Ranjith Jayaramu
School of Computer Science & Engineering, Vellore Institute of Technology, Tamil Nadu, Chennai, India
Vergin Sarobin
School of Computer Science & Engineering, Vellore Institute of Technology, Tamil Nadu, Chennai, India
Shree Varshan
School of Computer Science & Engineering, Vellore Institute of Technology, Tamil Nadu, Chennai, India
Briju Demel
School of Computer Science & Engineering, Vellore Institute of Technology, Tamil Nadu, Chennai, India
Hridhay Vardhan
School of Computer Science & Engineering, Vellore Institute of Technology, Tamil Nadu, Chennai, India
Keywords: Anomaly detection, Classification model, IoT-23, Machine learning, Security
Abstract
In this study, we analyze the effectiveness of combining various machine learning approaches in order to detect specific attack classes within the Internet of Things (IoT). Using the IoT23 dataset, the research examines distinct features associated with attack classes derived from the raw data. Multiple algorithms, such as Multi-layer Perceptron, Random Forest, Extreme Gradient Boosting (XGBoost), Decision Tree, K-Nearest Neighbor, Support Vector Machine, Logistic Regression, and Naive Bayes, were thoroughly trained and evaluated. Through the implementation of ensemble learning techniques, the study successfully achieved an elevated detection rate of attack classes and an overall accuracy improvement, maintaining a false alarm rate of up to 15%. The research highlights the importance of using ensemble learning methods to identify and categorize attacks in IoT domains, serving as a valuable resource for further research. The insights revealed in this study provide readers with a compelling reason to read it and should act as a catalyst for further research in similar directions.
References
Shafique K, Khawaja BA, Sabir F, Qazi S, Mustaqim M. Internet of things (IoT) for next-generation smart systems: A review of current challenges, future trends and prospects for emerging 5G-IoT scenarios. IEEE Access. 2020;8:23022-23040.
Dwivedi YK, Hughes L, Baabdullah AM, Ribeiro-Navarrete S, Giannakis M, Al-Debei MM, Dennehy D, Metri B, Buhalis D, Cheung CM, Conboy K. Metaverse beyond the hype: Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. Int J Inf Manage. 2022;66:102542.
Xiao L, Wan X, Lu X, Zhang Y, Wu D. IoT security techniques based on machine learning: How do IoT devices use AI to enhance security. IEEE Signal Process Mag. 2018;35(5):41-49.
Ullah I, Mahmoud QH. Design and development of RNN anomaly detection model for IoT networks. IEEE Access. 2022;10:62722-62750.
Mukhaini GA, Anbar M, Manickam S, Al-Amiedy TA, Al Momani A. A systematic literature review of recent lightweight detection approaches leveraging machine and deep learning mechanisms in Internet of Things networks. J King Saud Univ Comput Inf Sci. 2023:101866.
Rathore S, Park JH. Semi-supervised learning based distributed attack detection framework for IoT. Appl Soft Comput. 2018;72:79-89.
Liu L, Ma Z, Meng W. Detection of multiple-mix-attack malicious nodes using perceptron-based trust in IoT networks. Future Gener Comput Syst. 2019;101:865-879.
Miettinen M, Marchal S, Hafeez I, Asokan N, Sadeghi AR, Tarkoma S. Iot sentinel: Automated device-type identification for security enforcement in iot. IEEE 37th International Conference on Distributed Computing Systems (ICDCS). 2017;5: 2177-2184.
Siby S, Maiti RR, Tippenhauer NO. IoT Scanner: Detecting privacy threats in IoT neighborhoods. In poceedings of the 3rd ACM international workshop on IoT privacy, trust, and security. 2017;2:23-30.
Kawai H, Ata S, Nakamura N, Oka I. Identification of communication devices from analysis of traffic patterns. 13th International Conference on Network and Service Management (CNSM). 2017;26:1-5.
Sarker IH, Khan AI, Abushark YB, Alsolami F. Internet of things security intelligence: a comprehensive overview, machine learning solutions and research directions. Mob Netw Appl. 2023;28(1):296-312.
Liao LX, Chao HC, Chen MY. Intelligently modeling, detecting, and scheduling elephant flows in software defined energy cloud: A survey. J Parallel Distrib Comput. 2020;146:64-78.
Boutaba R, Salahuddin MA, Limam N, Ayoubi S, Shahriar N, Estrada-Solano F, Caicedo OM. A comprehensive survey on machine learning for networking: evolution, applications and research opportunities. J Internet Serv Appl. 2018;9(1):1-99.
Dutta V, Choraś M, Pawlicki M, Kozik R. A deep learning ensemble for network anomaly and cyber-attack detection. Sensors. 2020;20(16):4583.
Thanh HN, Van Lang T. Use the ensemble methods when detecting DoS attacks in network intrusion detection systems. EAI Endorsed Trans Context-aware Syst Appl. 2019;6(19):e163484.
Chary SN, Rama B. A survey on comparative analysis of decision tree algorithms in data mining. Int J Adv Sci Technol Eng Manag Sci. 2017;3(1):91-95.
Sahani R, Shatabdinalini, Rout C, Chandrakanta Badajena J, Jena AK, Das H. Classification of intrusion detection using data mining techniques. Prog Comput Anal Netw Proc ICCAN. 2017;18:753-764.
Xin Y, Kong L, Liu Z, Chen Y, Li Y, Zhu H, Gao M, Hou H, Wang C. Machine learning and deep learning methods for cybersecurity. IEEE Access. 2018;6:35365-35381.
Ahmad Z, Shahid Khan A, Wai Shiang C, Abdullah J, Ahmad F. Network intrusion detection system: A systematic study of machine learning and deep learning approaches. Trans Emerg Telecommun Technol. 2021;32(1):e4150.
Yang K, Kpotufe S, Feamster N. An efficient one-class SVM for anomaly detection in the internet of things. arXiv. 2104;1:11146.
Hasan M, Islam MM, Zarif MI, Hashem MM. Attack and anomaly detection in IoT sensors in IoT sites using machine learning approaches. Internet Things. 2019;7:100059.
Vangipuram R, Gunupudi RK, Puligadda VK, Vinjamuri J. A machine learning approach for imputation and anomaly detection in IoT environment. Expert Syst. 2020;37(5):e12556.
Thakkar A, Lohiya R. A survey on intrusion detection system: feature selection, model, performance measures, application perspective, challenges, and future research directions. Artif Intell Rev. 2022;55(1):453-563.
Yang K, Kpotufe S, Feamster N. An efficient one-class SVM for anomaly detection in the internet of things. arXiv. 2104;1:11146.
Chatterjee A, Ahmed BS. IoT anomaly detection methods and applications: A survey. Internet Things. 2022;19:100568.
Abusitta A, de Carvalho GH, Wahab OA, Halabi T, Fung BC, Al Mamoori S. Deep learning-enabled anomaly detection for IoT systems. Internet Things. 2023;21:100656.
Zhang H, Xie R, Li K, Huang W, Yang C, Liu J. Anomaly detection based on deep learning: insights and opportunities. IEEE 10th international conference on cyber security and cloud computing (CSCloud). IEEE 9th international conference on edge computing and scalable cloud (EdgeCom). 2023;1:30-36.
Alwahedi F, Aldhaheri A, Ferrag MA, Battah A, Tihanyi N. Machine learning techniques for IoT security: Current research and future vision with generative AI and large language models. Internet Things Cyber Phys Syst. 2024;3:1-5.
Wang X, Lu X. A host-based anomaly detection framework using XGBoost and LSTM for IoT devices. Wirel Commun Mob Comput. 2020;20:1-3.
Taghavirashidizadeh A, Zavvar M, Moghadaspour M, Jafari M, Garoosi H, Zavvar MH. Anomaly Detection In IoT Networks Using Hybrid Method Based On PCA-XGBoost. 8th iranian conference on signal processing and intelligent systems (ICSPIS). 2022;28:1-5.
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