Open AccessT‐SNERF: A novel high accuracy machine learning approach for Intrusion Detection Systems
Author(s) -
Hammad Mohamed,
Hewahi Nabil,
Elmedany Wael
Publication year - 2021
Publication title -
iet information security
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.308
H-Index - 34
eISSN - 1751-8717
pISSN - 1751-8709
DOI - 10.1049/ise2.12020
Subject(s) - random forest , computer science , intrusion detection system , embedding , data mining , phishing , artificial intelligence , false positive rate , anomaly based intrusion detection system , machine learning , feature (linguistics) , anomaly detection , pattern recognition (psychology) , the internet , linguistics , philosophy , world wide web
In the last few decades, Intrusion Detection System (IDS), in particular, machine learning‐based anomaly detection, has gained importance over Signature Detection Systems (SDSs) in the novel attacks detection. Herein, a novel approach called T‐Distributed Stochastic Neighbour Embedding and Random Forest Algorithm (T‐SNERF) is presented for the classification of cyber‐attacks. The approach consists of three different steps. First, the examination of feature correlations is provided. Second, the T‐Distributed Stochastic Neighbour Embedding (T‐SNE) data dimensional reduction technique is used. Third, Random Forest (RF) technique is utilised to evaluate the complications in the accuracy and False‐Positive Rate (FPR). The proposed approach has been tested on various well‐known datasets, namely, UNSW‐NB 15, CICIDS‐2017, and phishing datasets. The proposed novel approach achieved significant results compared with existing approaches, achieving 100% accuracy, and 0% FPR for the UNSW‐NB15 dataset, and achieving high accuracy rates, up to 99.7878%, and 99.7044%, for CICIDS‐2017 and Phishing datasets respectively.