Abstract
Nowadays, there is remarkable growth in technology and wireless sensor networks. These are primarily used for the purpose of communication. Communication between devices may be wired or wireless, hence, the chance of attacks through the networks is increasing daily. For secure communication, intrusion detection and prevention are primary concerns. Thus, analyses of intrusion detection and prevention techniques have become an important part of the engineering field. With the assistance of intrusion detection and prevention system, we are able to determine and then notify the normal and abnormal activities of the users. Thus, there’s a requirement to design effective intrusion detection and prevention system by exploitation machine learning and deep learning for wireless sensor networks. In this work, a comparative study and performance analysis of different machine learning and deep learning techniques are given for intrusion detection and prevention system. The performance evaluation of these techniques is done by experiments conducted on WSN-DS dataset. The comparative analysis shows that deep learning classifiers shows better intrusion detection results than machine learning techniques. In this work, Convolutional Neural Network classifier is used.
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References
Abdullah, M. A., Alsolami, B. M., Alyahya, H. M., & Alotibi, M. H. (2018). Intrusion detection of DoS attacks in WSNs using classification techniques. Journal of fundamental and Applied Sciences, 10(4S), 298–303.
Babar, S., Mahalle, P., Stango, A., Prasad, N., & Prasad, R. (2010). Proposed security model and threat taxonomy for the Internet of Things (IoT). In International Conference on Network Security and Applications. Berlin, Heidelberg: Springer.
Binh, H. T. T., &Dey, N. (Eds.). (2018). Soft computing in wireless sensor networks. CRC Press.
Chowdhuri, S., Chaudhuri, S. S., Banerjee, P., Dey, N., Mandal, A., & Santhil, V. (2016). Secure minimum loss route selection of MIMO-based MANET in combined (indoor, outdoor, and forest) terrain (pp. 1–26). Working paper, International Journal Advanced Intelligence Paradigms.
Shone, N., Ngoc, T. N., Phai, V. D., & Shi, Q. (2018, February). A deep learning approach to network intrusion detection. IEEE Transactions on Emerging Topics in Computational Intelligence, 2(1).
Mahalle, P. N., Anggorojati, B., Prasad, N. R., & Prasad, R. (2013). Identity authentication and capability based access control (IACAC) for the internet of things. Journal of Cyber Security and Mobility, 309–348.
Yin, C., Zhu, Y., Fei, J., & He, X. (2017). A deep learning approach for intrusion detection using recurrent neural networks (Vol. 5). ISSN: 2169–3536. IEEE. Translations.
Dey, N., Wagh, S., Mahalle, P. N., & Pathan, M. S. (2019). Applied machine learning for smart data analysis. CRC Press.
Shelke, M. P., Malhotra, A., & Mahalle, P. (2017). A packet priority intimation-based data transmission for congestion free traffic management in WSNs. Computers & Electrical Engineering, 248–261 (Pergamon).
Niyaz, Q., Sun, W., Javaid, A. Y., & Alam, M. (2015, December 03–05). A deep learning approach for network intrusion detection system. In BICT 2015, New York City, United States.
Almomani, I., Al-Kasasbeh, B., & AL-Akhras, M. (2016). WSN-DS: A dataset for intrusion detection systems in WSNs. Journal of Sensors, 2016(Article ID 4731953) (Hindawi Publishing Corporation).
Myint, H. O., & Meesad, P. (2009). Incremental Learning Algorithm based on Support Vector Machine with Mahalanobis distance (ISVMM) for Intrusion Prevention. 978-1-4244-3388-9/09/$25.00 ©2009 IEEE.
Farnaaz, N., & Jabbar, M. A. (2016). Random forest modeling for network intrusion detection system. Procedia Computer Science, 89, 213–217 (Elsevier).
Al-Qatf, M., Lasheng, Y., Alhabib, M., & Al-Sabahi, K. (2018). Deep learning approach combining sparse autoencoder with SVM for network intrusion detection. IEEE Access. https://doi.org/10.1109/ACCESS.2018.2869577.
Peddabachigari, S., Abraham, A., & Thomas, J. (2016). Intrusion detection systems using decision trees and support vector machines. International Journal of Advanced Networking and Applications, 07(04), 2828–2834. ISSN: 0975-0290.
Chowdhuri, S., Das, S. K., Roy, P., Chakraborty, S., Maji, M., &Dey, N. (2014, November). Implementation of a new packet broadcasting algorithm for MIMO equipped Mobile ad-hoc network. In International Conference on Circuits, Communication, Control and Computing (pp. 372–376). IEEE.
Panda, M., & Patra, M. R. (2007, December). Network intrusion detection using Naïve Bayes. IJCSNS International Journal of Computer Science and Network Security, 7(12).
Muna, A L-Hawawreh, Moustafa, Nour, & Sitnikova, Elena. (2018). Identification of malicious activities in industrial internet of things based on deep learning models. Journal of Information Security and Applications, 41, 1–11.
Shi, Hongtao, Li, Hongping, Zhang, Dan, Cheng, Chaqiu, & Cao, Xuanxuan. (2018). An efficient feature generation approach based on deep learning and feature selection techniques for traffic classification. Computer Networks, 132, 81–98.
Kausar, N., &Taiar, R. (2016). A Disaster Management Specific Mobility Model for Flying Ad-hoc Network.
Li, W., Yi, P., Wu, Y., Pan, L., & Li, J. (2014). A new intrusion detection system based on KNN classification algorithm in WSN. Journal of Electrical and Computer Engineering, 2014 (Hindawi Publishing Corporation).
Fong, S., Li, J., Song, W., Tian, Y., Wong, R. K., & Dey, N. (2018). Predicting unusual energy consumption events from smart home sensor network by data stream mining with misclassified recall. Journal of Ambient Intelligence and Humanized Computing, 1–25.
Mukherjee, A., Keshary, V., Pandya, K., Dey, N., & Satapathy, S. C. (2018). Flying ad hoc networks: A comprehensive survey. In Information and Decision Sciences (pp. 569–580). Singapore: Springer.
Van, N. T., Thinh, T. N., & Sach, L. T. (2017). An anomaly-based network intrusion detection system using deep learning. In 2017 International Conference on System Science and Engineering (ICSSE).
Agrawal, S. K., Singh, B. P., Kumar, R., & Dey, N. (2019). Machine learning for medical diagnosis: A neural network classifier optimized via the directed bee colony optimization algorithm (pp. 197–215). Academic Press.
Juma, S., Muda, Z., Mohamed, M. A., Yassin, W. (2015, February 28). Machine learning techniques for intrusion detection system: A review. Journal of Theoretical and Applied Information Technology, 72(3).
Chowdhuri, S., Chakraborty, S., Dey, N., Chaudhuri, S. S., & Banerjee, P. (2017). Propagation analysis of MIMO ad hoc network in hybrid propagation model and implement less propagation loss algorithm to find the minimum loss route. International Journal of Information and Communication Technology, 10(1), 66–80.
Zemmal, N., Azizi, N., Dey, N., & Sellami, M. (2016). Adaptative S3VM semi supervised learning with features cooperation for breast cancer classification. Journal of Medical Imaging and Health Informatics, 957–967 (American Scientific Publishers).
Xin, Y., Kong, L., Liu, Z. (Member, IEEE), Chen, Y., Li, Y., Zhu, H., et al. (2018). Machine learning and deep learning methods for cybersecurity. IEEE Access, 6.
Shah, S. A. R., & Issac, B. (2018). Performance comparison of intrusion detection systems and application of machine learning to Snort system. Future Generation Computer Systems, 80, 157–170.
Chowdhuri, S., Roy, P., Goswami, S., Azar, A. T., & Dey, N. (2014). Rough set based ad hoc network: A review. International Journal of Service Science, Management, Engineering, and Technology (IJSSMET), 5(4), 66–76.
El Mourabit, Y., Toumanari, A., Bouirden, A., & El Moussaid, N. (2015, June 5). A comparative evaluation of intrusion detection techniques in WSN. Journal of Theoretical and Applied Information Technology, 76(1).
Chatterjee, S., Ghosh, S., Dawn, S., Hore, S., & Dey, N. (2016). Forest type classification: A hybrid NN-GA model based approach. In Information systems design and intelligent applications (pp. 227–236). India: Springer.
Kamble, P. N., & Mahalle, P. N. (2013). Decision theory based auto-delegation (DTA-d) scheme for ubiquitous computing. International Journal of Computer Applications (Foundation of Computer Science).
Alsheikh, M. A., Lin, S., Niyato, D., & Tan, H.-P. (2015, March 19) Machine Learning in WSNs: Algorithms, Strategies, and Applications. arXiv:1405.4463v2 [cs.NI].
Chowdhuri, S., Dey, N., Chakraborty, S., & Baneerjee, P. K. (2015). Analysis of performance of MIMO ad hoc network in terms of information efficiency. In Emerging ICT for Bridging the Future-Proceedings of the 49th Annual Convention of the Computer Society of India CSI (Vol. 2, pp. 43–50). Cham: Springer.
Chowdhuri, S., Chakraborty, S., Dey, N., Azar, A. T., Salem, M. A. M. M., Chaudhury, S. S., et al. (2014). Recent research on multi input multi output (MIMO) based mobile ad hoc network: A review. International Journal of Service Science, Management, Engineering, and Technology (IJSSMET), 5(3), 54–65.
Li, Z., Dey, N., Ashour, A. S., Cao, L., Wang, Y., & Wang, D. (2017). Convolutional neural network based clustering and manifold learning method for diabetic plantar pressure imaging dataset. Journal of Medical Imaging and Health Informatics (American Scientific Publishers).
Bhattacherjee, A., Roy, S., Paul, S., Roy, P., Kausar, N., & Dey, N. (2016). Classification approach for breast cancer detection using back propagation neural network: A study (pp. 210–221). IGI Global.
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Chandre, P.R., Mahalle, P.N., Shinde, G.R. (2020). Deep Learning and Machine Learning Techniques for Intrusion Detection and Prevention in Wireless Sensor Networks: Comparative Study and Performance Analysis. In: Das, S., Samanta, S., Dey, N., Kumar, R. (eds) Design Frameworks for Wireless Networks. Lecture Notes in Networks and Systems, vol 82. Springer, Singapore. https://doi.org/10.1007/978-981-13-9574-1_5
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