in removing complicated attribute from network data, improving anomaly detection. The results shows enhancements in threat detection accuracy, with a drop in false positives by 30% and an rise in anomaly detection precision to 95%. The Sensor Data (Units) changes from 80 to 90 units over monitored time periods.