AI-DRIVEN THREAT DETECTION USING DATA SCIENCE: A COMPARATIVE STUDY OF MACHINE LEARNING MODELS ON CYBERSECURITY DATASETS
TL;DR
An AI threat detection framework is proposed, employing data science methods to boost cybersecurity performances and shows that the performance of ensemble learning models is higher than shallow learning models in this research; XGBoost and Random Forest.
Abstract
They originate from the rapid rise of cyber threats such as malware, phishing, ransomware, denial of service, and unauthorised network intrusion, which have proven to be so difficult to tackle that traditional security measures can hardly deal with the issue. Signature-based intrusion detection system techniques in particular, which are commonly adopted by traditional methods, usually lack the ability to detect novel and evolving attack vectors in addition to high false positive rate and response time. In this regard, this paper proposes an AI threat detection framework, employing data science methods to boost cybersecurity performances. The researchers of this paper have tested the effectiveness of several models using a benchmark dataset for cyber security, including CICIDS2017 or NSL-KDD and machine learning techniques such as Random Forest, Support Vector Machine, Logistic Regression and XGBoost for evaluating performance. Using measures of accuracy, precision, recall and F1- score, the experiments show that the performance of ensemble learning models is higher than shallow learning models in this research; XGBoost and Random Forest.