An AI-Driven Hybrid Framework for Intrusion Detection in IoT-Enabled E-Health. (21st August 2022)

• Record Type: Journal Article
• Authors: Wahab, Fazal; Zhao, Yuhai; Javeed, Danish; Al-Adhaileh, Mosleh Hmoud; Almaaytah, Shahab Ahmad; Khan, Wasiat; Saeed, Muhammad Shahid; Kumar Shah, Rajeev
• Other Names: Koundal Deepika Academic Editor.
• Is Part Of: Computational intelligence and neuroscience. Volume 2022(2022)
• Publication Date: 2022-08-21
• Subjects: Neurosciences -- Data processing -- Periodicals; Computational intelligence -- Periodicals; Computational neuroscience -- Periodicals; 612.80285
• DOI: 10.1155/2022/6096289
• Languages: English
• ISSNs: 1687-5265

Abstract

Abstract : E-health has grown into a billion-dollar industry in the last decade. Its device's high throughput makes it an obvious target for cyberattacks, and these environments desperately need protection. In this scientific study, we presented an artificial intelligence (AI)-driven software-defined networking (SDN)-enabled intrusion detection system (IDS) to address increasing cyber threats in the E-health and internet of medical things (IoMT) environments. AI's success in various fields, including big data and intrusion detection systems, has prompted us to develop a flexible and cost-effective approach to protect such critical environments from cyberattacks. We present a hybrid model consisting of long short-term memory (LSTM) and gated recurrent unit (GRU). The proposed model was thoroughly evaluated using the publicly available CICDDoS2019 dataset and conventional evaluation measures. Furthermore, for proper validation, the proposed framework is compared with relevant classifiers, such as cu-GRU+ DNN and cu-BLSTM. We have further compared the proposed model with existing literature to prove its efficacy. Lastly, 10-fold cross-validation is also used to verify that our results are unbiased. The proposed approach has bypassed the current literature with extraordinary performance ramifications such as 99.01% accuracy, 99.04% precision, 98.80 percent recall, and 99.12% F1-score.