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Security Model to Mitigate Black Hole Attack on Internet of Battlefield Things (IoBT) Using Trust and K-Means Clustering Algorithm


Affiliations
1 Department of Computer Applications, Karpagam Academy of Higher Education, Coimbatore, Tamil Nadu, India
 

The Internet of Things (IoT) acts an imperative part in the Battlefield Network (BN) for group-based communication. The new technology is called Internet of Battlefield Things (IoBT) that delivers intelligence services on the battlefield to soldiers and commanders equipped with smart devices. Though it provides numerous benefits, it is also susceptible to many attacks, because of the open and remote deployment of Battlefield Things (BTs). It is more critical to provide security in such networks than in commercial IoT applications because they must contend with both IoT networks and tactical battlefield environments. Because of restricted resources, an attacker may compromise the BTs. The BT that has been seized by the adversary is called a malicious BT and it may launch several security attacks on the BN. To identify these malicious BTs, the IoBT network requires a reputation-based trust model. To address the black hole attack or malicious attack over Routing Protocol for Low Power and Lossy Networks (RPL) is a key objective of the proposed work. The proposed work is the combination of both machine learning algorithm and trust management and it is named as KmCtrust model. By removing malicious BTs from the network, only BTs participating in the mission are trusted, which improves mission performance in the IoBT network. The simulation analysis of KmCtrust model has witnessed the better results in terms of various performance metrics.

Keywords

IoBT, RPL, Trust, Black Hole Attack, Multiple Regression, K-Means Clustering Algorithm, Security.
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  • Burrell, D. N. (2021). Creating Diverse and Religiously Inclusive Workplace Cultures in Hyper-Connected, Technical, and cyber-Driven Organizations. International Journal of Sociotechnology and Knowledge Development, 13(3), 17–32. doi:10.4018/ IJSKD.2021070102

  • K.Prathapchandran and T.Janani , “A trust aware security mechanism to detect sinkhole attack in RPL-based IoT environment using random forest – RFTRUST“ . Computer Networks. 198(10813), 1-20, 2021

  • Farooq, M. J., & Zhu, Q. “Secure and Reconfigurable Network Design for Critical Information Dissemination in the Internet of Battlefield Things (IoBT)”. 15th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt). doi:10.23919/wiopt.2017.7959892, 2017.

  • Ferrara, P., Mandal, A. K., Cortesi, A., & Spoto, F. (2021). Static analysis for discovering IoT vulnerabilities. International Journal of Software Tools for Technology Transfer, 23(1), 71–88. doi:10.1007/s10009-020-00592-x

  • Nobre, J., Rosario, D., Both, C., Cerqueira, E., & Gerla, M, “Toward Software-Defined Battlefield Networking”. IEEE Communications Magazine, 54(10), 152–157. doi:10.1109/mcom.2016.7588285, 2016.

  • K.Prathapchandran and T.Janani, “Decision Tree Trust (DTTrust)-Based Authentication Mechanism to Secure RPL Routing Protocol in Internet of Battlefield Thing (IoBT)”, International Journal of Business Data Communications and Networking (IJBDCN), 1(17), pp.1-23, 2021.

  • Pongle, P., & Chavan, G, “A Survey: Attacks on RPL and 6LoWPAN in IoT”. International Conference on Pervasive Computing (ICPC). doi:10.1109/pervasive.2015.7087034, 2015.

  • Kamble, A., Malemath, V. S., & Patil, D, “Security Attacks and Secure Routing Protocols in RPL-Based Internet of Things: Survey”, International Conference on Emerging Trends & Innovation in ICT (ICEI). doi:10.1109/etiict.2017.7977006, 2017.

  • Winter, T., “RPL: IPv6 Routing Protocol for Low-Power and Lossy Networks”, https://tools.ietf.org/html/rfc6550, 2012.

  • Glissa G, Rachedi, A, & Meddeb, A, “A Secure Routing Protocol-Based on RPL for Internet of Things” . 2016 IEEE Global Communications Conference (GLOBECOM). doi:10.1109/glocom.2016.7841543,2016.

  • Li, S., Iqbal, M., & Saxena, N. (2022). Future industry internet of things with zero-trust security. Information Systems Frontiers, 1-14.

  • Koohang, A., Sargent, C. S., Nord, J. H., & Paliszkiewicz, J. (2022). Internet of Things (IoT): From awareness to continued use. International Journal of Information Management, 62, 102442.

  • Marill, K. A, “Advanced Statistics: Linear Regression, Part II: Multiple Linear Regression. Academic Emergency Medicine” , 11(1), 94–102. doi: 10.1197/j.aem.2003.09.006, 2004.

  • Ng, H. P., Ong, S. H., Foong, K. W. C., Goh, P. S., & Nowinski, W. L. (n.d.). “Medical Image Segmentation Using K-Means Clustering and Improved Watershed Algorithm”, IEEE Southwest Symposium on Image Analysis and Interpretation. doi:10.1109/ssiai.2006.1633722, 2006.

  • Shanmugam, S., V., M. G., K., P., & T., J. (2022). Mitigating Black Hole Attacks in Routing Protocols Using a Machine Learning-Based Trust Model. International Journal of Socio technology and Knowledge Development (IJSKD), 14(1), 1-23. http://doi.org/10.4018/IJSKD.310067

  • Nidheesh, N., Abdul Nazeer, K. A., & Ameer, P. M. “ An Enhanced Deterministic K-Means Clustering Algorithm for Cancer Subtype Prediction from Gene Expression Data” . Computers in Biology and Medicine, 91, 213–221. doi: 10.1016/j.compbiomed.2017.10.014, 2017.

  • Cui, H., Ruan, G., Xue, J., Xie, R., Wang, L., & Feng, X, “ A Collaborative Divide-and-Conquer K-means Clustering Algorithm for Processing Large Data”, Proceedings of the 11th ACM Conference on Computing Frontiers - CF ‘14. doi:10.1145/2597917.2597918, 2014.

  • Kavitha, A., Reddy, V. B., Singh, N., Gunjan, V. K., Lakshmanna, K., Khan, A. A., & Wechtaisong, C. (2022). Security in IoT Mesh Networks based on Trust Similarity. IEEE Access, 10, 121712-121724.

  • S. Raza, L. Seitz, D. Sitenkov, G. Selander, “S3K: Scalable Security with Symmetric Keys − DTLS Key Establishment for the Internet of Things” , IEEE Trans. Autom. Sci. Eng. 13 (2016) 1270–1280, http://dx.doi.org/10.1109/TASE.2015.2511301, 2016.

  • Rutravigneshwaran, P., Anitha, G. & Prathapchandran, K. “Trust-based support vector regressive (TSVR) security mechanism to identify malicious nodes in the Internet of Battlefield Things (IoBT)”. Int J Syst Assur Eng Manag. https://doi.org/10.1007/s13198-022-01719-w, 2022

  • Hassan, T., Asim, M., Baker, T., Hassan, J., & Tariq, N. (2021). CTrust‐RPL: A control layer‐based trust mechanism for supporting secure routing in routing protocol for low power and lossy networks‐based Internet of Things applications. Transactions on Emerging Telecommunications Technologies, 32(3), e4224.

  • Sahay, G. Geethakumari, B. Mitra and V. Thejas, “Exponential Smoothing-Based Approach for Detection of Blackhole Attacks in IoT,” IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS), Indore, India, 2018, pp. 1-6, doi: 10.1109/ANTS.2018.8710073,2018.

  • Alamr, A. A., Kausar, F., Kim, J., & Seo, C, “A Secure ECC-Based RFID Mutual Authentication Protocol for Internet of Things”. The Journal of Supercomputing, 74(9), 4281–4294, 2018.

  • Patel, H. B., & Jinwala, D. C, “ Blackhole Detection in 6LoWPAN-Based Internet of Things: An Anomaly Based Approach”. TENCON 2019 - 2019 IEEE Region 10 Conference (TENCON). doi:10.1109/tencon.2019.8929491,2019.

  • Bostani, H., & Sheikhan, M, “ Hybrid of Anomaly-Based and Specification Based IDS for Internet of Things Using Unsupervised OPF Based on MapReduce Approach”. Computer Communications, 98, 52–71. doi:10.1016/j.comcom.2016.12.001, 2017.

  • Seyedi, B., & Fotohi, R, “NIASHPT: A Novel Intelligent Agent-Based Strategy Using Hello Packet Table (HPT) Function for Trust Internet of Things”. The Journal of Supercomputing. doi:10.1007/s11227-019-03143-7,2020.

  • Yahyaoui, A., Abdellatif, T., & Attia, R, “Hierarchical Anomaly-Based Intrusion Detection and Localization in IoT” 15th International Wireless Communications & Mobile Computing Conference (IWCMC). doi:10.1109/iwcmc.2019.8766574, 2019.

  • Ullah, I., & Mahmoud, Q. H, “A Two-Level Flow-Based Anomalous Activity Detection System for IoT Networks”. Electronics, 9(3), 530. doi:10.3390/electronics9030530, 2020.

  • Qureshi, K. N., Rana, S. S., Ahmed, A., & Jeon, G, “A Novel and Secure Attacks Detection Framework for Smart Cities Industrial Internet of Things”. Sustainable Cities and Society, 102343. doi: 10.1016/j.scs.2020.102343,2020.

  • Airehrour D, Gutierrez JA, Ray SK , “SecTrust-RPL: a secure trust-aware RPL Routing protocol for Internet of Things” . Future Generation Computer System. https://doi.org/10.1016/j.future.2018.03.021., 2018

  • Kandhoul, N., Dhurandher, S. K., & Woungang, I. T_CAFE: “A Trust based Security approach for Opportunistic IoT”., IET Communications. doi:10.1049/iet-com.2019.0657.2019.

  • Airehrour D, Gutierrez JA, Ray SK , “A trust-aware RPL routing protocol to detect black hole and selective forwarding attacks” . J Telecommun Digital Econ 5(1):50–69. https://doi.org/10.18080/ jtde.v5n1.88,2017.

  • Mehta, R., & Parmar, M. M, “ Trust-Based Mechanism for Securing IoT Routing Protocol RPL Against Wormhole & Grayhole Attacks” . 2018 3rd International Conference for Convergence in Technology (I2CT). doi:10.1109/i2ct.2018.8529426, 2018.

  • Lim, J., Ko, Y.-B., Kim, D., & Kim, D, “ A Stepwise Approach for Energy Efficient Trust Evaluation in Military IoT Networks”. International Conference on Information and Communication Technology Convergence (ICTC). doi:10.1109/ictc.2018.8539353, 2018.

  • Kamble, A., Malemath, V. S., & Patil, D. “Security Attacks and Secure Routing Protocols in RPL-Based Internet of Things: Survey”, International Conference on Emerging Trends & Innovation in ICT (ICEI). doi:10.1109/etiict.2017.7977006, 2017.

  • Kaur, J., Singh, G., “A Blockchain-Based Machine Learning Intrusion Detection System for Internet of Things” . In: Daimi, K., Dionysiou, I., El Madhoun, N. (eds) Principles and Practice of Blockchains. Springer, Cham. https://doi.org/10.1007/978-3-031-10507-4_650,2023.

  • Sun, Xi., Chang, G., & Li, F, “A Trust Management Model to Enhance Security of Cloud Computing Environments” , Second International Conference on Networking and Distributed Computing. doi:10.1109/icndc.2011.56. 2011.

  • Alshehri, M. D., Hussain, F., Elkhodr, M., & Alsinglawi, B. S, “A Distributed Trust Management Model for the Internet of Things (DTM-IoT)” . EAI/Springer Innovations in Communication and Computing, 1–9. doi:10.1007/978-3-319-99966-1_1, 2019.

  • Abdalla Ahmed, A. I., Ab Hamid, S. H., Gani, A., Suleman khan, & Khan, M. K, “ Trust and Reputation for Internet of Things: Fundamentals, Taxonomy, and Open Research Challenges”. Journal of Network and Computer Applications, 102409. doi:10.1016/j.jnca.2019.102409, 2019.

  • J. H. Cho, A. Swami, and I. R. Chen, “A Survey on Trust Management for Mobile Ad Hoc Networks,” IEEE Communications Surveys & Tutorials, vol. 13, no. 4, pp. 562-583. 2011.

  • Guo, J., Chen, I.-R., & Tsai, J. J. P.,“ A Survey of Trust Computation Models for Service Management in Internet of Things Systems”. Computer Communications, 97, 1–14. doi:10.1016/j.comcom.2016.10.012, 2017. How to cite this article:

  • Liqin, T., Chuang, L., & Tieguo, J. “ Quantitative Analysis of Trust Evidence in Internet” , International Conference on Communication Technology. doi:10.1109/icct.2006.342023, 2006.

  • Tripathy, B. K., Jena, S. K., Bera, P., & Das, “ An Adaptive Secure and Efficient Routing Protocol for Mobile Ad Hoc Networks”. Wireless Personal Communications. doi:10.1007/s11277-020-07423-x ,2020.

  • Wang, B., Chen, X., & Chang, W. “A Light-Weight Trust-Based QoS Routing Algorithm for Ad Hoc Networks” . Pervasive and Mobile Computing, 13, 164–180. doi:10.1016/j.pmcj.2013.06.004, 2014.

  • Wang, Y., Tian, Y., Miao, R., & Chen, W, “Heterogeneous IoTs Routing Strategy Based on Cellular Address” . IEEE International Conference on Smart Internet of Things (SmartIoT). doi:10.1109/smartiot.2018.00021,2018.

  • Shabut, A. M., Kaiser, M. S., Dahal, K. P., & Chen, W, “ A Multidimensional Trust Evaluation Model for MANETs”, Journal of Network and Computer Applications. doi:10.1016/j.jnca.2018.07.008, 2018.

  • Liang, W., Long, J., Weng, T.-H., Chen, X., Li, K.-C., & Zomaya, A. Y. “ TBRS: A trust based recommendation scheme for vehicular CPS network”. Future Generation Computer Systems. doi:10.1016/j.future.2018.09.002, 2018.

  • Iltaf, N., Ghafoor, A., & Zia, U. “ A Mechanism for Detecting Dishonest Recommendation in Indirect Trust Computation” . EURASIP Journal on Wireless Communications and Networking, 2013(1). doi:10.1186/1687-1499-2013-189,2013.

  • Airehrour, D., Gutierrez, J., & Ray, S. K, “ Securing RPL Routing Protocol from Blackhole Attacks Using a Trust-Based Mechanism” , 26th International Telecommunication Networks and Applications Conference (ITNAC). doi:10.1109/atnac.2016.7878793, 2016.


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  • Security Model to Mitigate Black Hole Attack on Internet of Battlefield Things (IoBT) Using Trust and K-Means Clustering Algorithm

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Authors

P. Rutravigneshwaran
Department of Computer Applications, Karpagam Academy of Higher Education, Coimbatore, Tamil Nadu, India
G. Anitha
Department of Computer Applications, Karpagam Academy of Higher Education, Coimbatore, Tamil Nadu, India

Abstract


The Internet of Things (IoT) acts an imperative part in the Battlefield Network (BN) for group-based communication. The new technology is called Internet of Battlefield Things (IoBT) that delivers intelligence services on the battlefield to soldiers and commanders equipped with smart devices. Though it provides numerous benefits, it is also susceptible to many attacks, because of the open and remote deployment of Battlefield Things (BTs). It is more critical to provide security in such networks than in commercial IoT applications because they must contend with both IoT networks and tactical battlefield environments. Because of restricted resources, an attacker may compromise the BTs. The BT that has been seized by the adversary is called a malicious BT and it may launch several security attacks on the BN. To identify these malicious BTs, the IoBT network requires a reputation-based trust model. To address the black hole attack or malicious attack over Routing Protocol for Low Power and Lossy Networks (RPL) is a key objective of the proposed work. The proposed work is the combination of both machine learning algorithm and trust management and it is named as KmCtrust model. By removing malicious BTs from the network, only BTs participating in the mission are trusted, which improves mission performance in the IoBT network. The simulation analysis of KmCtrust model has witnessed the better results in terms of various performance metrics.

Keywords


IoBT, RPL, Trust, Black Hole Attack, Multiple Regression, K-Means Clustering Algorithm, Security.

References





DOI: https://doi.org/10.22247/ijcna%2F2023%2F218514