Machine Learning Based Intrusion Detection Systems for IoT Applications

By Abhishek Verma, Virender Ranga. Wireless personal communications, 2019

364 citations recorded by the source corpus.

Internet of Things (IoT) and its applications are the most popular research areas at present. The characteristics of IoT on one side make it easily applicable to real-life applications, whereas on the other side expose it to cyber threats. Denial of Service (DoS) is one of the most catastrophic attacks against IoT. In this paper, we investigate the prospects of using machine learning classification algorithms for securing IoT against DoS attacks. A comprehensive study is carried on the classifiers which can advance the development of anomaly-based intrusion detection systems (IDSs). Performance assessment of classifiers is done in terms of prominent metrics and validation methods. Popular datasets CIDDS-001, UNSW-NB15, and NSL-KDD are used for benchmarking classifiers. Friedman and Nemenyi tests are employed to analyze the significant differences among classifiers statistically. In addition, Raspberry Pi is used to evaluate the response time of classifiers on IoT specific hardware. We also discuss a methodology for selecting the best classifier as per application requirements. The main goals of this study are to motivate IoT security researchers for developing IDSs using ensemble learning, and suggesting appropriate methods for statistical assessment of classifier’s performance.

Subfields: Artificial Intelligence, Computer Networks and Communications, Signal Processing

View Paper: https://www.semanticscholar.org/paper/c66558ed58594ef98fddf923ffcc3e2d366155b2

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Mian Dai
2. Shanqing Jiang
3. Yuyang Zhou
4. Guang Cheng

In Computer Networks and Communications

1. Mian Dai
2. Shanqing Jiang
3. Yuyang Zhou
4. Guang Cheng

Citations in the corpus

• Building an efficient intrusion detection system based on feature selection and ensemble classifier (2019, background): “In order to perform the comparison of classifiers in a correct way [88], it is necessary to statistically analyze the significance of the classifiers’ performance.”