BBFS-LSTM-AE: An Optimized Intrusion Detection Model for Securing IoT Networks

Abstract

Rapid growth in Internet of Things (IoT) devices has expanded the attack surface of modern-day networks. Therefore, it is imperative to deploy an IoT Intrusion Detection System (IDS) for secure communication and reliable operation. However, traditional IDS systems are often inefficient when dealing with high-dimensional data, new attack strategies, and severe imbalanced data problems, leading to low detection performance and high false alarms. In order to solve these problems, this paper introduces a novel intelligent IDS system using the Feature Selection technique inspired by Bowerbird Courtship (BBFS) and Long Short-Term Memory Autoencoder (LSTM-AE) using Seagull Optimizer (SGO). The most important objective here is to build an effective and scalable IDS that can perform efficient feature selection, learn deep temporal dependencies, and tune its hyperparameters to classify malicious and benign traffic more effectively. In this way, we consider all aspects of data preprocessing, feature optimization, balancing, and classification, overcoming the limitations of existing techniques. The evaluation of the CIC IoT 2023 intrusion dataset proves the efficiency of the model, which is shown by high scores: 99.63% of accuracy, 99.55% of detection rate, 99.71% of precision, and 99.59% of F1 score. As seen from the comparison with other models, the BBFS-LSTM-AE-SGO model is better than the compared model in terms of all metrics. It means that the proposed IDS can detect various types of attacks with minimum errors. All through this research has made way for the design of a novel, optimized IoT-IDS model that thereby strengthens cybersecurity resilience, supports real-time monitoring, and hence advances the intrusion detection for IoT-enabled environments.