Analysis of PowerSHAP Feature Selection Method on APT Detection in Network Traffic Dataset

Abstract

Novel feature selection methods are emerging to improve the accuracy of machine learning classifiers, including the method PowerSHAP (PS). This work analyzes the impact of PS to enhance the accuracy of Advanced Persistent Threat (APT) prediction in network traffic data. The dataset used in the experiments is DAPT2020, a labeled collection of network traffic data spanning Monday to Friday. The experimental data focuses on Wednesday’s traffic, which contains the majority of APT attack classes, such as Directory Bruteforce, Malware Download, Account Discovery, SQL Injection, CSRF, and the Normal class. Three experiments were conducted to assess the impact of feature selection with PowerSHAP in comparison to the standard data mining process.