Improvement of Anomaly Detection System in the IoT Networks using CNN-LSTM Approach

Abstract

In the last few years, there has been a massive increase in Internet of Things (IoT) devices and the data generated from these appliances. Devices involved in IoT networks can be challenging because of their resource-constrained nature, and security integration's on these devices are frequently disregarded. This results in attackers targeting more IoT devices. Thus, as the number of possible attacks on a network increases, it becomes more difficult for traditional intrusion detection systems (IDS) to deal with these attacks effectively. This paper presents a hybrid deep learning-based approach, a one- dimensional convolutional neural network, and long short-term memory (1D CNN-LSTM) algorithm, for anomaly detection that harnesses the power of the IoT, providing qualities to efficiently examine all traffic across the IoT. The comprehensive study was conducted utilizing the Bot-IoT dataset extracted from real network traffic, consisting of benign and malicious variants. Then, the anomaly detection including binary and multi-decision categories has been performed. The experimental results highlighted the superiority of the proposed model with an accuracy of 99.20% and lower false alarm with 0.80% compared to single CNN-based IDS.