Routing and Security Based Ad-Hoc Networks Configuration for Identification of Attack Using Reinforcement Learning Approach
Keywords:
False Node, Wireless Sensor Computer Networks, Denial of Service Attack, Packet Drop, Routing algorithm, Reinforcement LearningAbstract
In many fields of Research, wireless sensor networks have grown in popularity. Depending on the dangerous situation the networks fields, there are more chances to attack wireless sensor network. This research explains how to locate rogue nodes in the networks based on investigation and responses from each node in the networks. In generally a malicious node blocks the transmission of signal to other nodes. So, in order to increase network performance, avoid much traffic for the server to buffer, causing them to slow down and eventually stop. Hence to identify the fake node in wireless sensor network is main aim of this research through Network Simulator with the help of Reinforcement Learning based routing algorithms. Further, attack vectors allow hackers to exploit system’s vulnerabilities including manual elements. It is suggested that a workable security framework for the WSN (Wireless Sensor Network) used to estimate traffic or packet loss and throughput ratio in order to detect fault identity. So enhancing the networks performance is based on request-response mechanism of the nodes, which engages with features at various levels of the protocol's system, monitors and analyzes typical patterns and alerts node to ensure their dangerous activities cannot transmit across the network. Some of monitoring basic functions through routing algorithm and NS2 include overseeing server CPUs, paying attention to network traffic, identifying patterns in error rates, alerting you about slow pages and combing through your access logs to find out how long requests usually take.
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Liu, X., Abdelhakim, M., Krishnamurthy, P. and Tipper, D., 2018, May. Identifying malicious nodes in multihop IoT networks using diversity and unsupervised learning. In 2018 IEEE International Conference on Communications (ICC) (pp. 1-6). IEEE.
Lai, Y., Tong, L., Liu, J., Wang, Y., Tang, T., Zhao, Z. and Qin, H., 2022. Identifying malicious nodes in wireless sensor networks based on correlation detection. computers & security, 113, p.102540.
J. N. Arauz. 802.11 Markov channel modeling. Ph.D. Dissertation, School of Information Science, University of Pittsburgh, 2004.
C. Ateniese, R. Burns, R. Curtmola, J. Herring, L. Kissner, Z. Peterson, and D. Song. Provable data possession at untrusted stores. In Proceedings of the ACM Conference on Computer and Communications Security (CCS), pages 598–610, Oct. 2007.
G. Ateniese, S. Kamara, and J. Katz. Proofs of storage from homomorphic identification protocols. In Proceedings of the International Conference on the Theory and Application of Cryptology and Information Security (ASIACRYPT), 2009.
Liu, L., Yang, J. and Meng, W., 2019. Detecting malicious nodes via gradient descent and support vector machine in Internet of Things. Computers & Electrical Engineering, 77, pp.339-353.
Khalid, N.A., Bai, Q. and Al-Anbuky, A., 2019. Adaptive trust-based routing protocol for large scale WSNs. IEEE Access, 7, pp.143539-143549.
B. Awerbuch, R. Curtmola, D. Holmer, C. Nita-Rotaru, and H. Rubens. ODSBR: an on-demand secure byzantine resilient routing protocol for wireless ad hoc networks. ACM TISSEC, 10(4), 2008.
K. Balakrishnan, J. Deng, and P. K. Varshney. TWOACK: preventing selfishness in mobile ad hoc networks. In Proceedings of the IEEE WCNC Conference, 2005.
D. Boneh, B. Lynn, and H. Shacham. Short signatures from the weil pairing. Journal of Cryptology, 17(4):297–319, Sept. 2004.
S. Buchegger and J. Y. L. Boudec. Performance analysis of the confidant protocol (cooperation of nodes: fairness in dynamic ad-hoc networks). In Proceedings of the ACM MobiHoc Conference, 2002.
L. Buttyan and J. P. Hubaux. Stimulating cooperation in self-organizing mobile ad hoc networks. ACM/Kluwer Mobile Networks and Applications, 8(5):579–592, Oct. 2003.
J. Crowcroft, R. Gibbens, F. Kelly, and S. Ostring. Modelling incentives for collaboration in mobile ad hoc networks. In Proceedings of WiOpt, 2003.
J. Avinash and N. Sudhakar, “Location and Quality of Service Guaranteed Optimized Routing In Wireless Sensor Network Environment,” Journal of Advanced Research in Dynamical and Control Systems, Vol. 10, no. 11, pp. 295-306, Nov. 2018.
Basi Reddy A., Liyaz P., Surendra Reddy B., Manoj Kumar K., Sathish K. (2019) Advanced Spatial Reutilization for Finding Ideal Path in Wireless Sensor Networks. In: Singh M., Gupta P., Tyagi V., Flusser J., Ören T., Kashyap R.(eds) Advances in Computing and Data Sciences. ICACDS 2019. Communications in Computer and Information Science, vol 1046. Springer, Singapore.
J. Avinash and N. Sudhakar, “Interference Reduction Aware Optimal Route Path Establishment In Wireless Sensor Network Environment,” International Journal of Engineering and Advanced Technology, Vol. 8, no. 5, pp. 642-648, June. 2019.
Ch Muni Koteswara Rao, T N Siva Kumar J. Avinash, “Detection of Network Intrusion by using Supervised Machine Learning Technique with Feature Selection,” International Conference on Advances in Science, Engineering and Technology, Vol. 6, no. 12, pp. 1-4 Dec. 2019, ISSN 2394-2320.
J. Avinash and N. Sudhakar, “Route Breakage Concerned Non Interfering Multi Path Routing Protocol for Wireless Sensor Network Environment,” International Journal of Advanced Science & Technology, Vol. 28, no. 13, pp. 38-48, Nov. 2019.
Lolai, A., Wang, X., Hawbani, A., Dharejo, F.A., Qureshi, T., Farooq, M.U., Mujahid, M. and Babar, A.H., 2022. Reinforcement learning based on routing with infrastructure nodes for data dissemination in vehicular networks (RRIN). Wireless Networks, 28(5), pp.2169-2184.
R. Anderson and M. Kuhn, Tamper resistance û a cautionary note, in: tiProceedings of the Second Usenix Workshop on Electronic Commerce, Oakland, CA (November 1996).
J. Avinash, K. Geetanjali, and Ch. Bhaskar Rao, “Active direction finding for files integrity and interruption segregated services in wireless sensor networks,” International Journal of Innovative Technology and Research, Vol. 5, Issue No. 2, pp. 5637-5641, Feb-Mar. 2017, ISSN 2320-5547.
Srivastava, S. and Singh, J.P., 2021, October. Efficiency of Multi-Protocol LABEL Switching over Traditional Switching. In 2021 5th International Conference on Information Systems and Computer Networks (ISCON) (pp. 1-4). IEEE.
Çakmakçı, S.D., Hutschenreuter, H., Maeder, C. and Kemmerich, T., 2021, June. A framework for intelligent DDoS attack detection and response using SIEM and ontology. In 2021 IEEE International Conference on Communications Workshops (ICC Workshops) (pp. 1-6). IEEE.
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