Network Based Detection of IoT Attack Using AIS-IDS Model

In recent days Internet of Things attained more familiarity. Although it is a promising technology, it tends to lead to a variety of security issues. Conventional methods such as IoT ecosystem based solutions were not suitable to give dilemmas to the system. A new system model called adaptive and intelligent Artificial Immune System (AIS) imitates the process of human being an immune system that consists of eligible properties of this varying environment. Therefore, it enhanced IoT security. Conventionally classifiers such as Random Forest Classifier, Recurrent Neural Network and K-nearest neighbours are used to classify the signals as normal or abnormal and predict malicious attacks. But unfortunately, these classifiers generated a high false alarm rate. Thus, a framework with maximum accuracy and minimum false alarm rate was required. In this work, the AIS model adopts the benefits of the Hopfield Neural Network (HNN) for classification. HNN classifier has a fixed weight, as it cannot be changed for its backpropagation property. This work optimally selects the fixed weight using Fast- Particle Swarm Optimization (F-PSO) and helps to enhance the accuracy of the HNN classifier. This classifier model then differentiates IoT attacks with a high detection rate and normal signal. Three datasets are taken to execute the proposed model and define its accuracy. The proposed Artificial Immune system using HNN for Intrusion Detection System (AIS-IDS) model exhibits 99.8% accuracy for the first dataset and minimum error value. The false alarm rate was minimized using danger theory and its high activation function; thus, the false alarm rate was minimized by up to 8% more than previous classifiers.