Intrusion Detection System via CNN-Ghostnet for IoT-Based Smart Cities

R. Surendiran

doi:https://doi.org/10.59232/DSM-V1I1P103

Research Article | Open Access | Download Full Text

Volume 1 | Issue 1 | Year 2024 | Article Id: DSM-V1I1P103 DOI: https://doi.org/10.59232/DSM-V1I1P103

Intrusion Detection System via CNN-Ghostnet for IoT-Based Smart Cities

R. Surendiran

Received	Revised	Accepted	Published
19 Oct 2023	19 Nov 2023	11 Dec 2023	22 Jan 2024

Citation

R. Surendiran. “Intrusion Detection System via CNN-Ghostnet for IoT-Based Smart Cities.” DS Journal of Multidisciplinary, vol. 1, no. 1, pp. 16-22, 2024.

Abstract

Cyber security plays a vital role in securing the data as well as the system. Poor security and high attack possibilities are present in IoT. So, an accurate Intrusion Detection System is needed to develop with high detection accuracy. In the existing methods, poor accuracy detection is termed as the major disadvantage. So, to develop the security rate and the detection accuracy, a novel Convolution Neural Network-based DEEP-Intrusion Detection System (IDS) has been developed. The main motive of this research is to improve attack detection accuracy. UNSW-BN15 and BoTIoT datasets are used to detect the attack. Moreover, the features present in the data set are extracted through the Convolution Neural Network. The layers present in the convolution networks are responsible for the feature extraction process. Then, the performance of the proposed model can be validated in different parameters such as detection accuracy, recall, precision, and F1 score. The performance score of the proposed model is then compared with the existing models. Finally, the proposed model has achieved 98% of intrusion detection accuracy. It is high when compared with the existing methodologies.

Keywords

Convolution Neural Networks (CNN), Intrusion Detection System (IDS), Internet of Things (IoT), Attacks, Data normalisation.

References

[1] Karthik Kallepalli, and Umair B. Chaudhry, “Intelligent Security: Applying Artificial Intelligence to Detect Advanced Cyber Attacks,” Challenges in the IoT and Smart Environments, pp. 287-320, 2021.

[CrossRef ] [Google Scholar ] [Publisher Link ]

[2] Yanmiao Li et al., “Robust Detection for Network Intrusion of Industrial IoT Based on Multi-CNN Fusion,” Measurement, vol. 154, 2020.

[CrossRef ] [Google Scholar ] [Publisher Link ]

[3] Jin Yuan et al., “Gated CNN: Integrating Multi-Scale Feature Layers for Object Detection,” Pattern Recognition, vol. 105, 2020.

[CrossRef ] [Google Scholar ] [Publisher Link ]

[4] Yong Soon Tan et al., “Convolutional Neural Network with Spatial Pyramid Pooling for Hand Gesture Recognition,” Neural Computing and Applications, vol. 33, pp. 5339-5351, 2021.

[CrossRef ] [Google Scholar ] [Publisher Link ]

[5] Selina Y. Cho, Jassim Happa, and Sadie Creese, “Capturing Tacit Knowledge in Security Operation Centers,” IEEE Access, vol. 8, pp. 42021-42041, 2020.

[CrossRef ] [Google Scholar ] [Publisher Link ]

[6] Sun Jingyao et al., “Securing a Network: How Effective Using Firewalls and VPNs Are?,” Advances in Information and Communication: Proceedings of the 2019 Future of Information and Communication Conference, vol. 2, pp. 1050-1068, 2020.

[CrossRef ] [Google Scholar ] [Publisher Link ]

[7] Soliman Abd Elmonsef Sarhan, Hassan A. Youness, and Ayman M. Bahaa-Eldin, “A Framework for Digital Forensics of Encrypted Real-Time Network Traffic, Instant Messaging, and VoIP Application Case Study,” Ain Shams Engineering Journal, vol. 14, no. 9, 2022.

[CrossRef ] [Google Scholar ] [Publisher Link ]

[8] Vikash Kumar et al., “An Integrated Rule-Based Intrusion Detection System: Analysis on UNSW-NB15 Data Set and the Real Time Online Dataset,” Cluster Computing, vol. 23, pp. 1397-1418, 2020.

[CrossRef ] [Google Scholar ] [Publisher Link ]

[9] Jung Hyun Ryu, and Jong Hyuk Park, “Machine Learning-Based Intrusion Detection System for Smart City,” Advances in Computer Science and Ubiquitous Computing: CSA-CUTE 2018, Springer Singapore, pp. 405-409, 2020.

[CrossRef ] [Google Scholar ] [Publisher Link ]

[10] Laisen Nie et al., “Data-Driven Intrusion Detection for Intelligent Internet of Vehicles: A Deep Convolutional Neural Network-Based Method,” IEEE Transactions on Network Science and Engineering, vol. 7, no. 4, pp. 2219-2230, 2020.

[CrossRef ] [Google Scholar ] [Publisher Link ]

[11] Tanzila Saba et al., “Anomaly-Based Intrusion Detection System for IoT Networks through Deep Learning Model,” Computers and Electrical Engineering, vol. 99, 2022.

[CrossRef ] [Google Scholar ] [Publisher Link ]

[12] Yifan Guo et al., “Unsupervised Anomaly Detection in IoT Systems for Smart Cities,” IEEE Transactions on Network Science and Engineering, vol. 7, no. 4, pp. 2231-224, 2020.

[CrossRef ] [Google Scholar ] [Publisher Link ]

[13] Wajdi Alhakami et al., “Network Anomaly Intrusion Detection Using a Nonparametric Bayesian Approach and Feature Selection,” IEEE Access, vol. 7, pp. 52181-52190, 2019.

[CrossRef ] [Google Scholar ] [Publisher Link ]

[14] Seyedeh Mahsan Taghavinejad et al., “Intrusion Detection in IoT-Based Smart Grid Using Hybrid Decision Tree,” 6^th International Conference on Web Research, 2020.

[CrossRef ] [Google Scholar ] [Publisher Link ]