A novel approach to IoT security for intrusion detection system using ensemble network and heuristic-assisted feature fusion

Kathole, Atul B.; Vhatkar, Kapil; Dharmale, Gulbakshee; Chiwhane, Shwetambari; Kimbahune, Vinod V.; Goyal, Ankur

doi:10.47974/JDMSC-2092

Open Access Research Article

A novel approach to IoT security for intrusion detection system using ensemble network and heuristic-assisted feature fusion

Atul B. Katholeatul.kathole1910@gmail.comDepartment of Computer Engineering Dr. D. Y. Patil Institute of TechnologyPune, Maharashtra, 411018, IndiaView full profile → , Kapil Vhatkarkapilnv@gmail.comDepartment of Computer Engineering Dr. D. Y. Patil Institute of TechnologyPune, Maharashtra, 411018, IndiaView full profile → , Gulbakshee Dharmalegulbakshi.dharmale@pccoepune.orgDepartment of Information Technology Pimpri Chinchwad College of EngineeringPune, Maharashtra, 411044, IndiaView full profile → , ^*Shwetambari ChiwhaneCorresponding authorshwetambari.chiwhane@sitpune.edu.inDepartment of Computer Science and Engineering Symbiosis Institute of Technology Symbiosis International (Deemed University)Department of Computer Engineering Symbiosis Institute of Technology Symbiosis International (Deemed University)Pune, Maharashtra, 412115, IndiaView full profile → , Vinod V. Kimbahunevinod.kimbahune@dypvp.edu.inDepartment of Computer Engineering Dr. D. Y. Patil Institute of TechnologyDepartment of Computer Engineering Dr. D. Y. Patil Institute of TechnologyPune, Maharashtra, 411018, IndiaView full profile → , Ankur Goyalankur_gg5781@yahoo.co.inDepartment of Computer Science & Engineering Symbiosis International Deemed University Symbiosis Institute of TechnologyDepartment of Computer Science and Engineering Symbiosis Institute of Technology Symbiosis International (Deemed University) LavalePune, Maharashtra, 412115, IndiaView full profile →

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pp. 2207–2217Vol. 27Issue 7October 2024DOI: 10.47974/JDMSC-2092 Crossmark XML

Received:: 07 Mar 2024
Published Online:: 15 Oct 2024
Article type:: Research Article
Language:: EN
Article no.:: JDMSC-2092
Pages:: 2207–2217

Abstract

The area of “Internet of Things (IoT)” has gained popularity recently as a method for developing intelligent models. Security and privacy are regarded as the two main issues in every real-world IoT application. The security risks posed by IoT-enabled devices are serious threats to the development of the smart industry. Thus, to reduce the security dangers that emanate from IoT devices and give birth to numerous security concerns, Intrusion Detection Systems (IDSs) specifically tailored for the IoT sectors are important. Conventional intrusion detection systems (IDSs) are unsuitable for use in standard Internet of Things (IoT) networks for a variety of reasons, including memory, battery life, and processing power. Various Intrusion Detection Systems (IDSs) have been proposed in academic research to address these problems. However, while identifying anomalies, various intrusion detection systems (IDSs) have issues with low accuracy and numerous false alarms. To counteract the shortcomings of conventional systems, it is recommended that an ensemble deep learning model be utilized to identify intrusions within the Internet of Things (IoT) domain. The final result is then determined by averaging the scores, which is accomplished by combining them. Divergent measurements are thus used to validate the model. On the other hand, by prohibiting unwanted access, the recommended method not only increases the detection rate but also improves network security.

Keywords

Intrusion detection system Internet of Things Weighted feature fusion Fitness value of pelican optimization algorithm Ensemble networks

Subject Classifications

Primary 93A30Secondary 49K15

References

[1] A. Dhumane, S. Chiwhane, A. Mangore, and S. Ambala, “Cluster-based energy-efficient routing in Internet of Things,” in ICT with Intelligent Applications, J. Choudrie, P. Mahalle, T. Perumal, and A. Joshi, Eds., vol. 311. Singapore: Springer, pp. 1-10 (2023). doi: 10.1007/978-981-19-3571-8_40.
[2] B. Sharma, L. Sharma, C. Lal, and S. Roy, “Anomaly based network intrusion detection for IoT attacks using deep learning technique,” Computers and Electrical Engineering, vol. 107, p. 108626, Apr. (2023).
[3] T. Saba, A. Rehman, T. Sadad, H. Kolivand, and S. A. Bahaj, “Anomaly-based intrusion detection system for IoT networks through deep learning model,” Computers and Electrical Engineering, vol. 99, p. 107810, Apr. (2022).
[4] M. Keshk, N. Koroniotis, N. Pham, N. Moustafa, B. Turnbull, and A. Y. Zomaya, “An explainable deep learning-enabled intrusion detection framework in IoT networks,” Information Sciences, vol. 639, p. 119000, Aug. (2023).
[5] M. Abd Elaziz, M. A. A. Al-qaness, A. Dahou, R. A. Ibrahim, and A. A. Abd El-Latif, “Intrusion detection approach for cloud and IoT environments using deep learning and Capuchin Search Algorithm,” Advances in Engineering Software, vol. 176, p. 103402, Feb. (2023).
[6] M. Vishwakarma and N. Kesswani, “DIDS: A deep neural network-based real-time intrusion detection system for IoT,” Decision Analytics Journal, vol. 5, p. 100142, Dec. (2022).
[7] M. Catillo, A. Pecchia, and U. Villano, “CPS-GUARD: Intrusion detection for cyber-physical systems and IoT devices using outlier-aware deep autoencoders,” Computers & Security, vol. 129, p. 103210, Jun. (2023).
[8] S. D. Patil, A. B. Kathole, S. Kumbhare, and K. Vhatkar, “A blockchain-based approach to ensuring the security of electronic data,” International Journal of Intelligent Systems and Applications in Engineering, vol. 12, no. 1, pp. 1-8 (2024).
[9] S. Nagaraj, A. B. Kathole, L. Arya, N. Tyagi, S. B. Goyal, A. S. Rajawat, M. S. Raboaca, T. C. Mihaltan, C. Verma, and G. Suciu, “Improved secure encryption with energy optimization using random permutation pseudo algorithm based on Internet of Things in wireless sensor networks,” Energies, vol. 16, no. 1, p. 8, Jan. (2023). [Online]. Available: https://doi.org/10.3390/en16010008
[10] A. B. Kathole, J. Katti, D. Dhabliya, V. Deshpande, A. S. Rajawat, S. B. Goyal, M. S. Raboaca, T. C. Mihaltan, C. Verma, and G. Suciu, “Energy-aware UAV based on blockchain model using IoE application in 6G network-driven cyber twin,” Energies, vol. 15, no. 21, p. 8304, Nov. (2022). [Online]. Available: https://doi.org/10.3390/en15218304.
[11] S. D. Patil, A. B. Kathole, S. Kumbhare, K. Vhatkar, and V. V. Kimbahune, “A blockchain-based approach to ensuring the security of electronic data,” International Journal of Intelligent Systems and Applications in Engineering, vol. 12, no. 11s, pp. 649–655 (2024).
[12] R. A. Elsayed, R. A. Hamada, M. I. Abdalla, and S. A. Elsaid, “Securing IoT and SDN systems using deep-learning-based automatic intrusion detection,” Ain Shams Engineering Journal, vol. 14, no. 10, pp. 102211, Oct. (2023).
[13] B. Madhu, M. V. G. Char, R. Vankdothu, A. K. Silivery, and V. Aerranagula, “Intrusion detection models for IoT networks via deep learning approaches,” Measurement: Sensors, vol. 25, p. 100641, Feb. (2023).
[14] M. Eskandari, Z. H. Janjua, M. Vecchio, and F. Antonelli, “Passban IDS: An intelligent anomaly-based intrusion detection system for IoT edge devices,” IEEE Internet of Things Journal, vol. 7, no. 8, pp. 6882-6897, Aug. (2020).
[15] Kumar, S. S. R. N. P. Bhat, and S. Gupta, “An enhanced quantum key distribution protocol for security authentication,” Journal of Discrete Mathematical Sciences and Cryptography, vol. 22, no. 4, pp. 499-507, (2019).
[16] R. Mills, A. K. Marnerides, M. Broadbent and N. Race, “Practical Intrusion Detection of Emerging Threats,” IEEE Transactions on Network and Service Management, vol. 19, no. 1, pp. 582-600, March (2022).
[17] S. Kumar, P. K. Srivastava, G. K. Srivastava, P. Singhal, D. Singh, and D. Goyal, “Chaos based image encryption security in cloud computing,” J. Discrete Math. Sci. Cryptogr., vol. 25, no. 4, pp. 1041-1051, Jul. (2022).

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