Deep learning approach for intrusion detection in IoT-multi cloud environment

• Published in: Automated software engineering Vol. 28; no. 2; p. 19
• Main Authors: Selvapandian, D., Santhosh, R.
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.11.2021; Springer Nature B.V
• Subjects: Accuracy; Artificial Intelligence; Complexity; Computer Science; Cybersecurity; Deep learning; Internet of Things; Intrusion detection systems; Machine learning; Neural networks; Software Engineering/Programming and Operating Systems; Special Issue on Intelligent Software-Driven Autonomous Systems; System effectiveness; Wireless networks; Detection accuracy; Internet of Things (IoT); Intrusion detection system (IDS); Deep learning technique; Multi-cloud computing
• ISSN: 0928-8910; 1573-7535
• DOI: 10.1007/s10515-021-00298-7

The possibility of connecting billions of smart end devices in the Internet of Things (IoT) provides wide range of services to the user. But, the unlimited connectivity of devices in IoT brings security issues when it is connected to wireless networks. Integrating cloud with IoT networks gains more attention as it reduces the sensor node resource limitations. However, the network complexity, open broadcast characteristics of IoT networks are vulnerable to attacks. To ensure network security and reliable operations, Intrusion Detection Systems (IDS) are widely preferred. IDS identifies the anomalies effectively in complex network environments and ensures the security of the network. Traditional intrusion detection systems based on neural networks consume long training time and low classification accuracy. Recently, deep learning methods are widely used in various image and signal processing, security applications. This research work presents a deep learning-based intrusion detection system for multi-cloud IoT environment to overcome the limitations of neural network-based intrusion detection models. The proposed intrusion detection model improves the detection accuracy by improving the training efficiency. Experimental evaluation of proposed model using NSL-KDD dataset provides improved performance than conventional techniques attaining 97.51% of detection rate, 96.28% of detection accuracy, and 94.41% of precision.