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

• Published in: IEEE access Vol. 8; pp. 77396 - 77404
• Main Authors: Manimurugan, S., Al-Mutairi, Saad, Aborokbah, Majed Mohammed, Chilamkurti, Naveen, Ganesan, Subramaniam, Patan, Rizwan
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Anomalies; Anomaly detection; Belief networks; Cybersecurity; Data models; DBN; deep learning; Internet of medical things; Internet of Things; Intrusion detection; Intrusion detection systems; IoT; Machine learning; Mathematical model; Neural networks; Privacy; Training
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.2986013

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.