Use of Smart Intrusion Detection System for Enhancing the Security in Hierarchical Wireless Sensor Network

Rahul Das; Dr. Mona Dwivedi

doi:10.21276/ijircst.2021.9.5.2

Abstract

Trusted environment provides safety measures for the sensor network. There are many problems that occur during the management of resources. Memory management and computation overhead or CPU usage are the major issues. Security issues is another problem in Wireless sensor network.. The three types of issues like issues of memory, computation overhead and intrusion detection is considered in this proposed research. The proposed model has provided a mechanism for resource management in a wireless sensor network. This model would also resolve one diff type of issue like computation overhead. The proposed work is capable to classify IDS attacks using a deep learning model. For improving accuracy a Network model is proposed during intrusion detection using a recurring neural network. The network model is used for testing by the help of a confusion matrix to calculate The accuracy, precision, recall and fscore. For clustered wireless sensor networks the advanced adaptive and dual data communication trust scheme (adct) have been used. Research is able to handle untrustworthy nodes inefficient manner. A function that is used in this research to assess direct trust among nodes is called Adaptive trust function. In intracluster as well as intercluster this Trust mechanism ADCT is used. With the help of compression mechanism Packets have been compressed for reducing the computation overhead. For security in Intrusion, this proposed model would also be capable. Moreover, research work would also deal with selfish nodes and malicious nodes to provide better of service for network lifetime for different network sizes.

Keywords

Wireless Sensor Network, IDS, intrusion detection system, Trust management, LSTM

References

Momani, M., & Challa, S. (2010). Survey of trust models in different network domains. arXiv preprint arXiv:1010.0168.
Han, G., Jiang, J., Shu, L., Niu, J., & Chao, H. C. (2014). Management and applications of trust in Wireless Sensor Networks: A survey. Journal of Computer and System Sciences, 80(3), 602-617.
Karthik, S., Vanitha, K., & Radhamani, G. (2011, February). Trust management techniques in wireless sensor networks: an evaluation. In 2011 International Conference on Communications and Signal Processing (pp. 328-330). IEEE.
Dhulipala, V. S., & Karthik, N. (2017). Trust management technique in wireless sensor networks: challenges and issues for reliable communication: a review. CSI Transactions on ICT, 5(3), 281-294.
Bao, F., Chen, R., Chang, M., & Cho, J. H. (2012). Hierarchical trust management for wireless sensor networks and its applications to trust-based routing and intrusion detection. IEEE transactions on network and service management, 9(2), 169-183.
Li, Wenchao, Ping Yi, Yue Wu, Li Pan, and Jianhua Li. "A new intrusion detection system based on KNN classification algorithm in wireless sensor network." Journal of Electrical and Computer Engineering 2014 (2014).
Buczak, Anna L., and Erhan Guven. "A survey of data mining and machine learning methods for cyber security intrusion detection." IEEE Communications surveys & tutorials 18, no. 2 (2015): 1153-1176.
A.Javaid, Ahmad, Quamar Niyaz, Weiqing Sun, and Mansoor Alam. "A deep learning approach for network intrusion detection system." In Proceedings of the 9th EAI International Conference on Bio-inspired Information and Communications Technologies (formerly BIONETICS), pp. 21-26. 2016.
Tang, Tuan A., Lotfi Mhamdi, Des McLernon, Syed Ali Raza Zaidi, and Mounir Ghogho. "Deep learning approach for network intrusion detection in software defined networking." In 2016 International Conference on Wireless Networks and Mobile Communications (WINCOM), pp. 258-263. IEEE, 2016.
M. Sheikhan, Z. Jadidi, and A. Farrokhi, ‘‘Intrusion detection with support of reduced-size RNN dependent on feature grouping,’’NeuralComput. Appl.,vol. 21, no. 6, pp. 1185–1190, Sep. 2012.
Tavallaee, Mahbod, Ebrahim Bagheri, Wei Lu, and Ali A. Ghorbani. "A detailed analysis of the KDD CUP 99 data set." In 2009 IEEE symposium on computational intelligence for security and defense applications, pp. 1-6. IEEE, 2009..
Revathi, S., and A. Malathi. "A detailed analysis on NSL-KDD dataset using various machine learning techniques for intrusion detection." International Journal of Engineering Research & Technology (IJERT) 2, no. 12 (2013): 1848-1853..
Paulauskas, Nerijus, and Juozas Auskalnis. "Analysis of data pre-processing influence on intrusion detection using NSL-KDD dataset." In 2017 open conference of electrical, electronic and information sciences (eStream), pp. 1-5. IEEE, 2017..
Bhattacharjee, Partha Sarathi, Abul Kashim Md Fujail, and Shahin Ara Begum. "Intrusion detection system for NSL-KDD data set using vectorised fitness function in genetic algorithm." Adv. Comput. Sci. Technol. 10, no. 2 (2017): 235-246..
Ashfaq, Rana Aamir Raza, Xi-Zhao Wang, Joshua Zhexue Huang, Haider Abbas, and Yu-Lin He. "Fuzziness based semi-supervised learning approach for intrusion detection system." Information Sciences 378 (2017): 484-497..
Martens, James, and Ilya Sutskever. "Learning recurrent neural networks with hessian-free optimization." In Proceedings of the 28th international conference on machine learning (ICML-11), pp. 1033-1040. 2011..
Pichotta, Karl, and Raymond J. Mooney. "Learning Statistical Scripts with LSTM Recurrent Neural Networks." In AAAI, pp. 2800-2806. 2016.
Khan, Muhammad Ashfaq, Md Karim, and Yangwoo Kim. "A scalable and hybrid intrusion detection system based on the convolutional-LSTM network." Symmetry 11, no. 4 (2019): 583..
Althubiti, S. A., Jones, E. M., & Roy, K. (2018, November). Lstm for anomaly-dependent network intrusion detection. In 2018 28th International Telecommunication Networks and Applications Conference (ITNAC) (pp. 1-3). IEEE.
Kim, Chanho, Fuxin Li, and James M. Rehg. "Multi-object tracking with neural gating using bilinear lstm." In Proceedings of the European Conference on Computer Vision (ECCV), pp. 200-215. 2018.
Bansal, Ashu. "Ddr scheme and lstm rnn algorithm for building an efficient ids." PhD diss., 2018..
Zhao, Zheng, Weihai Chen, Xingming Wu, Peter CY Chen, and Jingmeng Liu. "LSTM network: a deep learning approach for short-term traffic forecast." IET Intelligent Transport Systems 11, no. 2 (2017): 68-75..
Tan, Ming, Cicero dos Santos, Bing Xiang, and Bowen Zhou. "Lstm-based deep learning models for non-factoid answer selection." arXiv preprint arXiv:1511.04108 (2015)..
Gensler, André, Janosch Henze, Bernhard Sick, and Nils Raabe. "Deep Learning for solar power forecasting—An approach using AutoEncoder and LSTM Neural Networks." In 2016 IEEE international conference on systems, man, and cybernetics (SMC), pp. 002858-002865. IEEE, 2016..
Sun, Lei, Jun Du, Li-Rong Dai, and Chin-Hui Lee. "Multiple-target deep learning for LSTM-RNN based speech enhancement." In 2017 Hands-free Speech Communications and Microphone Arrays (HSCMA), pp. 136-140. IEEE, 2017..
Li, Peisong, and Ying Zhang. "A Novel Intrusion Detection Method for Internet of Things." In 2019 Chinese Control And Decision Conference (CCDC), pp. 4761-4765. IEEE, 2019..
Yin, Chuanlong, Yuefei Zhu, Jinlong Fei, and Xinzheng He. "A deep learning approach for intrusion detection using recurrent neural networks." Ieee Access 5 (2017): 21954-21961..
Dong, Bo, and Xue Wang. "Comparison deep learning method to traditional methods using for network intrusion detection." In 2016 8th IEEE International Conference on Communication Software and Networks (ICCSN), pp. 581-585. IEEE, 2016.
Althubiti, Sara A., Eric Marcell Jones, and Kaushik Roy. "Lstm for anomaly-based network intrusion detection." In 2018 28th International Telecommunication Networks and Applications Conference (ITNAC), pp. 1-3. IEEE, 2018..
Li, Wenchao, Ping Yi, Yue Wu, Li Pan, and Jianhua Li. "A new intrusion detection system based on KNN classification algorithm in wireless sensor network." Journal of Electrical and Computer Engineering 2014 (2014)..
Buczak, Anna L., and Erhan Guven. "A survey of data mining and machine learning methods for cyber security intrusion detection." IEEE Communications surveys & tutorials 18, no. 2 (2015): 1153-1176..
Javaid, Ahmad, Quamar Niyaz, Weiqing Sun, and Mansoor Alam. "A deep learning approach for network intrusion detection system." In Proceedings of the 9th EAI International Conference on Bio-inspired Information and Communications Technologies (formerly BIONETICS), pp. 21-26. 2016..
Tang, Tuan A., Lotfi Mhamdi, Des McLernon, Syed Ali Raza Zaidi, and Mounir Ghogho. "Deep learning approach for network intrusion detection in software defined networking." In 2016 International Conference on Wireless Networks and Mobile Communications (WINCOM), pp. 258-263. IEEE, 2016..
M. Sheikhan, Z. Jadidi, and A. Farrokhi, ‘‘Intrusion detection with support of reduced-size RNN dependent on feature grouping,’’NeuralComput. Appl.,vol. 21, no. 6, pp. 1185–1190, Sep. 2012.
Tavallaee, Mahbod, Ebrahim Bagheri, Wei Lu, and Ali A. Ghorbani. "A detailed analysis of the KDD CUP 99 data set." In 2009 IEEE symposium on computational intelligence for security and defense applications, pp. 1-6. IEEE, 2009..
Revathi, S., and A. Malathi. "A detailed analysis on NSL-KDD dataset using various machine learning techniques for intrusion detection." International Journal of Engineering Research & Technology (IJERT) 2, no. 12 (2013): 1848-1853..
Paulauskas, Nerijus, and Juozas Auskalnis. "Analysis of data pre-processing influence on intrusion detection using NSL-KDD dataset." In 2017 open conference of electrical, electronic and information sciences (eStream), pp. 1-5. IEEE, 2017..
Bhattacharjee, Partha Sarathi, Abul Kashim Md Fujail, and Shahin Ara Begum. "Intrusion detection system for NSL-KDD data set using vectorised fitness function in genetic algorithm." Adv. Comput. Sci. Technol. 10, no. 2 (2017): 235-246..
Ashfaq, Rana Aamir Raza, Xi-Zhao Wang, Joshua Zhexue Huang, Haider Abbas, and Yu-Lin He. "Fuzziness based semi-supervised learning approach for intrusion detection system." Information Sciences 378 (2017): 484-497..
Martens, James, and Ilya Sutskever. "Learning recurrent neural networks with hessian-free optimization." In Proceedings of the 28th international conference on machine learning (ICML-11), pp. 1033-1040. 2011..
Song, S., Huang, H., &Ruan, T. (2019). Abstractive text summarization with support of LSTM-CNN dependent deep learning. Multimedia Tools and Applications, 78(1), 857-875.
Liu, Hui, Xiwei Mi, and Yanfei Li. "Smart multi-step deep learning model for wind speed forecasting based on variational mode decomposition, singular spectrum analysis, LSTM network and ELM." Energy Conversion and Management 159 (2018): 54-64..
Lu, Na, Yidan Wu, Li Feng, and Jinbo Song. "Deep learning for fall detection: Three-dimensional CNN combined with LSTM on video kinematic data." IEEE journal of biomedical and health informatics 23, no. 1 (2018): 314-323..
Mohan, Arvind T., and Datta V. Gaitonde. "A deep learning based approach to reduced order modeling for turbulent flow control using LSTM neural networks." arXiv preprint arXiv:1804.09269 (2018)..
Reddy, Bhargava K., and Dursun Delen. "Predicting hospital readmission for lupus patients: An RNN-LSTM-based deep-learning methodology." Computers in biology and medicine 101 (2018): 199-209..
Mohan, Arvind, Don Daniel, Michael Chertkov, and Daniel Livescu. "Compressed convolutional LSTM: An efficient deep learning framework to model high fidelity 3D turbulence." arXiv preprint arXiv:1903.00033 (2019)..
Chen, Jinyin, Jian Zhang, Xuanheng Xu, Chenbo Fu, Dan Zhang, Qingpeng Zhang, and Qi Xuan. "E-lstm-d: A deep learning framework for dynamic network link prediction." IEEE Transactions on Systems, Man, and Cybernetics: Systems (2019)..
Abdullah, Malak, Mirsad Hadzikadicy, and Samira Shaikhz. "SEDAT: sentiment and emotion detection in Arabic text using CNN-LSTM deep learning." In 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 835-840. IEEE, 2018..
Balouji, Ebrahim, Irene YH Gu, Math HJ Bollen, Azam Bagheri, and Mahmood Nazari. "A LSTM-based deep learning method with application to voltage dip classification." In 2018 18th International Conference on Harmonics and Quality of Power (ICHQP), pp. 1-5. IEEE, 2018...
Shen, Shui-Long, Pierre Guy Atangana Njock, Annan Zhou, and Hai-Min Lyu. "Dynamic prediction of jet grouted column diameter in soft soil using Bi-LSTM deep learning." Acta Geotechnica (2020): 1-13..
Prakash, Aaditya, Sadid A. Hasan, Kathy Lee, Vivek Datla, Ashequl Qadir, Joey Liu, and Oladimeji Farri. "Neural paraphrase generation with stacked residual lstm networks." arXiv preprint arXiv:1610.03098 (2016)..
Tong, Weitian, Lixin Li, Xiaolu Zhou, Andrew Hamilton, and Kai Zhang. "Deep learning PM 2.5 concentrations with bidirectional LSTM RNN." Air Quality, Atmosphere & Health 12, no. 4 (2019): 411-423..
Mohan, Arvind, Don Daniel, Michael Chertkov, and Daniel Livescu. "Compressed convolutional LSTM: An efficient deep learning framework to model high fidelity 3D turbulence." arXiv preprint arXiv:1903.00033 (2019)..
Wang, Fei, Yili Yu, Zhanyao Zhang, Jie Li, Zhao Zhen, and Kangping Li. "Wavelet decomposition and convolutional LSTM networks based improved deep learning model for solar irradiance forecasting." Applied Sciences 8, no. 8 (2018): 1286..
Bouktif, Salah, Ali Fiaz, Ali Ouni, and Mohamed Adel Serhani. "Multi-sequence LSTM-RNN deep learning and metaheuristics for electric load forecasting." Energies 13, no. 2 (2020): 391..
Wang, Ziyi, Aiying Yang, Peng Guo, and Pinjing He. "OSNR and nonlinear noise power estimation for optical fiber communication systems using LSTM based deep learning technique." Optics express 26, no. 16 (2018): 21346-21357..
Liu, Yangdong, Yizhe Wang, Xiaoguang Yang, and Linan Zhang. "Short-term travel time prediction by deep learning: a comparison of different LSTM-DNN models." In 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), pp. 1-8. IEEE, 2017..
Kumar, Sumit, Lasani Hussain, Sekhar Banarjee, and Motahar Reza. "Energy load forecasting using deep learning approach-LSTM and GRU in spark cluster." In 2018 Fifth International Conference on Emerging Applications of Information Technology (EAIT), pp. 1-4. IEEE, 2018..
Yuan, Xiaofeng, Lin Li, Yuri Shardt, Yalin Wang, and Chunhua Yang. "Deep learning with spatiotemporal attention-based LSTM for industrial soft sensor model development." IEEE Transactions on Industrial Electronics (2020)..
https://datasilk.com/intrusion-detection-prevention
https://towardsdatascience.com/training-deep-neural-networks-9fdb1964b964
https://adventuresinmachinelearning.com/keras-lstm-tutorial
https://www.researchgate.net/profile/Syariful_Shamsudin/publication/299390844/figure/fig5/AS:650029635235894@1531990550259/The-representation-of-neural-network-training-process.png
Royston, Patrick. "Approximating the Shapiro-Wilk W-test for non-normality." Statistics and computing 2, no. 3 (1992): 117-119.

Cites this article as

R. Das, D. M. Dwivedi, "Use of Smart Intrusion Detection System for Enhancing the Security in Hierarchical Wireless Sensor Network", International Journal of Innovative Research in Computer Science and Technology (IJIRCST), Vol-9, no.5, pp.14-23, 2021. Available from: https://doi.org/10.21276/ijircst.2021.9.5.2

Corresponding Author

Rahul Das

Research Scholar, Department of Computer Science, Mansarovar Global University, Madhya Pradesh, India (rahul.rr.das@gmail.com )

Download Full Paper

Download PDF

No. of Downloads: 53 | No. of Views: 5318

Advancing Surgical Imaging with cGAN for Effective Defogging

Lingxi Xiao, Ruilin Xu, Yiru Cang, Yan Chen, Yijing Wei.

May 2024 - Vol 12, Issue 3
Intelligent Plant Disease Diagnosis Using Machine Learning Techniques- A Review

Anuj Kumar Kem, Ayush Chauhan, Mohan Agnihotri, Aniruddh Kumar.

May 2024 - Vol 12, Issue 3
Optimizing Real-Time Object Detection- A Comparison of YOLO Models

Pravek Sharma, Dr. Rajesh Tyagi, Dr. Priyanka Dubey.

May 2024 - Vol 12, Issue 3

IJIRCST

Use of Smart Intrusion Detection System for Enhancing the Security in Hierarchical Wireless Sensor Network

Citations

Download Full Paper PDF

Total View 5318

Total Download 53