Feature Selection and Classification for Anomaly Detection in IoT Networks using Multi-layer Perceptron - Chaotic Ant Lion Optimization (MLP-CALO) Algorithm

As the number of devices in Internet of Things (IoT) increases exponentially, the risks of unknown vulnerabilities and threats also increases resulting in degradation of performance. Traditional anomaly detection systems are ineffective within IoT ecosystems, since the range of possible normal behaviours of devices is significantly larger and more dynamic. The main objective of this work is to develop a machine learning (ML) algorithm to classify the selected features of IoT traffic and detect the malicious behaviour of users and unauthorised devices dynamically. In this paper feature Selection and classification for anomaly detection in IoT using Multi Layer Perception (MLP) and Chaotic Ant Lion Optimization (CALO) algorithm is proposed. In this algorithm, Filtering method using Fisher’ score and correlation coefficient is applied to select the candidate feature set. Then hybrid MLP-CALO algorithm is proposed to classify the selected features and detects the anomalies from the IoT traffic. The objective function of the CALO algorithm minimizes the average MSE of MLP output. Experimental results have shown that MLP-CALO has higher classification accuracy and lesser computation cost when compared to traditional ALO and ANN algorithms