Unleashing the power of Bat optimized CNN-BiLSTM model for advanced network anomaly detection: Enhancing security and performance in IoT environments

Abstract

The growth of IoT (Internet of Things) devices has revolutionized several industries and brought about novel security threats. Recognizing network anomalies that may point to malicious activity or system flaws is a major issue. Traditional anomalous identification methods frequently need to catch up when dealing with the special traits of IoT environments, including resource limitations and changing network behavior. This paper introduces an innovative approach, the Bat-optimized CNN-BiLSTM model, to enhance the security and efficiency of IoT environments. This model combines the strengths of Convolutional Neural Networks (CNNs) for spatial analysis and Bidirectional Long Short-Term Memory (BiLSTM) networks for capturing temporal patterns, thus effectively representing time and space trends in IoT data. To optimize its performance further, researchers have leveraged the Bat algorithm, inspired by natural behaviors, to fine-tune the model. This program effectively searches for the best network anomaly detection parameters by imitating the echo activity of bats. Researchers want to increase detection accuracy by lowering false positives and false negatives using the Bat algorithm to enhance the CNN-BiLSTM model. The experimental findings show that the Bat-optimised CNN-BiLSTM model beats the state-of-the-art anomaly detection methods with 99.43% accuracy and efficiency.

Keywords

• Internet of Things (IoT)
• Bidirectional Long Short-Term Memory (BiLSTM)
• Convolutional Neural Network (CNN)
• Particle Swarm Optimisation (PSO)
• Anomaly Detection