Visible to the public Artificial Intelligence based Network Intrusion Detection with Hyper-Parameter Optimization Tuning on the Realistic Cyber Dataset CSE-CIC-IDS2018 using Cloud Computing

TitleArtificial Intelligence based Network Intrusion Detection with Hyper-Parameter Optimization Tuning on the Realistic Cyber Dataset CSE-CIC-IDS2018 using Cloud Computing
Publication TypeConference Paper
Year of Publication2019
AuthorsKanimozhi, V., Jacob, T. Prem
Conference Name2019 International Conference on Communication and Signal Processing (ICCSP)
KeywordsAmazon Web Services, area under ROC curve, artificial intelligence, artificial intelligence algorithms, artificial intelligence based network intrusion detection, artificial intelligence technique, Artificial neural networks, AWS, Botnet, botnet attack detection, Canadian institute for cybersecurity, CIC, cloud computing, composability, computer network security, CSE-CIC-IDS2018, cyber-physical system traffic data, hyper-parameter optimization and realistic network traffic cyber dataset, hyper-parameter optimization tuning, Intrusion detection, intrusion detection system, latest intrusion detection dataset, Metrics, network intrusion detection, neural nets, neural network algorithms, Optimization, pubcrawl, realistic cyber dataset CSE-CIC-IDS2018, realistic cyber defense dataset, receiver operator characteristic, Resiliency, telecommunication traffic, Tuning, web services
AbstractOne of the latest emerging technologies is artificial intelligence, which makes the machine mimic human behavior. 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 trendy days, artificial intelligence algorithms are rising as a brand new computing technique which will be applied to actual time issues. 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 defense dataset (CSE-CIC-IDS2018), the very latest Intrusion Detection Dataset created 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 is 99.97% and an average area under ROC (Receiver Operator Characteristic) curve is 0.999 and an average False Positive rate is a mere value of 0.001. The proposed system using artificial intelligence of botnet attack detection is powerful, more accurate and precise. The novel proposed system can be implemented in n machines to conventional network traffic analysis, cyber-physical system traffic data and also to the real-time network traffic analysis.
DOI10.1109/ICCSP.2019.8698029
Citation Keykanimozhi_artificial_2019