Anomaly Detection on IoT Network Intrusion Using Machine Learning

Zhipeng Liu, Niraj Thapa, Addison Shaver, Kaushik Roy, Xiaohong Yuan, Sajad Khorsandroo

202081 citationsDOI

Abstract

Enhancing the security of IoT networks is trending as one of the most crucial issues the information technology community faces. With large scales of IoT devices being developed and deployed, the ability for these devices to communicate securely without compromising performance is challenging. The challenges exist because most of IoT devices are limited by power hence constrained to less computational ability. Subsequently, encryption and authentication are difficult to be applied to fence off malicious cyber-attacks. Intrusion Detection System (IDS) logically becomes the forefront security solution. Anomaly-based network intrusion detection plays a major role in safeguarding networks against different malicious activities. In this paper, we apply different machine learning algorithms to efficiently detect anomalies on the IoT Network Intrusion Dataset. The results show promise as we were able to achieve 99%-100% accuracy while having high efficiency.

Topics & Concepts

Computer scienceIntrusion detection systemInternet of ThingsAnomaly detectionEncryptionComputer securityAuthentication (law)Anomaly-based intrusion detection systemSafeguardingComputer networkArtificial intelligenceNursingMedicineNetwork Security and Intrusion DetectionInternet Traffic Analysis and Secure E-votingAdvanced Malware Detection Techniques