Hybrid optimised deep learning-deep belief network for attack detection in the internet of things

Abstract

ABSTRACT Internet of Things (IoT) is a new revolution of the Internet. However, the IoT network of physical devices and objects is often vulnerable to attacks like Denial of Service (DoS) and Distributed Denial of Service (DDoS). The proposed attack detection system makes the interlinking of Development and Operations (DevOps) as it makes the relationship between development and IT operations. For this, the proposed system includes (i) Proposed Feature Extraction and (ii) Classification. The data from each application are processed under the initial stage of feature extraction, where the statistical and higher-order statistical features are concatenated. Subsequently, the extracted features are subjected to a classification process, where it determines the presence of attacks. For the classification process, this paper intends to deploy the optimised Deep Belief Network (DBN), in which the activation function is optimally tuned. A new hybrid algorithm termed Firefly Alpha Evaluated Grey Wolf Optimisation (FAE-GWO) algorithm is proposed, which is the combination of Firefly (FF) and Grey Wolf Optimisation (GWO). Finally, the performance of the proposed system model is compared over other conventional works in terms of certain performance measures.