An enhanced optimization based algorithm for intrusion detection in SCADA network

Supervisory Control and Data Acquisition (SCADA) systems are widely used in many applications including power transmission and distribution for situational awareness and control. Identifying and detecting intrusions in a SCADA is a critical and demanding task in recent days. For this purpose, various Intrusion Detection Systems (IDSs) are developed in the existing works. But, it has some drawbacks including it has high false positive and false negative rates, it cannot detect the encrypted date and it supports only for detecting the external intrusions. In order to overcome all these issues, an Intrusion Weighted Particle based Cuckoo Search Optimization (IWP-CSO) and Hierarchical Neuron Architecture based Neural Network (HNA-NN) techniques are proposed in this paper. The main intention of this paper is to detect and classify the intrusions in a SCADA network based on the optimization. At first, the input network dataset is given as the input, where the attributes are arranged and the clusters are initialized. Then, the features are optimized to select the best attributes by using the proposed IWP-CSO algorithm. Finally, the intrusions in a network are classified by employing the proposed HNA-AA algorithm. The experimental results evaluate the performance of the proposed system in terms of sensitivity, specificity, precision, recall, accuracy, Jaccard, Dice and false detection rate.