A hybrid attack detection strategy for cybersecurity using moth elephant herding optimisation‐based stacked autoencoder

Abstract

Abstract Cybersecurity is a major concern in the network resources of the Internet of Things (IoT) environment, as the attacks on the IoT devices degrade the performance of the computational operations. Various attack detection methods are adopted in the research area to prevent an illegal user from accessing the resources. To resolve the vulnerabilities in the computing devices, an effective attack detection method, named, Moth Elephant Herding Optimisation (MEHO)‐based stacked autoencoder approach is proposed in this research. Initially, the input data is passed into the pre‐processing stage, where the data is cleaned by removing the noise and artefacts . The pre‐processed data is further subjected to the feature selection stage, where the Class‐Wise Information Gain technique (CIG) is used to select the essential features. The class‐aware features, like traffic‐based features, content‐based features and basic features are selected efficiently. Finally, the attack detection is performed using the stacked autoencoder classifier, which is trained using the proposed MEHO algorithm. The MEHO algorithm is developed by integrating the Moth search (MS) algorithm and the Elephant Herding Optimisation (EHO). The performance is evaluated using the metrics, like accuracy, False Acceptance Rate (FAR) and detection rate, acquired with the values of 0.9286, 0.0636 and 0.9258 respectively.

Keywords

• computer crime
• learning (artificial intelligence)
• neural nets
• optimisation
• pattern classification
• search problems