Network traffic analysis through deep learning for detection of an army of bots in health IoT network

• Published in: International journal of pervasive computing and communications Vol. 19; no. 5; pp. 653 - 665
• Main Authors: Geetha, K, Brahmananda SH
• Format: Journal Article
• Language: English
• Published: Bingley Emerald Group Publishing Limited 16.11.2023
• Subjects: Algorithms; Army; Artificial intelligence; Classification; Communications traffic; Datasets; Deep learning; Denial of service attacks; Internet of medical things; Internet of Things; Machine learning; Malware; Manufacturers; Medical research; Network analysis; Traffic analysis
• ISSN: 1742-7371; 1742-738X
• DOI: 10.1108/IJPCC-10-2021-0259

PurposeIoT has a wide range of applications in the health-care sector and has captured the interest of many academic and industrial communities. The health IoT devices suffer from botnet attacks as all the devices are connected to the internet. An army of compromised bots may form to launch a DDoS attack, steal confidential data of patients and disrupt the service, and hence detecting this army of bots is paramount. This study aims to detect botnet attacks in health IoT devices using the deep learning technique.Design/methodology/approachThis paper focuses on designing a method to protect health IoT devices from botnet attacks by constantly observing communication network traffic and classifying them as benign and malicious flow. The proposed algorithm analyzes the health IoT network traffic through implementing Bidirectional long-short term memory, a deep learning technique. The IoT-23 data set is considered for this research as it includes diverse botnet attack scenarios.FindingsThe performance of the proposed method is evaluated using attack prediction accuracy. It results in the highest accuracy of 84.8%, classifying benign and malicious traffic.Originality/valueThe proposed method constantly monitors the health IoT network to detect botnet attacks and classifies the traffic as benign or attack. The system is implemented using the BiLSTM algorithm and trained using the IoT-23 data set. The diversity of attack scenarios of the IoT-23 data set demonstrates the proposed algorithm's competence in detecting botnet types in a heterogeneous environment.