International Journal of Computer Network and Information Security (IJCNIS)
IJCNIS Vol. 16, No. 3, 8 Jun. 2024
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Auto-metric Graph Neural Network for Attack Detection on IoT-based Smart Environment and Secure Data Transmission using Advanced Wild Horse Standard Encryption Method
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Author(s)
Ranganath Yadawad
1,*
Umakant P. Kulkarni
1
Jafar A. Alzubi
2
Index Terms
Attack Detection, Secure Data Transmission, Color Harmony Algorithm, Auto-metric Graph Neural Network, Enhanced Identity-based Encryption, Wild Horse Optimizer
Abstract
Smart cities (SCs) are being constructed with the huge placement of the Internet of Things (IoT). Real-time enhancements to life quality based on comfort and efficiency. The key concerns in most SCs that immediately impact network performance are security and privacy. Numerous approaches are proposed for secure data transmission, but the current methods do not provide high accuracy and it provide high computational time. To resolve these problems, an Auto-metric Graph Neural Network for Attack Detection and Secure Data Transmission using Optimized Enhanced Identity-Based Encryption in IoT (AGNN-AWHSE-ST-IoT) is proposed. Primarily, the input data is taken from the NSL-KDD dataset. The input data is gathered with the aid of NSL-KDD is pre-processed using three steps, crisp data conversion, splitting, and normalization. Then the Pre-processed input is fed into the Colour Harmony Algorithm (CHA) based feature selection to select the important features. After feature selection, the preferred features are given to the AGNN classifier. After classifying, the data is given to Enhanced Identity-Based Encryption (EIBE), and it is optimized using Wild Horse Optimizer (WHO) for transmitting the data more safely. The outcomes of the normal data are displayed using the LCD monitor. The AGNN-AWHSE-ST-IoT method is implemented in PYTHON. The AGNN-AWHSE-ST-IoT method attains 8.888%, 13.953%, 19.512% higher accuracy, 2.105%, 6.593%, 8.988% higher cumulative accuracy, 54.285%, 54.285%, 52.941% lower encryption time, 8.2%, 3.3%, 6.9% lower decryption time, 11.627%, 10.344%, 6.666% higher security level and 60.869%, 70% and 64% lower computational time than the existing approaches such as SBAS-ST-IoT, BDN-GWMNN-ST-IoT and DNN-LSTM-ST-IoT respectively.
Cite This Paper
Ranganath Yadawad, Umakant P. Kulkarni, Jafar A. Alzubi, "Auto-metric Graph Neural Network for Attack Detection on IoT-based Smart Environment and Secure Data Transmission using Advanced Wild Horse Standard Encryption Method", International Journal of Computer Network and Information Security(IJCNIS), Vol.16, No.3, pp.1-15, 2024. DOI:10.5815/ijcnis.2024.03.01
Reference
[1]S. Manimurugan, S. Al-Mutairi, M.M. Aborokbah, N. Chilamkurti, S. Ganesan, R. Patan, “Effective attack detection in internet of medical things smart environment using a deep belief neural network,” IEEE Access, vol. 8, pp.77396-77404, April 2020.
[2]P.K. Keserwani, M.C. Govil, E.S. Pilli, P. Govil, “A smart anomaly-based intrusion detection system for the Internet of Things (IoT) network using GWO–PSO–RF model,” J. Reliab. Intell. Environ. vol. 7, no. 1, pp. 3-21, March 2021.
[3]Z.A. Baig, S. Sanguanpong, S.N. Firdous, T.G. Nguyen, C. So-In, “Averaged dependence estimators for DoS attack detection in IoT networks,” Future Gener. Comput. Syst., vol. 102, pp. 198-209, January 2020.
[4]V.R. Kebande, S. Alawadi, F.M. Awaysheh, J.A. Persson, “Active machine learning adversarial attack detection in the user feedback process,” IEEE Access, vol. 9, pp. 36908-36923, March 2021.
[5]M. Waqas, K. Kumar, A.A. Laghari, U. Saeed, M.M. Rind, A.A. Shaikh, F. Hussain, A. Rai, A.Q. Qazi, “Botnet attack detection in Internet of Things devices over cloud environment via machine learning,” Concurr. Comput. Pract. Exp. vol. 34, no. 4, pp. e6662, February 2022.
[6]S. Venkatraman, P. Muthusamy, B. Balusa, T. Jayasankar, G. Kavithaa, K.R. Sekar, C. Bharatiraja, “Time dependent anomaly detection system for smart environment using probabilistic timed automaton,” J. Ambient Intell. Humaniz. Comput. pp. 1-9, January 2021.
[7]S.V. Simpson, G. Nagarajan, “A fuzzy based co-operative blackmailing attack detection scheme for edge computing nodes in MANET-IOT environment,” Future Gener. Comput. Syst. vol. 125, pp. 544-563, December 2021.
[8]M.J. Awan, U. Farooq, H.M. Babar, A. Yasin, H. Nobanee, M. Hussain, O. Hakeem, A.M. Zain, “Real-time DDoS attack detection system using big data approach,” Sustainability, vol. 13, no. 19, pp. 10743, September 2021.
[9]A.A. Malibari, S.S. Alotaibi, R. Alshahrani, S. Dhahbi, R. Alabdan, F.N. Al-wesabi, A.M. Hilal, “A novel metaheuristic with deep learning enabled intrusion detection system for secured smart environment,” Sustain. Energy Technol. Assess. vol. 52, pp. 102312, August 2022.
[10]N. Chamara, M.D. Islam, G.F. Bai, Y. Shi, Y. Ge, “Ag-IoT for crop and environment monitoring: Past, present, and future,” Agric. Syst., vol. 203, pp. 103497, December 2022.
[11]V.M. Manikandan, “A secure biometric authentication system for smart environment using reversible data hiding through encryption scheme,” Machine Learning for Biometrics Academic Press, pp. 201-216, January 2022.
[12]S. Peneti, M. Sunil Kumar, S. Kallam, R. Patan, V. Bhaskar, M. Ramachandran, “BDN-GWMNN: internet of things (IoT) enabled secure smart city applications,” Wirel. Pers. Commun. vol. 119, no. 3, pp. 2469-2485, August 2021.
[13]M. Al Razib, D. Javeed, M.T. Khan, R. Alkanhel, M.S. Muthanna, “Cyber Threats Detection in Smart Environments Using SDN-Enabled DNN-LSTM Hybrid Framework,” IEEE Access, vol. 10, pp. 53015-53026, May 2022.
[14]R. Selvaraj, V.M. Kuthadi, S. Baskar, P.M. Shakeel, A. Ranjan, “Creating security modelling framework analysing in internet of things using EC-GSM-IoT,” Arab. J. Sci. Eng. pp. 1-3, June 2021.
[15]A. Duraisamy, M. Subramaniam, “Attack Detection on IoT Based Smart Cities using IDS Based MANFIS Classifier and Secure Data Transmission Using IRSA Encryption,” Wirel. Pers. Commun. vol. 119, no. 2, 2021, pp. 1913-1934.
[16]D.K. Reddy, H.S. Behera, J. Nayak, P. Vijayakumar, B. Naik, P.K. Singh, “Deep neural network based anomaly detection in Internet of Things network traffic tracking for the applications of future smart cities,” Trans. Emerg. Telecommun. Technol. vol. 32, no. 7, 2021, p. e4121.
[17]M.A. Rahman, A.T. Asyhari, L.S. Leong, G.B. Satrya, M.H. Tao, M.F. Zolkipli, “Scalable machine learning-based intrusion detection system for IoT-enabled smart cities. Sustain. Cities Soc. vol. 61, 2020, p. 102324.
[18]A. Maseleno, D. Abdullah, E. Satria, F.N. Souisa, R. Rahim, An Intelligent Intrusion Detection for Smart Cities Application Based on Random Optimization with Recurrent Network. In Artificial Intelligence Applications for Smart Societies, Springer, Cham, 2021, pp. 119-133.
[19]W. Xu, J. Jang-Jaccard, A. Singh, Y. Wei, F. Sabrina, “Improving performance of autoencoder-based network anomaly detection on nsl-kdd dataset,” IEEE Access, vol. 9, pp. 140136-140146, September 2021.
[20]M. Zaeimi, A. Ghoddosian, “Color harmony algorithm: an art-inspired metaheuristic for mathematical function optimization,” Soft Comput. vol. 24, no. 16, pp. 12027-12066, August 2020.
[21]X. Song, M. Mao, X. Qian, “Auto-Metric Graph Neural Network Based on a Meta-Learning Strategy for the Diagnosis of Alzheimer's Disease,” IEEE J. Biomed. Health Inform. vol. 25, no. 8, pp. 3141-3152, January 2021.
[22]R.K. Gupta, K.K. Almuzaini, R.K. Pateriya, K. Shah, P.K. Shukla, R. Akwafo, “An improved secure key generation using enhanced identity-based encryption for cloud computing in large-scale 5G,” Wirel. Commun. Mob. Comput. 2022.
[23]I. Naruei, F. Keynia, “Wild horse optimizer: A new meta-heuristic algorithm for solving engineering optimization problems,” Eng. Comput. pp.1-32, June 2021.