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Emerging network communication for malicious node detection in wireless multimedia sensor networks

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Abstract

Wireless sensor networks (WMSNs) are becoming increasingly popular in many fields, from academia to transportation, environmental monitoring, wildlife preservation, and military espionage. Therefore, examining potential threats, power consumption, vulnerability recognition, and systemic vulnerability characteristics is essential to develop a reliable information security approach for WSNs. As a result, it is becoming increasingly crucial for the technical community to conduct intrusion recognition method evaluations. Since this is the case, using deep learning techniques in creating intrusion identification and mitigation systems for wireless multimedia sensor networks is essential. This article examines how well different machine learning and deep learning algorithms perform in attack identification systems. Testing the efficacy of different methods on the WMSN-DS database through experimentation is essential. In this work, we combine the power of a Convolutional Neural Network classifier with a Random Forest. To accomplish this, a Convolutional Neural Network with a Random Forest Classifier is used. The intrusion detection system (IDS) is a crucial technique proposed in this study for WMSN. To address this issue, the current study proposal uses deep Learning with a Random Forest classifier to detect and prevent attacks and to promote efficient forwarding in WMSNs. Multiple WMSN assaults have been investigated, and the results of these investigations have been critically evaluated.

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Data availability

The de-identified data supporting the conclusions of our research are available from the corresponding authors, without undue reservation, to qualified researchers.

Abbreviations

WMSN:

Wireless multimedia sensor networks

WSN:

Wireless sensor network

CNN:

Convolutional neural networks

IDS:

Intrusion detection system

ANN:

Artificial neural network

RF:

Random forest

QoS:

Quality of service

DoS:

Denial of service

CAN:

Controller area network

IVN:

In-vehicle Network

PCA:

Principal component analysis

ROC:

Receiver operating curves

References

  • Agrawal, S., Singh, B., Kumar, R., Dey, N.: Machine Learning for Medical Diagnosis: A Neural Network Classifier Optimized via the Directed Bee Colony Optimization Algorithm. In: U-Healthcare Monitoring Systems, pp. 197–215. Elsevier, Amsterdam (2019). https://doi.org/10.1016/B978-0-12-815370-3.00009-8

    Chapter  Google Scholar 

  • Almomani, I., Al-Kasasbeh, B., AL-Akhras, M.: WSN-DS: a dataset for intrusion detection systems in wireless sensor networks. J. Sens. 2016, 1–16 (2016). https://doi.org/10.1155/2016/4731953

    Article  Google Scholar 

  • Arockia Jayadhas, S., Emalda, R.: An analysis on routing procedures in multimedia wireless sensor networks. In: International Conference on Communication Computing and Internet of things (IC3IoT) (2018)

  • Arockia Jayadhas, S., Emalda, R.: Link failure detection in multimedia sensor networks using multi-tier clustering based VGG-CNN classification approach. IJCNA Journal 8(6) (2019). https://doi.org/10.22247/ijcna/2021/210719

  • Cardei, M., Fernandez, E. B., Sahu, A., Cardei, I.: A pattern for sensor network architectures. In: Proceedings of the 2nd Asian Conference on Pattern Languages of Programs-AsianPLoP ‘11, Tokyo, Japan, (2011), pp. 1–8. https://doi.org/10.1145/2524629.2524641

  • Chatterjee, S., Ghosh, S., Dawn, S., Hore, S., Dey, N.: Forest Type Classification: A Hybrid NN-GA Model-Based Approach. In: Satapathy, S.C., Mandal, J.K., Udgata, S.K., Bhateja, V. (eds.) Information Systems Design and Intelligent Applications, vol. 435, pp. 227–236. New Delhi, Springer India (2016). https://doi.org/10.1007/978-81-322-2757-1_23

    Chapter  Google Scholar 

  • Chhaya, L., Sharma, P., Bhagwatikar, G., Kumar, A.: Wireless sensor network based smart grid communications: cyber attacks, intrusion detection system and topology control. Electronics 6(1), 5 (2017). https://doi.org/10.3390/electronics6010005

    Article  Google Scholar 

  • El Mourabit, Y., Bouirden, A., Toumanari, A., Moussaid, N.E.: Intrusion detection techniques in wireless sensor network using data mining algorithms: comparative evaluation based on attacks detection. Int. J. Adv. Comput. Sci. Appl. 6(9), 164–172 (2015). https://doi.org/10.14569/IJACSA.2015.060922

    Article  Google Scholar 

  • Farhan, B.I., Jasim, A.D.: Performance analysis of intrusion detection for deep learning model based on CSE-CIC-IDS2018 dataset. Indones. J. Electr. Eng. Comput. Sci. 26(2), 1165 (2022). https://doi.org/10.11591/ijeecs.v26.i2.pp1165-1172

    Article  Google Scholar 

  • Farooq, Y, Beenish, H., Fahad, M.: Intrusion detection system in wireless sensor networks-a comprehensive survey. In: 2019 Second International Conference on Latest Trends in Electrical Engineering and Computing Technologies (INTELLECT), Karachi, Pakistan, (2019), pp. 1–6. https://doi.org/10.1109/INTELLECT47034.2019.8954984

  • Ghugar, U., Pradhan, J.: A review on Wormhole attacks in wireless sensor networks. Int. J. Inf. Commun. Technol. Digital Converg. 4(1), 32–45 (2019)

    Google Scholar 

  • Godala, S., Vaddella, R.P.V.: A study on intrusion detection system in wireless sensor networks. Int. J. Commun. Netw. Inf. Secur. 12(1), 127–141 (2020). https://doi.org/10.17762/ijcnis.v12i1.4429

    Article  Google Scholar 

  • Gurumekala, T., Senthil Sivakumar, M., Sundaram, A., Arputharaj, T.: Enhanced fuzzy based clustering approach for improving reliability of WSNs. Int. J. Appl. Eng. Res. 10(55), 1314–1319 (2015a)

    Google Scholar 

  • Gurumekala, T., Senthil Sivakumar, M., Sundaram, A., Arputharaj, T.: Identification of high throughput path in WMNs using novel routing metric. Int. J. Appl. Eng. Res. 10(55), 1278–1283 (2015b)

    Google Scholar 

  • Hachimi, M., Kaddoum, G., Gagnon, G., Illy, P.: Multi-stage jamming attacks detection using deep learning combined with kernelized support vector machine in 5G cloud radio access networks. arXiv, (2020). Accessed: Jul. 29, 2022. [Online]. Available: http://arxiv.org/abs/2004.06077

  • Intanagonwiwat, C., Govindan, R., Estrin, D., Heidemann, J., Silva, F.: Directed diffusion for wireless sensor networking. IEEE ACM Trans. Netw. 11(1), 2–16 (2003). https://doi.org/10.1109/TNET.2002.808417

    Article  Google Scholar 

  • Javaid, A., Niyaz, Q., Sun, W., Alam, M.: A deep learning approach for network intrusion detection system. New York City, United States, (2016).https://doi.org/10.4108/eai.3-12-2015.2262516

  • Jayadhas, S.A., Roslin, S.E.: Performance analysis of malicious node detection in wireless multimedia sensor networks using modified LeNET architecture. Int. J. Comput. Netw. Appl. 9(2), 179–188 (2022). https://doi.org/10.22247/ijcna/2022/212334

    Article  Google Scholar 

  • Lansky, J., et al.: Deep learning-based intrusion detection systems: a systematic review. IEEE Access 9, 101574–101599 (2021). https://doi.org/10.1109/ACCESS.2021.3097247

    Article  Google Scholar 

  • Masoud, M.Z., Jaradat, Y., Jannoud, I., Al Sibahee, M.A.: A hybrid clustering routing protocol based on machine learning and graph theory for energy conservation and hole detection in wireless sensor network. Int. J. Distrib. Sensor Netw. 15(6), 1550147719858231 (2019)

    Article  Google Scholar 

  • Mehedi, S.T., Anwar, A., Rahman, Z., Ahmed, K.: Deep transfer learning based intrusion detection system for electric vehicular networks. Sensors 21(14), 4736 (2021). https://doi.org/10.3390/s21144736

    Article  PubMed  PubMed Central  ADS  Google Scholar 

  • Mohammed, L.A., Issac, B.: Detailed DoS attacks in wireless networks and countermeasures. Int. J. Ad Hoc Ubiquitous Comput. 2(3), 157–166 (2007). https://doi.org/10.1504/IJAHUC.2007.012417

    Article  Google Scholar 

  • Mukherjee, P., Sen, S.: Using Learned Data Patterns to Detect Malicious Nodes in Sensor Networks. In: Rao, S., Chatterjee, M., Jayanti, P., Murthy, C.S.R., Saha, S.K. (eds.) Distributed Computing and Networking, vol. 4904, pp. 339–344. Springer, Berlin (2008). https://doi.org/10.1007/978-3-540-77444-0_35

    Chapter  Google Scholar 

  • Naveena, A., Lakshmi, M.V.: (2022) A heuristic deep feature system for energy management in wireless sensor network. Review. https://doi.org/10.21203/rs.3.rs-1648588/v1

  • Sedjelmaci, H., Feham, M.: Novel hybrid intrusion detection system for clustered wireless sensor network. Int. J. Netw. Secure. Appl. 3(4), 1–14 (2011). https://doi.org/10.5121/ijnsa.2011.3401

    Article  Google Scholar 

  • Senthil Sivakumar, M., Gurumekala, T., Sundaram, A., Banupriya, M., Arputharaj, T.: Design of novel AES processor for high speed next generation internet security. Int. J. Appl. Eng. Res. 10(55), 389–393 (2015)

    Google Scholar 

  • Shelke, M.P., Malhotra, A., Mahalle, P.: A packet priority intimation-based data transmission for congestion-free traffic management in wireless sensor networks. Comput. Electr. Eng. 64, 248–261 (2017). https://doi.org/10.1016/j.compeleceng.2017.03.007

    Article  Google Scholar 

  • Yuanli, W., Xianghui, L., Jianping, Y.: Requirements of quality of service in wireless sensor network. In: International Conference on Networking, International Conference on Systems and International Conference on Mobile Communications and Learning Technologies (ICNICONSMCL’06), Morne, Mauritius, (2006), pp. 116–116. https://doi.org/10.1109/ICNICONSMCL.2006.185

  • Zahariadis, T., Leligou, H.C., Trakadas, P., Voliotis, S.: Trust management in wireless sensor networks. Eur. Trans. Telecommun. 21(4), 386–395 (2010)

    Article  Google Scholar 

  • Zamani, M., Movahedi, M.: Machine learning techniques for intrusion detection. arXiv, (2015). Accessed: Aug. 03, 2022. [Online]. Available: http://arxiv.org/abs/1312.2177

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Acknowledgements

This research did not receive any specific grant from funding agencies in the public, commercial, or non-profit sectors.

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Authors and Affiliations

  1. Faculty of Electronics, Sathyabama Institute of Science and Technology, Chennai, 600119, India

    S. Arockia Jayadhas

  2. Department of Electronics and Communication Engineering, Sathyabama Institute of Science and Technology, Chennai, 600119, India

    S. Emalda Roslin

  3. Department of Electrical, Electronics and Communication Engineering, St. Joseph University in Tanzania, Dar es Salaam, Tanzania

    W. Florin

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  1. S. Arockia Jayadhas
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  2. S. Emalda Roslin
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  3. W. Florin
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The author has proposed a deep Learning technique Convolutional Neural Network with a Random Forest classifier to detect and prevent attacks and to promote efficient forwarding in WMSNs.

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Correspondence to S. Arockia Jayadhas.

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Jayadhas, S.A., Roslin, S.E. & Florin, W. Emerging network communication for malicious node detection in wireless multimedia sensor networks. Opt Quant Electron 56, 59 (2024). https://doi.org/10.1007/s11082-023-05659-y

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