Artificial Intelligence based Network Intrusion Detection with hyper-parameter optimization tuning on the realistic cyber dataset CSE-CIC-IDS2018 using cloud computing

Abstract

One of the latest emerging technologies is artificial intelligence, which makes the machine mimic human behaviour. The most important component used to detect cyber attacks or malicious activities is the intrusion detection system (IDS). Artificial intelligence plays a vital role in detecting intrusions and widely considered as the better way in adapting and building IDS. In modern days, neural network algorithms are emerging as a new artificial intelligence technique that can be applied to real-time problems. The proposed system is to detect a classification of botnet attack which poses a serious threat to financial sectors and banking services. The proposed system is created by applying artificial intelligence on a realistic cyber defence dataset (CSE-CIC-IDS2018), the latest IDS Dataset in 2018 by Canadian Institute for Cybersecurity (CIC) on AWS (Amazon Web Services).The proposed system of Artificial Neural Networks provides an outstanding performance of Accuracy score 99.97%, an average area under ROC(Receiver Operator Characteristic) curve 0.999 and the average False Positive rate is a mere value of 0.03. The proposed system of Artificial Intelligence-based Intrusion detection of botnet attack classification is powerful, more accurate and precise. The novel proposed system can be applied to conventional network traffic analysis, cyber–physical system traffic analysis and also can be applied to the real-time network traffic data analysis. Keywords: Artificial intelligence, AWS, CSE-CIC-IDS2018, Hyper-parameter optimization, Realistic network traffic cyber dataset