Anomaly Based Intrusion Detection for IoT with Machine Learning
Addison Shaver, Zhipeng Liu, Niraj Thapa, Kaushik Roy, Balakrishna Gokaraju, Xiaohon Yuan
Abstract
The Internet of Things (IoT) is the network that connects smart devices over the Internet. These devices are increasingly found in every facet of life, providing distributed data computing power and improving the accessibility of everyday routines in many households. However, these connected devices expand and so does the risk that they become valuable targets for malicious threats. This is because, IoT devices have lower power and computation management, meaning that traditional methods of security like encryption or firewalls tend to be unworkable to secure these devices. Therefore, Intrusion Detection Systems (IDSs) provide an alternative for securing IoT devices, by classifying with anomaly detection, whether a network communication is a potential attack. Enhancing existing IDS by integrating various common machine learning models could provide a logical solution to this issue. In this study, we contribute by reviewing and comparing various machine learning (ML) models with intrusion detection. In this comparative analysis, the experimental results from the integrated ML models were promising with an achieved 99% accuracy rates in both binary and multiclass classifiers for intrusion detection.