Intrusion Detection Using Bat Optimization Algorithm and DenseNet for IoT and Cloud Based Systems

Abstract

In Internet of Things (IoT) and cloud systems, Intrusion Detection (ID) is very vital for protecting the security infrastructures. ID techniques are extensively used to detect and track malicious threats in cloud and IoT systems. In the IoT based ID, the conventional techniques work based on the manual traffic feature values that increase the complexity of the networks and achieve a limited detection rate on the larger IoT databases. For addressing the above-stated issues and achieving high classification results, an effective deep learning based ID-System (IDS) is implemented in this article. Initially, the IoT data is acquired from the NSW-NB15 and NSL-KDD databases, and then, the standard scaling normalization technique, known as Min-Max normalization, is applied to select the dominant attributes and to eliminate outliers from the acquired databases. Additionally, the optimal features are selected from the rescaled normalized data by implementing the Bat optimization algorithm. The selection of optimal features decreases the computational complexity and training time of the IDS. The chosen optimal features are passed into the DenseNet model for carrying out intrusion attack detection. Particularly, in the binary-class classification, the Bat-based DenseNet model obtained 98.89% and 98.40% of accuracy on the UNSW-NB15 and NSL-KDD databases, correspondingly. The obtained simulation results prove the higher effectiveness of the current study when it is related to the state-of-the-art classifiers.