A novel framework for the prevention of black-hole in wireless sensors using hybrid convolution network
https://doi.org/10.17586/2226-1494-2022-22-2-317-323
Abstract
Problems of Wireless Sensor Networks (WSN) are associated with a significant increase in the number of devices on these networks. In this regard, the requirements for the protection and the security of WSN from external influences are increasing significantly. WSN security problems are solved by solving the problem of optimal path routing, energy conservation, and so on. This paper proposes a hybrid model of an efficient packet routing and delivery system to prevent Black-hole attacks. This type of attack is considered the most common on the network due to its unique characteristics. To detect such attacks, a deep learning model using a Convolutional Neural Network (CNN) is proposed. The learning algorithm must be reliable and trustworthy so that attack analysis can be considered at different levels to study the intelligent behavior of network attacks. The paper considers the problem of finding the optimal shortest path using Deep Q-Learning and convolutional neural networks to perform efficient routing and delivery of packets in a safer way. As a result of simulation, the achieved accuracy reached 98.57 %.
About the Authors
J. Kolangiappan
Periyar University
India
India
Jayaraman Kolangiappan — PhD, Research Scholar
Salem, 636011
A. Senthil Kumar
Arignar Anna Government Arts College
India
India
Angamuthu Senthil Kumar — PhD, Assistant Professor
Nxamakkal, 637002
References
1. Ding Z., Zhu C., De Graef M. Determining crystallographic orientation via hybrid convolutional neural network // Materials Characterization. 2021. V. 178. P. 111213. https://doi.org/10.1016/j.matchar.2021.111213
2. Subasini C.A., Karuppiah S.P., Sheeba Adlin, Padmakala S. Developing an attack detection framework for wireless sensor network-based healthcare applications using hybrid convolutional neural network // Transactions on Emerging Telecommunications Technologies. 2021. V. 32. N 11. P. e4336. https://doi.org/10.1002/ett.4336
3. da Silva Cotrim W., Felix L.B., Minim V.P., Campos R.C., Minim L.A. Development of a hybrid system based on convolutional neural networks and support vector machines for recognition and tracking color changes in food during thermal processing // Chemical Engineering Science. 2021. V. 240. P. 116679. https://doi.org/10.1016/j.ces.2021.116679
4. Bangotra D.K., Singh Y., Selwal A., Kumar N., Singh P.K., Hong W-C. An intelligent opportunistic routing algorithm for wireless sensor networks and its application towards e-healthcare // Sensors. 2020. V. 20. N 14. P. 3887. https://doi.org/10.3390/s20143887
5. Pankaj R. Chandre, Dr. Parikshit N. Mahalle, Dr. Gitanjali R. Shinde. Intrusion prevention framework for WSN using Deep CNN // Turkish Journal of Computer and Mathematics Education. 2021. V. 12. N 6. .P 3567–3572.
6. Pasyar P., Mahmoudi T., Kouzehkanan S.Z., Ahmadian A., Arabalibeik H., Soltanian N., Radmard A.R. Hybrid classification of diffuse liver diseases in ultrasound images using deep convolutional neural networks // Informatics in Medicine Unlocked. 2021. V. 22. P. 100496. https://doi.org/10.1016/j.imu.2020.100496
7. Gulla K.K., Viswanath P., Veluru S.B., Kumar R.R. Machine learning based intrusion detection techniques // Handbook of Computer Networks and Cyber Security: Principles and Paradigms. 2019. P. 873–888. https://doi.org/10.1007/978-3-030-22277-2_35
8. Jo W., Kim S., Lee C., Shon T. Packet preprocessing in CNN-based network intrusion detection system // Electronics. 2020. V. 9. N 7. P. 1151. https://doi.org/10.3390/electronics9071151
9. Yi P., Zhu T., Zhang Q., Wu Y., Li J. A denial of service attack in advanced metering infrastructure network // Proc. of the 1st IEEE International Conference on Communications (ICC). 2014. P. 1029– 1034. https://doi.org/10.1109/ICC.2014.6883456
10. Wang G., Ren Y., Dou K., Li J. IDTCP: an effective approach to mitigating the TCP incast problem in data center networks // Information Systems Frontiers. 2014. V. 16. N 1. P. 35–44. https://doi.org/10.1007/s10796-013-9463-4
11. Wang G., Ren Y., Li J. An effective approach to alleviating the challenges of transmission control protocol // IET Communications. 2014. V. 8. N 6. P. 860–869. https://doi.org/10.1049/ietcom.2012.0154
12. Al-Mandhari W., Gyoda K., Nakajima N. Performance evaluation of active route time-out parameter in ad-hoc on demand distance vector (AODV) // Proc. of the 6th WSEAS International Conference on Applied Electromagnetics, Wireless and Optical. 2008. P. 47–51.
13. McHugh J. Testing intrusion detection systems: a critique of the 1998 and 1999 darpa intrusion detection system evaluations as performed by lincoln laboratory // ACM Transactions on Information and System Se curity. 2000. V. 3. N 4. P. 262–294. https://doi.org/10.1145/382912.382923
14. Draper-Gil G., Lashkari A.H., Mamun M.S.I., Ghorbani A.A. Characterization of encrypted and VPN traffic using time-related features // Proc. of the 2nd International Conference on Information Systems Security and Privacy (ICISSP). 2016. P. 407–414. https://doi.org/10.5220/0005740704070414
15. Hinton G.E., Srivastava N., Krizhevsky A., Sutskever I., Salakhutdinov R.R. Improving neural networks by preventing coadaptation of feature detectors // arXiv. 2012. arXiv:1207.0580.
16. Wang S., Minku L.L., Yao X. A systematic study of online class imbalance learning with concept drift // IEEE Transactions on Neural Networks and Learning Systems. 2018. V. 29. N 10. P. 4802–4821. https://doi.org/10.1109/TNNLS.2017.2771290
Review
For citations:
Kolangiappan J., Senthil Kumar A. A novel framework for the prevention of black-hole in wireless sensors using hybrid convolution network. Scientific and Technical Journal of Information Technologies, Mechanics and Optics. 2022;22(2):317-323. https://doi.org/10.17586/2226-1494-2022-22-2-317-323
Views:
2
Email this article 