A Hybrid System for Integrating Cross-Correlation, DNNs and LSTMs for Enhanced Intrusion Detection System

A WSN is a network of wireless sensors that are becoming increasingly vulnerable to cyberattacks due to their widespread deployment and resource constraints. Traditional intrusion detection systems (IDS) often struggle to keep up with the evolving nature of threats, necessitating the adoption of more sophisticated techniques. We do this by using a combination of three powerful tools: Cross correlation, which can understand words from both directions, and two types of neural networks called DNNs and LSTMs. Basically, Cross-correlation measures the similarity between two time series, which can be used to identify patterns in sensor data that deviate from normal behaviour. It used for a variety of purposes, including feature extraction, recognition, filtering, and synchronization, it can help to protect WSNs from a variety of threats, including Denial-of-Service (DoS) attacks, data manipulation attacks, and malicious control attacks. LSTM-DNN models excel at extracting temporal patterns from sensor data, making them well-suited for intrusion detection tasks. Cross-correlation, a statistical measure of similarity between two time series, can further enhance anomaly detection by identifying deviations from normal behaviour patterns. Our tests on real intrusion detection data show that this hybrid model works well. extract complex patterns, capture temporal dependencies, and identify anomalies makes it well-suited for detecting a wide range of intrusions in real-time. Mixing Cross-correlation language skills with DNN and LSTM’s monitoring network traffic and system activities for suspicious behaviour that could indicate an intrusion.