| Title | Using Machine Learning for Intrusion Detection System in Wireless Body Area Network |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Alsubaie, Fheed, Al-Akhras, Mousa, Alzahrani, Hamdan A. |
| Conference Name | 2020 First International Conference of Smart Systems and Emerging Technologies (SMARTTECH) |
| Keywords | Artificial neural networks, body area networks, composability, denial of service, Intrusion Detection Systems, intrusion tolerance, machine learning, machine learning algorithms, Noise measurement, pubcrawl, Resiliency, security threats, wireless body area network, Wireless communication, Wireless sensor networks |
| Abstract | This paper introduces a technique that enhances the capabilities of an intrusion detection system (IDS) in a wireless body area network (WBAN). This technique involves adopting two known machine-learning algorithms: artificial neural network (ANN) and the J48 form of decision trees. The enhanced technique reduces the security threats to a WBAN, such as denial-of-service (DoS) attacks. It is essential to manage noise, which might affect the data gathered by the sensors. In this paper, noise in data is measured because it can affect the accuracy of the machine learning algorithms and demonstrate the level of noise at which the machine-learning model can be trusted. The results show that J48 is the best model when there is no noise, with an accuracy reaching 99.66%, as compared to the ANN algorithm. However, with noisy datasets, ANN shows more tolerance to noise. |
| DOI | 10.1109/SMART-TECH49988.2020.00036 |
| Citation Key | alsubaie_using_2020 |
Using Machine Learning for Intrusion Detection System in Wireless Body Area Network