Intrusion Detection System in Smart Home Network Using Artificial Immune System and Extreme Learning Machine Hybrid Approach

Alalade, Emmanuel Dare

Internet of Things (IoT) enabled smart homes to have made our daily lives easier, but these conveniences have also introduced security concerns. IoT devices hold security risks as well as smart home hubs and gateways. Gateways present a centralized point of communication among devices that can create a backdoor into network data for hackers but also present a detection opportunity. Intrusion detection is a common way to detect anomalies in network traffic. This paper introduces early work on an intrusion detection system (IDS) by detecting anomalies in the smart home network using Extreme Learning Machine and Artificial Immune System (AIS ELM). AIS uses the clonal Algorithm for the optimization of the input parameters, and ELM analyzes the input parameter for better convergence in detecting anomalous activity. The larger goal of this work is to apply this approach to a smart home network gateway and combined it with a push notification system that will allow the homeowner to identify any abnormalities in the smart home network and take appropriate action.

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