Smart City Gnosys

Smart city article details

Title	Automatic Feature Extraction And Selection For Machine Learning Based Intrusion Detection
ID_Doc	11324
Authors	Liu J.; Chung S.S.
Year	2019
Published	Proceedings - 2019 IEEE SmartWorld, Ubiquitous Intelligence and Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Internet of People and Smart City Innovation, SmartWorld/UIC/ATC/SCALCOM/IOP/SCI 2019
DOI	http://dx.doi.org/10.1109/SmartWorld-UIC-ATC-SCALCOM-IOP-SCI.2019.00254
Abstract	As the advances in mobile technologies and IoT-enabled devices have been integrated into our daily lives, significant increases in wireless network traffic generate a large scale of high dimensional network log data. This has led to challenges in security of Wi-Fi network systems that have to analyze such complex big data for intrusion detection. Many Wi-Fi network systems commonly employee machine learning based Intrusion Detection Systems (IDS). Such IDS usually adopt supervised methods that heavily depend on observations of human experts for feature extraction, feature selection, and labeling processes of training data for classification. In this study, using the recently collected Aegean Wi-Fi Intrusion Dataset (AWID) which contains real traces of different network attacks types, we propose an unsupervised approach with automatic feature extraction and selection process to replace human intervention and manual labeling process for analyzing a large scale high dimensional data to improve the prediction accuracy of classification to detect 3 most common network attack types - Injection, Flooding, and Impersonate attacks in an IDS with a large scale of high dimensional data. The experiment results showed the effectiveness of our approach for feature extraction and selection. The quality of the selected features and the accuracy of intrusion detection of the three attack types are compared and analyzed. © 2019 IEEE.
Author Keywords	Classification; Feature extraction; Feature selection; Intrusion detection system; Machine learning; Network security

Similar Articles

Id	Similarity	Authors	Title	Published
36055	0.91	Ngo V.-D.; Vuong T.-C.; Van Luong T.; Tran H.	Machine Learning-Based Intrusion Detection: Feature Selection Versus Feature Extraction	Cluster Computing, 27, 3 (2024)
831	0.899	Vuong T.-C.; Tran H.; Trang M.X.; Ngo V.-D.; Luong T.V.	A Comparison Of Feature Selection And Feature Extraction In Network Intrusion Detection Systems	Proceedings of 2022 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2022 (2022)
38994	0.886	Ali A.; Shaukat S.; Tayyab M.; Khan M.A.; Khan J.S.; Arshad; Ahmad J.	Network Intrusion Detection Leveraging Machine Learning And Feature Selection	HONET 2020 - IEEE 17th International Conference on Smart Communities: Improving Quality of Life using ICT, IoT and AI (2020)
3003	0.879	Gopalakrishnan B.; Purusothaman P.	A New Design Of Intrusion Detection In Iot Sector Using Optimal Feature Selection And High Ranking-Based Ensemble Learning Model	Peer-to-Peer Networking and Applications, 15, 5 (2022)
23837	0.873	Almotairi A.; Atawneh S.; Khashan O.A.; Khafajah N.M.	Enhancing Intrusion Detection In Iot Networks Using Machine Learning-Based Feature Selection And Ensemble Models	Systems Science and Control Engineering, 12, 1 (2024)
59592	0.868	Khatkar M.; Kumar K.; Kumar B.	Unfolding The Network Dataset To Understand The Contribution Of Features For Detecting Malicious Activities Using Ai/Ml	Materials Today: Proceedings, 59 (2022)
2483	0.867	Nepolo E.; Ngxande M.; Zodi G.-A.L.	A Machine Learning-Based Performance Analysis Of Feature Selection Methods For Anomaly Detection For Iot Network Security	Learning and Analytics in Intelligent Systems, 43 (2025)
22935	0.867	Merlin R.T.; Ravi R.	Empowering Smart City Iot Network Intrusion Detection With Advanced Ensemble Learning-Based Feature Selection	International Journal of Electrical and Electronics Research, 12, 2 (2024)
5365	0.867	Natarajan B.; Bose S.; Maheswaran N.; Logeswari G.; Anitha T.	A Survey: An Effective Utilization Of Machine Learning Algorithms In Iot Based Intrusion Detection System	12th IEEE International Conference on Advanced Computing, ICoAC 2023 (2023)
31583	0.865	Gaber T.; El-Ghamry A.; Hassanien A.E.	Injection Attack Detection Using Machine Learning For Smart Iot Applications	Physical Communication, 52 (2022)