DLIIoT: A Deep Learning based Intelligent Attack Detection in IoT Networks using Cooja Simulator

Authors

  • H. Sharma Department of Computer Science and Information Technology, University of Jammu, India
  • J. Manhas Department of Computer Science and Information Technology, Bhaderwah Campus, University of Jammu, India
  • V. Sharma Department of Computer Science and Information Technology, University of Jammu, India

DOI:

https://doi.org/10.3329/jsr.v17i1.74612

Abstract

Internet of things (IoT) has incredibly transformed the whole domain of communication process. The extensive dependency on these devices leads to various advanced cyber security threats. IoT devices fall easily into the ambit of malicious threats and are susceptible to vast range of attacks due to their limited computation capabilities and memory constraints. Intrusion Detection Systems (IDSs) are dedicated outstanding frameworks to protect these devices from cyber threats. In this study, a comprehensive review of different AI based IDS applied on IoTs is done. It has been observed that machine learning and deep learning has widely influenced the domain of IoT security. The focus of the research carried out is to earmark the techniques that are performing best on a given data set. Features selection, type of attacks, proposed solutions in solving security menaces are taken into consideration. Further, we have presented DLIIoT, a deep learning based intelligent attack detection in IoT networks by generating precise IoT datasets in Cooja Simulator. Four Deep learning algorithms are utilized and analysed under standard performance criteria metrics such as Precision, Recall, Accuracy and F1-score. It was found that deep learning algorithms have remarkable potential in detecting and recognizing malicious data patterns in IoT networks.

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Published

2025-01-01

How to Cite

Sharma, H., Manhas, J., & Sharma, V. (2025). DLIIoT: A Deep Learning based Intelligent Attack Detection in IoT Networks using Cooja Simulator. Journal of Scientific Research, 17(1), 177–193. https://doi.org/10.3329/jsr.v17i1.74612

Issue

Section

Section A: Physical and Mathematical Sciences