Sliding Window Based Multilayer Perceptron for Cyber Hacking Detection System (CHDS)

Cyber Hacking Detection System (CHDS) plays a major important role to identify any type of incidents that occur in the system. For instance, a successful CHDS could identify when an invader has compromised a system with the help of the system vulnerability. In addition, many CHDS are capable of monitoring reconnaissance activities, which indicate whether the attack is impending or it is for a particular system or the characteristics of a system that carries specific interests to intruders. The major aim of the work is to design a new CHDS. In this paper, pre-processing SMOTE algorithm and Linear Discriminant Analysis (LDA) by feature selection has been introduced for CHDS. SMOTE preprocessing in Cyber Hacking Detection System (CHDS) can result in a representative and well-balanced training dataset. The LDA method determines a projection vector that decreases the within-class scatter matrix in the feature space while increasing the between-class scatter matrix. For classification, X Gradient Boosting, K Nearest Neighbor (KNN) and Sliding Window based MultiLayer Perceptron (MLP) is used for CHDS. MLP classifier is a set of input-based values to their corresponding outputs. From the results obtained, the proposed Sliding Window based MLP produces Accuracy of 90.70%, Precision of 0.89, Recall of 0.87. The tool used is Jupyter Notebook and the language used is python.