Feature optimization using a two-tier hybrid optimizer in an Internet of Things network

• Autorzy: Agrawal Akhileshwar Prasad , Singh Nanhay

Identyfikatory

DOI

10.34768/amcs-2023-0023

• Języki publikacji: EN

Abstrakty

EN

The growing use of the Internet of Things (IoT) in smart applications necessitates improved security monitoring of IoT components. The security of such components is monitored using intrusion detection systems which run machine learning (ML) algorithms to classify access attempts as anomalous or normal. However, in this case, one of the issues is the large length of the data feature vector that any ML or deep learning technique implemented on resource-constrained intelligent nodes must handle. In this paper, the problem of selecting an optimal-feature set is investigated to reduce the curse of data dimensionality. A two-layered approach is proposed: the first tier makes use of a random forest while the second tier uses a hybrid of gray wolf optimizer (GWO) and the particle swarm optimizer (PSO) with the k-nearest neighbor as the wrapper method. Further, differential weight distribution is made to the local-best and global-best positions in the velocity equation of PSO. A new metric, i.e., the reduced feature to accuracy ratio (RFAR), is introduced for comparing various works. Three data sets, namely, NSLKDD, DS2OS and BoTIoT, are used to evaluate and validate the proposed work. Experiments demonstrate improvements in accuracy up to 99.44%, 99.44% and 99.98% with the length of the optimal-feature vector equal to 9, 4 and 8 for the NSLKDD, DS2OS and BoTIoT data sets, respectively. Furthermore, classification improves for many of the individual classes of attacks: denial-of-service (DoS) (99.75%) and normal (99.52%) for NSLKDD, malicious control (100%) and DoS (68.69%) for DS2OS, and theft (95.65%) for BoTIoT.

Słowa kluczowe

EN

IoT; Internet of things; anomaly mitigation; GWO; Gray Wolf Optimizer; feature optimization; PSO; particle swarm optimizer

PL

IoT; Internet rzeczy; GWO; optymalizacja funkcji; PSO

• Wydawca: Oficyna Wydawnicza Uniwersytetu Zielonogórskiego
• Czasopismo: International Journal of Applied Mathematics and Computer Science
• Rocznik: 2023
• Tom: Vol. 33, no. 2
• Strony: 313--326
• Opis fizyczny: Bibliogr. 39 poz., rys., tab., wykr.

Twórcy

autor

Agrawal Akhileshwar Prasad

• AIACT&R, Guru Gobind Singh Indraprastha University Dseu, Delhi, India

autor

Singh Nanhay

• Department of Computer Science and Engineering Netaji Subhas University of Technology (East Campus) Geeta Colony, Delhi 110031, India

Bibliografia

• [1] Alazzam, H., Sharieh, A. and Sabri, K.E. (2020). A feature selection algorithm for intrusion detection system based on pigeon inspired optimizer, Expert Systems with Applications 148: 113249.
• [2] Alzubi, Q.M., Anbar, M., Alqattan, Z.N., Al-Betar, M.A. and Abdullah, R. (2020). Intrusion detection system based on a modified binary grey wolf optimisation, Neural Computing and Applications 32(10): 6125-6137.
• [3] Ashraf, E., Areed, N.F., Salem, H., Abdelhay, E.H. and Farouk, A. (2022). FIDChain: Federated intrusion detection system for blockchain-enabled IoT healthcare applications, Healthcare 10(6): 1110.
• [4] Band, S.S., Janizadeh, S., ChandraPal, S., Saha, A., Chakrabortty, R., Shokri, M. and Mosavi, A. (2020). Novel ensemble approach of deep learning neural network (DLNN) model and particle swarm optimization (PSO) algorithm for prediction of gully erosion susceptibility, Sensors 20(19): 5609.
• [5] Bhattacharjya, A. (2022). A holistic study on the use of blockchain technology in CPS and IoT architectures maintaining the CIA triad in data communication, International Journal of Applied Mathematics and Computer Science 32(3): 403-413, DOI: 10.34768/amcs-2022-0029.
• [6] Carvalho, M. and Ludermir, T.B. (2007). Particle swarm optimization of neural network architectures and weights, 7th International Conference on Hybrid Intelligent Systems (HIS 2007), Kaiserslautern, Germany, pp. 336-339.
• [7] Chen, L., Li, Y., Deng, X., Liu, Z., Lv, M. and Zhang, H. (2022). Dual auto-encoder GAN-based anomaly detection for industrial control system, Applied Sciences 12(10): 4986.
• [8] Chopra, N., Kumar, G. and Mehta, S. (2016). Hybrid GWO-PSO algorithm for solving convex economic load dispatch problem, International Journal of Research in Advent Technology 4(6): 37-41.
• [9] Gao, X., Shan, C., Hu, C., Niu, Z. and Liu, Z. (2019). An adaptive ensemble machine learning model for intrusion detection, IEEE Access 7: 82512-82521.
• [10] Gu, J. and Lu, S. (2021). An effective intrusion detection approach using SVM with naïve Bayes feature embedding, Computers & Security 103: 102158.
• [11] Gu, J., Wang, L., Wang, H. and Wang, S. (2019). A novel approach to intrusion detection using SVM ensemble with feature augmentation, Computers & Security 86: 53-62.
• [12] Hasan, M., Islam, M.M., Zarif, M.I. and Hashem, M.M. (2019). Attack and anomaly detection in IoT sensors in IoT sites using machine learning approaches, Internet of Things 7: 100059.
• [13] Huma, Z.E., Latif, S., Ahmad, J., Idrees, Z., Ibrar, A., Zou, Z., Alqahtani, F. and Baothman, F. (2021). A hybrid deep random neural network for cyberattack detection in the industrial Internet of Things, IEEE Access 9: 55595-605.
• [14] Hur, J.H., Ihm, S.Y. and Park, Y.H. (2017). A variable impacts measurement in random forest for mobile cloud computing, Wireless Communications and Mobile Computing 2017, Article ID: 6817627.
• [15] Kennedy, J. and Eberhart, R.C. (1997). A discrete binary version of the particle swarm algorithm, 1997 IEEE International conference on Systems, Man, and Cybernetics, Orlando, USA, pp. 4104-4108.
• [16] Khraisat, A., Gondal, I., Vamplew, P., Kamruzzaman, J. and Alazab, A. (2019). A novel ensemble of hybrid intrusion detection system for detecting Internet of Things attacks, Electronics 8(11): 1210.
• [17] Kim, D. and Heo, T.Y. (2022). Anomaly detection with feature extraction based on machine learning using hydraulic system IoT sensor data, Sensors 22(7): 2479.
• [18] Koroniotis, N., Moustafa, N., Sitnikova, E. and Turnbull, B. (2019). Towards the development of realistic botnet dataset in the Internet of Things for network forensic analytics: BOT-IOT dataset, Future Generation Computer Systems 100: 779-796.
• [19] Kowalski, P.A. and Słoczyński, T. (2021). A modified particle swarm optimization procedure for triggering fuzzy flip-flop neural networks, International Journal of Applied Mathematics and Computer Science 31(4): 577-586, DOI: 10.34768/amcs-2021-0039.
• [20] Kumar, P., Gupta, G.P. and Tripathi, R. (2021a). A distributed ensemble design based intrusion detection system using fog computing to protect the Internet of Things networks, Journal of Ambient Intelligence and Humanized Computing 12(10): 9555-9572.
• [21] Kumar, P., Gupta, G. and Tripathi, R. (2021b). Toward design of an intelligent cyber attack detection system using hybrid feature reduced approach for IoT networks, Arabian Journal for Science and Engineering 46(4): 3749-3778.
• [22] Kunhare, N., Tiwari, R. and Dhar, J. (2020). Particle swarm optimization and feature selection for intrusion detection system, Sādhanā 45(1): 1-14.
• [23] Kusy, M. and Zajdel, R. (2021). A weighted wrapper approach to feature selection, International Journal of Applied Mathematics and Computer Science 31(4): 685-696, DOI: 10.34768/amcs-2021-0047.
• [24] Mahfouz, A.M., Venugopal, D. and Shiva, S.G. (2020). Comparative analysis of ML classifiers for network intrusion detection, 2020 4th International Congress on Information and Communication Technology, London, UK, pp. 193-207.
• [25] Mirjalili, S., Mirjalili, S.M. and Lewis, A. (2014). Grey wolf optimizer, Advances in Engineering Software 69: 46-61.
• [26] Pahl, M. and Aubet, F. (2018). All eyes on you: Distributed multidimensional IoT microservice anomaly detection, 14th International Conference on Network and Service Management (CNSM), Rome, Italy, pp. 72-80.
• [27] Pajouh, H.H., Dastghaibyfard, G. and Hashemi, S. (2017). Two-tier network anomaly detection model: A machine learning approach, Journal of Intelligent Information Systems 48(1): 61-74.
• [28] Safaldin, M., Otair, M. and Abualigah, L. (2021). Improved binary gray wolf optimizer and SVM for intrusion detection system in wireless sensor networks, Journal of Ambient Intelligence and Humanized Computing 12(2): 1559-1576.
• [29] Shafiq, M., Tian, Z., Sun, Y., Du, X. and Guizani, M. (2020). Selection of effective machine learning algorithm and BoT-IoT attacks traffic identification for Internet of Things in smart city, Future Generation Computer Systems 107: 433-442.
• [30] Shafiq, U., Shahzad, M.K., Anwar, M., Shaheen, Q., Shiraz, M. and Gani, A. (2022). Transfer learning auto-encoder neural networks for anomaly detection of DDoS generating IoT devices, Security and Communication Networks 2022, Article ID: 8221351.
• [31] Singh, N. and Singh, S.B. (2017). Hybrid algorithm of particle swarm optimization and grey wolf optimizer for improving convergence performance, Journal of Applied Mathematics 2017, Article ID: 2030489.
• [32] Siwek, K. and Osowski, S. (2016). Data mining methods for prediction of air pollution, International Journal of Applied Mathematics and Computer Science 26(2): 467-478, DOI: 10.1515/amcs-2016-0033.
• [33] Soe, Y., Feng, Y., Santosa, P., Hartanto, R. and Sakurai, K. (2020). Towards a lightweight detection system for cyber attacks in the IoT environment using corresponding features, Electronics 9(1): 144.
• [34] Su, T., Sun, H., Zhu, J., Wang, S. and Li, Y. (2020). BAT: Deep learning methods on network intrusion detection using NSL-KDD dataset, IEEE Access 8: 29575-29585.
• [35] Tavallaee, M., Bagheri, E., Lu, W. and Ghorbani, A.A. (2009). A detailed analysis of the KDD CUP 99 data set, 2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications, Ottawa, Canada, pp. 1-6.
• [36] Tian, D. (2018). Particle swarm optimization with chaos-based initialization for numerical optimization, Intelligent Automation & Soft Computing 24(2): 331-342.
• [37] Tian, Q., Han, D., Li, K.C., Liu, X., Duan, L. and Castiglione, A. (2020). An intrusion detection approach based on improved deep belief network, Applied Intelligence 50(10): 3162-3178.
• [38] Wei, W., Chen, S., Lin, Q., Ji, J. and Chen, J. (2020). A multi-objective immune algorithm for intrusion feature selection, Applied Soft Computing 95: 106522.
• [39] Zeeshan, M., Riaz, Q., Bilal, M.A., Shahzad, M.K., Jabeen, H., Haider, S.A. and Rahim, A. (2021). Protocol-based deep intrusion detection for DoS and DDoS attacks using UNSW-NB15 and BoT-IoT data-sets, IEEE Access 10: 2269-83.

Uwagi

PL

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023)