An Efficient Intrusion Detection and Prevention System for DDOS Attack in WSN Using SS-LSACNN and TCSLR
DOI:
https://doi.org/10.13052/jcsm2245-1439.1315Keywords:
Distributed denial of service (DDoS) attack, soft swish linear scaling based adam convolution neural network (SS-LSACNN), two’s Compliment shift left reverse (TCSLR), correlation coefficient based SMOTE (CC-SMOTE)Abstract
Sensor Nodes (SNs) are utilized by Wireless Sensor Networks (WSNs) to recognize their environment; in addition, the WSN delivers data from sensing nodes to the sink. The WSNs are exposed to several security threats owing to the broadcast performance of transmission along with the increase in the growth of application regions. Countermeasures like Intrusion Detection and Prevention Systems (IDPS) should be adopted to overcome the aforementioned attacks. By implementing these systems, several intrusions can be detected in WSN; also, WSN can be prevented from various security attacks. Therefore, identifying the general attack that influences the SNs mentioned as Distributed Denial of Service (DDoS) attack and recuperating the data utilizing Soft Swish (SS)-Linear Scaling-centered Adam Convolution Neural Network (SS-LSACNN) along with Two’s Compliment Shift Reverse (TCSLR) operation are the intentions of this work. Firstly, for extracting the vital features, the data gathered as of the dataset are utilized. After that, the extracted features are pre-processed. It is then utilized for attack detection. The null features and the redundant data are removed in preprocessing. By employing the Correlation Coefficient-centered Synthetic Minority Oversampling Technique (CC-SMOTE) methodology, data separation regarding classes and data balancing was performed to prevent the imbalance issue. Subsequently, to provide the preprocessed data for attack detection, the Numeralization and feature scaling are executed. After that, by utilizing Chebyshev Distance (CD)-centric K-Means Algorithm (KMA), the real-time SNs are initialized as well as clustered. The data gathered as of the SNs are utilized for attack detection following the clustering phase. Following the detection phase, the data being attacked are amassed in the log file; similarly, the non-attacked data are inputted into the prevention phase. Next, the experiential analysis is carried out for examining the proposed system’s efficacy. The outcomes revealed that the proposed model exhibits 98.15% accuracy, 97.59% sensitivity, 95.72% specificity, and 95.48% F-measure, which displays the proposed model’s efficacy.
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Gavel, S., A. S. Raghuvanshi, and S. Tiwari. 2020. A novel density estimation based intrusion detection technique withPearson’s divergence for Wireless Sensor Networks. ISA Transactions (Pre-proof). https://doi.org/10.1016/j.isatra.2020.11.016.
Premkumar, M., and T. V. P. Sundararajan. 2020. DLDM Deep learning-based defense mechanism for denial of service attacks in wireless sensor networks. Microprocessors and Microsystems. 79(8): 1–10.
Prabakaran K, N. Kumaratharan, P. S. D. Epsiba. 2020. An evaluation of effective intrusion DoS detection and prevention system based on SVM classifier for WSN. IOP Conference Series Materials Science and Engineering. 925(1): 1–11.
Bisen, D., B. Barmaiya, R. Prasad and P. Saurabh. 2021. Detection and prevention of black hole attack using trusted and secure routing in wireless sensor network. Springer, Cham, 1st Edition, ISBN: 978-3-030-49335-6.
Borkar, G. M., L. H. Patil, D. Dalgade and A. Hutke. 2019. A novel clustering approach and adaptive SVM classifier for intrusion detection in WSN a data mining concept. Sustainable Computing Informatics and Systems. 23(5): 120–135.
Ajeetha, G., and G. M. Priya. 2019. Machine learning based DDoS attack detection. Innovations in Power and Advanced Computing Technologies (i-PACT), 22–23 March, Vellore, India.
Belej, O. 2020. Development of a technique for detecting distributed denial-of-service attacks in security systems of wireless sensor network. 15th International Conference on Computer Sciences and Information Technologies (CSIT), 23–26 September, Zbarazh-Lviv, Ukraine, 2020.
Liu, G., H. Zhao, F. Fan, G. Liu, Q. Xu and S. Nazir. 2022. An enhanced intrusion detection model based on improved kNN in WSNs. Sensors. 22(4): 1–18.
Narayanan, K. L., S. R. Krishnan, E. G. Julie, Y. H. Robinson and V. Shanmuganathan. 2021. Machine learning based detection and a Novel EC-BRTT algorithm-based prevention of DoS attacks in wireless sensor networks. Wireless Personal Communications. https://doi.org/10.1007/s11277-021-08277-7.
Mehetre, D. C., S. E. Roslin and S. J. Wagh. 2018. Detection and prevention of black hole and selective forwarding attack in clustered WSN with active trust. Cluster Computing. https://doi.org/10.1007/s10586-017-1622-9.
Pajila, P. J. B., E. G. Julie and Y. H. Robinson. 2021. FBDR-Fuzzy based DDoS attack detection and recovery mechanism for wireless sensor networks. Wireless Personal Communications. https://doi.org/10.1007/s11277-021-09040-8.
Zhang, W., Han, D., Kuan-Ching Li and Massetto, F. I. 2020. Wireless sensor network intrusion detection system based on MK-ELM. Soft Computing. 24(1): 12361–12374.
Jiang, S., Zhao, J., and Xu, X. 2020. SLGBM an intrusion detection mechanism for wireless sensor networks in smart environments. IEEE Access. 8: 169548–169558.
Premkumar, M., and Sundararajan, T.V.P. 2020. DLDM: Deep learning-based defense mechanism for denial of serviceattacks in wireless sensor networks. Microprocessors and Microsystems.79: 1–10.
Narayanan, K.L., Krishnan, R.S., Julie, E.G., Robinson, Y.H., and Shanmuganathan, V. 2022. Machine learning based detection and a novel EC-BRTT algorithm based prevention of dos attacks in wireless sensor networks. Wireless Personal Communications. 127: 479–503.
Ademola, P. A., and Kabaso, B. 2021. Lightweight models for detection of denial-of-service attack inwireless sensor networks. IET Networks. 10: 185–199.
Suryaprabha, E., and Kumar, N. M.S. 2020. Enhancement of security using optimized DoS (denial-of-service)detection algorithm for wireless sensor network. Soft Computing. 24: 10681–10691.
Safaldin, M., Otair, M., andAbualigah, L. 2021. Improved binary gray wolf optimizer and SVM for intrusion detectionsystem in wireless sensor networks. Journal of Ambient Intelligence and Humanized Computing. 12: 1559–1576.
Balaji, S., and Sasilatha, T. 2019. Detection of denial of service attacks by domination graph applicationin wireless sensor networks. Cluster Computing. 22: 15121–15126.
Sinha, S., and Paul, A. 2020. Neuro-fuzzy based intrusion detection system for wireless sensor network. Wireless Personal Communications. 114: 835–851.
Siva, S.S.S., Geetha, S., and Kannan, A. 2012. Decision tree based light weight intrusion detection using a wrapper approach. Expert Systems with Applications. 39: 129–141.
Elreedy, D., and Amir, F. Atiya. 2019. A novel distribution analysis for SMOTE oversampling method in handling class imbalance. 1st ed, ISBN: 978-3-030-22743-2. Springer, Cham.
Thara, D. K., Sudha, B. G. P., and Fan Xiong. 2019. Auto-detection of epileptic seizure events using deep neural network with different feature scaling techniques. Pattern Recognition Letters. 128: 544–550.
Abdulkareem, S.S., Akgul, A., Jalal, V.J., Faraj, B.M., and Abdullah, O.G.H. 2020. Numerical solution for time period of simple pendulum with large angle. Thermal Science. 24: 25–30.
Faraj, B., and Modanli, M. 2017. Using difference scheme method for the numerical solution of telegraph partial differential equation. Proceedings in Journal of Garmian University. pp. 157–163, Iraq.
Faraj B.M., and Ahamed, F.W. 2019. On the matlab technique by using laplace transform for solving second order ode with initial conditions exactly. Matrix Science Mathematic. 3(2): 8–10.
Modanli, M., Faraj, B.M., and Walyahamed, F. 2019. Using matrix stability for variable telegraph partial differential equation. An International Journal of Optimization and Control: Theories and Applications. 10(2): 237–243.
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