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BAPRP: a machine learning approach to blackhole attacks prevention routing protocol in vehicular Ad Hoc networks

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Abstract

One of the network performance challenges of Vehicular Ad Hoc Networks (VANET) is the Black Hole attack. This destructive attack severely damages the network performance if succeeded. By replying to a route request with the minimum routing cost and the maximum sequence number (SN) value, the malicious vehicle can fool the source vehicle that it has the “freshest” and best cost-effective route to the destination vehicle. As a result, all data packets intended to a legitimate destination are caught and destroyed by the malicious vehicle. This may also cause a serious risk for autonomous vehicle control system or traffic warning applications. Previous published malicious detection algorithms suffer high error rates when the malicious node uses a “close-to-legitimate” SN value to attack or actively change the information in the route reply packet. This paper proposes a Black Hole Attack Detection Algorithm (BADA) based on a machine learning approach. In VANETs, it is a challenge to differentiate the behavior of malicious vehicles as they try to avoid detection by imitating that of the normal vehicles. The BADA classification algorithm outperforms existing solutions because it relies on the history of each vehicle’s route request and response behavior, and it deploys the k-Nearest Neighbors machine learning algorithm to identify malicious vehicles. The paper also proposes a Black hole Attack Detection Routing Protocol which utilizes the proposed BADA solution to realize a more secured and improved AODV-based protocol. Using the NS2 simulation system, the paper evaluates the performance of the proposed protocol and compares it with related solutions on the traffic network model in Ho Chi Minh city, Vietnam under black hole attacks using minimum SN values. Simulation results have shown that the proposed algorithm can correctly detect the malicious node higher than 99.0%, outperforming previously published algorithms.

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References

  1. Liu, H., Li, Y., Fu, Y., et al.: Polestar++: an intelligent routing engine for national-wide public transportation. IEEE Trans. Knowl. Data Eng. 35, 6194–6208 (2023). https://doi.org/10.1109/TKDE.2022.3153711

    Article  Google Scholar 

  2. Liu, H., Han, J., Fu, Y., et al.: Unified route representation learning for multi-modal transportation recommendation with spatiotemporal pre-training. VLDB J. 32, 325–342 (2022). https://doi.org/10.1007/s00778-022-00748-y

    Article  Google Scholar 

  3. Nassar, L., Jundi, A., Golestan, K., et al.: Vehicular Ad-hoc Networks(VANETs): Capabilities, Challenges in Context-Aware Processing and Communication Gateway. Autonomous and Intelligent Systems (AIS 2012). Lecture Notes in Computer Science, Springer, Berlin, Heidelberg (2012). https://doi.org/10.1007/978-3-642-31368-4_6

    Book  Google Scholar 

  4. Jeroen, H., Ingrid, M., Bart, D., Piet, D.: An overview of Mobile Ad hoc Networks: applications and challenges. J. Commun. Netw. 3(3), 60–66 (2004). https://doi.org/10.1109/MPRV.2009.2

    Article  Google Scholar 

  5. Amani A.S., Shcherbakov, M.: A survey on routing protocols in Vehicular Ad hoc Networks. In: 9th International Conference on Information, Intelligence, Systems and Applications (IISA), pp. 1–8. IEEE, Zakynthos, Greece (2018). https://doi.org/10.1109/IISA.2018.8633700

  6. Bilgin, B.E., Gungor, C.V.: Performance comparison of IEEE 802.11p and IEEE 802.11b for vehicle-to-vehicle communications in highway, rural, and urban areas. Int. J. Veh. Technol 2013, 1–10 (2013). https://doi.org/10.1155/2013/971684

    Article  Google Scholar 

  7. Alotaibi, E., Mukherjee, B.: A survey on routing algorithms for Wireless Ad-hoc and Mesh networks. Comput. Netw. 56(2), 940–965 (2012). https://doi.org/10.1016/j.comnet.2011.10.011

    Article  Google Scholar 

  8. Sakiz, F., Sen, S.: A survey of attacks and detection mechanisms on intelligent transportation systems: VANETs and IoV. Ad Hoc Netw. 61, 33–50 (2017). https://doi.org/10.1016/j.adhoc.2017.03.006

    Article  Google Scholar 

  9. Tseng, F.H., Chou, L., Chao, H.C.: A survey of black hole attacks in wireless mobile ad hoc networks. HCIS 1(4), 1–16 (2011). https://doi.org/10.1186/2192-1962-1-4

    Article  Google Scholar 

  10. Casado, L.S., Fernández, G.M., et al.: Identification of contamination zones for Sinkhole detection in MANETs. J. Netw. Comput. Appl. 54, 62–77 (2015). https://doi.org/10.1016/j.jnca.2015.04.008

    Article  Google Scholar 

  11. Gao, X., Chen, W.: A novel Gray hole attack detection scheme for Mobile Ad-hoc Networks. In: IFIP International Conference on Network and Parallel Computing Workshops, pp. 209–214. IEEE, Dalian, China (2007). https://doi.org/10.1109/NPC.2007.88

  12. Vo, T.T., Luong, N.T., Hoang, D.: MLAMAN: a novel multi-level authentication model and protocol for preventing wormhole attack in mobile ad hoc network. Wirel. Netw. 25(7), 4115–4132 (2019). https://doi.org/10.1007/s11276-018-1734-z

    Article  Google Scholar 

  13. Luong, N.T., Vo, T.T.: Whirlwind: A new method to attack Routing Protocol in Mobile Ad hoc Network. Int. J. Netw. Secur. 19(5), 832–838 (2017). https://doi.org/10.6633/IJNS.201709.19(5).21

    Article  Google Scholar 

  14. Andrew, F., Yudha, T.A., Ahmad, S., et al.: Performance analysis of black hole attack and flooding attack AODV routing protocol on VANET (Vehicular Ad-Hoc Network). In: 8th International Conference on Cyber and IT Service Management (CITSM), pp. 1–5. IEEE, Pangkal, Indonesia (2020). https://doi.org/10.1109/CITSM50537.2020.9268789

  15. Hassan, Z., Mehmood, A., et al.: Intelligent detection of black hole attacks for secure communication in autonomous and connected vehicles. IEEE Access 8, 199618–199628 (2020). https://doi.org/10.1109/ACCESS.2020.3034327

    Article  Google Scholar 

  16. Hortelano, J., Ruiz J.C., Manzoni, P.: Evaluating the usefulness of watchdogs for intrusion detection in VANETs. In: 2010 IEEE International Conference on Communications Workshops, pp. 1–5. IEEE, Cape Town, South Africa (2010). https://doi.org/10.1109/ICCW.2010.5503946

  17. Cai, J., Yi, P., et al.: An adaptive approach to detecting black and gray hole attacks in Ad Hoc network. In: 24th IEEE International Conference on Advanced Information Networking and Applications, pp. 775–780. IEEE, Perth, WA, Australia (2010). https://doi.org/10.1109/AINA.2010.143

  18. Daeinabi, A., Rahbar, A.G.: Detection of malicious vehicles (DMV) through monitoring in Vehicular Ad-Hoc Networks. Multimed. Tools Appl. 66(2), 325–338 (2013). https://doi.org/10.1007/s11042-011-0789-y

    Article  Google Scholar 

  19. Kadam, M., Limkar, S.: Performance investigation of DMV (Detecting Malicious Vehicle) and D &PMV (Detection and prevention of misbehave/malicious vehicles): future road map. In: International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA) 2013. Advances in Intelligent Systems and Computing, Vol 247. Springer, Cham (2013). https://doi.org/10.1007/978-3-319-02931-3_43

  20. Wahab, O.A., Otrok, H., Mourad, A.: A dempster-shafer based tit-for-tat strategy to regulate the cooperation in VANET using QoS-OLSR protocol. Wirel. Pers. Commun. Vol. 75(3), 1635–1667 (2014). https://doi.org/10.1007/s11277-013-1443-y

    Article  Google Scholar 

  21. Baiad, R., Otrok, H., et al.: Cooperative cross layer detection for blackhole attack in VANET-OLSR. In: International Wireless Communications and Mobile Computing Conference (IWCMC), pp. 863–868. IEEE, Nicosia, Cyprus (2014). https://doi.org/10.1109/IWCMC.2014.6906469

  22. Gruebler, A., Maier, K.D.M., Alheeti, K.M.A.: An intrusion detection system against black hole attacks on the communication network of self-driving cars. In: Sixth International Conference on Emerging Security Technologies (EST), pp. 86–91. IEEE, Braunschweig, Germany (2015). https://doi.org/10.1109/EST.2015.10

  23. Kumar, V., Kumar, R.: An adaptive approach for detection of blackhole attack in mobile Ad hoc network. Procedia Comput. Sci. 48, 472–479 (2015). https://doi.org/10.1016/j.procs.2015.04.122

    Article  Google Scholar 

  24. Dhaka, A., Nandal, A., Dhaka, R.S.: Gray and black hole attack identification using control packets in MANETs. Procedia Comput. Sci. 54, 83–91 (2015). https://doi.org/10.1016/j.procs.2015.06.010

    Article  Google Scholar 

  25. Ndajah, P., Matine, A.O., Hounkonnou, M.N.: Black hole attack prevention in wireless peer-to-peer networks: a new strategy. Int. J. Wirel. Inf. Netw. 26(1), 48–60 (2019). https://doi.org/10.1007/s10776-018-0418-z

    Article  Google Scholar 

  26. Delkesh, T., Jabraeil Jamali, M.A.: EAODV: detection and removal of multiple black hole attacks through sending forged packets in MANETs. J. Ambient. Intell. Humaniz. Comput. 10(5), 1897–1914 (2019). https://doi.org/10.1007/s12652-018-0782-7

    Article  Google Scholar 

  27. Sivanesh, S., Dhulipala, V.R.S.: Accurate and cognitive intrusion detection system (ACIDS): A novel black hole detection mechanism in Mobile Ad Hoc Networks. Mob. Netw. Appl. 26, 1696–1704 (2021). https://doi.org/10.1007/s11036-019-01505-2

    Article  Google Scholar 

  28. Perkins, C.E., Park, M., Royer, E.M.: Ad-hoc on-demand distance vector routing. In: Proceedings of Second IEEE Workshop on Mobile Computing Systems and Applications (WMCSA), pp. 90–100 (1999). https://doi.org/10.1145/581291.581306

  29. Su, M.Y.: Prevention of selective black hole attacks on mobile ad hoc networks through intrusion detection systems. Comput. Commun. 34(1), 107–117 (2011). https://doi.org/10.1016/j.comcom.2010.08.007

    Article  Google Scholar 

  30. Jaiswal, R., Sharma, S.: A novel approach for detecting and eliminating cooperative black hole attack using advanced DRI table in Ad hoc Network. In: Advance Computing Conference (IACC), 2013 IEEE 3rd International, pp. 499–504. IEEE, Ghaziabad, India (2013). https://doi.org/10.1109/IAdCC.2013.6514276

  31. Li, W., Song, H.: ART: an attack-resistant trust management scheme for securing vehicular Ad Hoc networks. IEEE Trans. Intell. Transp. Syst. 17(4), 960–969 (2016). https://doi.org/10.1109/TITS.2015.2494017

    Article  Google Scholar 

  32. Alheeti, K.M.A., Gruebler, A., Maier, K.M.: Using discriminant analysis to detect intrusions in external communication for self-driving vehicles. Digit. Commun. Netw. 3(3), 180–187 (2017). https://doi.org/10.1016/j.dcan.2017.03.001

    Article  Google Scholar 

  33. Arulkumaran, G., Gnanamurthy, R.K.: Fuzzy trust approach for detecting black hole attack in mobile Adhoc network. Mob. Netw. Appl. 24, 386–393 (2019). https://doi.org/10.1007/s11036-017-0912-z

    Article  Google Scholar 

  34. Tyagi, P., Dembla, D.: Advanced secured routing algorithm of vehicular Ad-Hoc network. Wirel. Pers. Commun. 102(1), 41–60 (2018). https://doi.org/10.1007/s11277-018-5824-0

    Article  Google Scholar 

  35. Farahani, G.: Black hole attack detection using K-nearest neighbor algorithm and reputation calculation in Mobile Ad Hoc Networks. Secur. Commun. Netw. 2021, 1–15 (2021). https://doi.org/10.1155/2021/8814141

    Article  Google Scholar 

  36. Mota, V.F.S., Cunha, F.D., et al.: Protocols, mobility models and tools in opportunistic networks: a survey. Comput. Commun. 48(15), 5–19 (2014). https://doi.org/10.1016/j.comcom.2014.03.019

    Article  Google Scholar 

  37. Teerawat, I., Ekram, H.: Introduction to Network Simulator NS2, p. 535. Springer (2009). https://doi.org/10.1007/978-1-4614-1406-3. NS2 source code: http://www.isi.edu/nsnam/ns/

  38. Sanjib, K.S., Pankaj, K.A., Singh, P.: Modified k-NN algorithm for classification problems with improved accuracy. Int. J. Inf. Technol. 10(1), 65–70 (2018). https://doi.org/10.1007/s41870-017-0058-z

    Article  Google Scholar 

  39. Luong, N.T., Vo, T.T., Hoang, D.: FAPRP: a machine learning approach to flooding attacks prevention routing protocol in Mobile Ad Hoc Networks. Wirel. Commun. Mob. Comput. 2019, 1–17 (2019). https://doi.org/10.1155/2019/6869307

    Article  Google Scholar 

  40. Luong, N.T., Nguyen, A.Q., Hoang, D.: FAPDRP: a flooding attacks prevention and detection routing protocol in vehicular ad hoc network using behavior history and nonlinear median filter transformation. Wirel. Netw. (2022). https://doi.org/10.1007/s11276-022-03174-8

    Article  Google Scholar 

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Acknowledgements

This research is supported by the project B2021.SPD.07, The Dong Thap University, Vietnam.

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Authors and Affiliations

  1. Faculty of Mathematics and Informatics Teacher Education, Dong Thap University, Dong Thap, 870000, Vietnam

    Ngoc T. Luong

  2. Faculty of Engineering and Information Technology, The University of Technology Sydney, Sydney, 2007, Australia

    Doan Hoang

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Correspondence to Ngoc T. Luong.

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Luong, N.T., Hoang, D. BAPRP: a machine learning approach to blackhole attacks prevention routing protocol in vehicular Ad Hoc networks. Int. J. Inf. Secur. 22, 1547–1566 (2023). https://doi.org/10.1007/s10207-023-00705-y

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