Effective Attack Detection in Internet of Medical Things Smart Environment Using a Deep Belief Neural Network

Abstract

The Internet of Things (IoT) has lately developed into an innovation for developing smart environments. Security and privacy are viewed as main problems in any technology's dependence on the IoT model. Privacy and security issues arise due to the different possible attacks caused by intruders. Thus, there is an essential need to develop an intrusion detection system for attack and anomaly identification in the IoT system. In this work, we have proposed a deep learning-based method Deep Belief Network (DBN) algorithm model for the intrusion detection system. Regarding the attacks and anomaly detection, the CICIDS 2017 dataset is utilized for the performance analysis of the present IDS model. The proposed method produced better results in all the parameters in relation to accuracy, recall, precision, F1-score, and detection rate. The proposed method has achieved 99.37% accuracy for normal class, 97.93% for Botnet class, 97.71% for Brute Force class, 96.67% for Dos/DDoS class, 96.37% for Infiltration class, 97.71% for Ports can class and 98.37% for Web attack, and these results were compared with various classifiers as shown in the results.

A systematic review of security and privacy issues in the internet of medical things; the role of machine learning approaches

“…In a recent work performed by Manimurugan et al (2020) , Deep Belief Network (DBN) was proposed for attacks detection in the IoMT. The measurement criteria used in the study were precision, recall, accuracy, and F1-score.…”

Peer Review #3 of "A systematic review of security and privacy issues in the internet of medical things; the role of machine learning approaches (v0.1)"

“…Eventually, the classifier holds the reactions in a passive fashion, maintaining each specific record with the disruptive behavior and hence detecting the anomalous events. In a recent work performed by Manimurugan et al (Manimurugan et al 2020), Deep Belief Network (DBN) was proposed for attacks detection in the IoMT. The measurement criteria used in the study were precision, recall, accuracy, and F1-score.…”

Analysis of factors affecting IoT-based smart hospital design

“…With the increase of research in wearable technologies, a new technology called the Internet of Medical Things (IoMT) has emerged [21]. Some applications within this scope are patient monitoring [22], fall detection [23], detection for motion disorder [24], sleep monitoring [21], evaluation of illness degree of a clinical risk level [25], health monitoring [26,27], medical image segmentation [28], attack detection [29], and implantable sensors [30]. Although this application required more expensive hardware but allowed for a more sophisticated service.…”