Abstract
VANET is a crucial part of the intelligent transport system (ITS). VANET helps the vehicle nodes to exchange important and life-saving information, so any attack on VANET should be detected fast. The DDoS attack is one of the cyber-attacks that attack the availability of the VANET systems. Due to the DDoS attack vehicle nodes are not capable to exchange valuable information. In this chapter, we propose a fog-based DDoS detection approach that uses fuzzy logic to differentiate attack traffic from normal traffic in 5G-enabled smart cities. The proposed approach achieves more than 90% precision and true negative rate, it indicates that our proposed approach correctly identifies the DDoS attack traffic.
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References
Abd EL-Latif, A.A., Abd-El-Atty, B., Venegas-Andraca, S.E., Mazurczyk, W.: Efficient quantum-based security protocols for information sharing and data protection in 5G networks. Future Gener. Comput. Syst. 100, 893–906 (2019)
Abou-Nassar, E.M., Iliyasu, A.M., El-Kafrawy, P.M., Song, O.Y., Bashir, A.K., Abd El-Latif, A.A.: Ditrust chain: towards blockchain-based trust models for sustainable healthcare IoT systems. IEEE Access 8, 111223–111238 (2020)
Adhikary, K., Bhushan, S., Kumar, S., Dutta, K.: Hybrid algorithm to detect DDoS attacks in vanets. Wirel. Person. Commun. 1–22 (2020)
Ahmed, S.H., Rani, S.: A hybrid approach, smart street use case and future aspects for internet of things in smart cities. Future Gener. Comput. Syst. 79, 941–951 (2018)
Al-Nawasrah, A., Almomani, A.A., Atawneh, S., Alauthman, M.: A survey of fast flux botnet detection with fast flux cloud computing. Int. J. Cloud Appl. Comput. (IJCAC) 10(3), 17–53 (2020)
Al-Turjman, F.: 5G-enabled devices and smart-spaces in social-IoT: an overview. Future Gener. Comput. Syst. 92, 732–744 (2019)
Alomari, E., Manickam, S., Gupta, B., Karuppayah, S., Alfaris, R.: Botnet-based distributed denial of service (DDoS) attacks on web servers: classification and art (2012). arXiv preprint arXiv:1208.0403
AlZu’bi, S., Shehab, M., Al-Ayyoub, M., Jararweh, Y., Gupta, B.: Parallel implementation for 3D medical volume fuzzy segmentation. Pattern Recogn. Lett. 130, 312–318 (2020)
Badve, O.P., Gupta, B.: Taxonomy of recent DDoS attack prevention, detection, and response schemes in cloud environment. In: Proceedings of the International Conference on Recent Cognizance in Wireless Communication & Image Processing, pp. 683–693. Springer (2016)
Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: Sumo—simulation of urban mobility: an overview. In: Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation. ThinkMind (2011)
Bello, O., Zeadally, S.: Toward efficient smartification of the internet of things (IoT) services. Future Gener. Comput. Syst. 92, 663–673 (2019)
Benadda, M., Belalem, G.: Improving road safety for driver malaise and sleepiness behind the wheel using vehicular cloud computing and body area networks. Int. J. Softw. Sci. Comput. Intell. (IJSSCI) 12(4), 19–41 (2020)
Bhushan, K., Gupta, B.B.: Distributed denial of service (DDoS) attack mitigation in software defined network (SDN)-based cloud computing environment. J. Ambient Intell. Hum. Comput. 10(5), 1985–1997 (2019)
Bonomi, F., Milito, R., Zhu, J., Addepalli, S.: Fog computing and its role in the internet of things. In: Proceedings of the First Edition of the MCC Workshop on Mobile Cloud Computing. pp. 13–16 (2012)
Chaudhary, R., Kumar, N., Zeadally, S.: Network service chaining in fog and cloud computing for the 5G environment: data management and security challenges. IEEE Commun. Mag. 55(11), 114–122 (2017)
Chen, J., Ran, X.: Deep learning with edge computing: a review. Proc. IEEE 107(8), 1655–1674 (2019)
Chettri, L., Bera, R.: A comprehensive survey on internet of things (IoT) toward 5D wireless systems. IEEE Internet Things J. 7(1), 16–32 (2019)
Chhabra, M., Gupta, B., Almomani, A.: A novel solution to handle DDoS attack in manet (2013)
Cui, J., Wei, L., Zhong, H., Zhang, J., Xu, Y., Liu, L.: Edge computing in vanets-an efficient and privacy-preserving cooperative downloading scheme. IEEE J. Sel. Areas Commun. 38(6), 1191–1204 (2020)
Dahiya, A., Gupta, B.: Multi attribute auction based incentivized solution against DDoS attacks. Comput. Secur. 92, 101763 (2020)
Dao, N.N., Park, M., Kim, J., Paek, J., Cho, S.: Resource-aware relay selection for inter-cell interference avoidance in 5G heterogeneous network for internet of things systems. Future Gener. Comput. Syst. 93, 877–887 (2019)
Elgendy, I.A., Zhang, W.Z., He, H., Gupta, B.B., Abd El-Latif, A.A.: Joint computation offloading and task caching for multi-user and multi-task MEC systems: reinforcement learning-based algorithms. Wirel. Netw. 1–16 (2021)
Erskine, S.K., Elleithy, K.M.: Secure intelligent vehicular network using fog computing. Electronics 8(4), 455 (2019)
Fang, D., Qian, Y., Hu, R.Q.: Security for 5G mobile wireless networks. IEEE Access 6, 4850–4874 (2017)
Ferrag, M.A., Maglaras, L., Argyriou, A., Kosmanos, D., Janicke, H.: Security for 4G and 5G cellular networks: a survey of existing authentication and privacy-preserving schemes. J. Netw. Comput. Appl. 101, 55–82 (2018)
Gaurav, A., Gupta, B., Castiglione, A., Psannis, K., Choi, C.: A novel approach for fake news detection in vehicular ad-hoc network (vanet). In: International Conference on Computational Data and Social Networks. pp. 386–397. Springer (2020)
Gaurav, A., Singh, A.K.: Super-router: A collaborative filtering technique against DDoS attacks. In: International Conference on Advanced Informatics for Computing Research. pp. 294–305. Springer (2017)
Gaurav, A., Singh, A.K.: Light weight approach for secure backbone construction for manets. J. King Saud Univ. Comput. Inf. Sci. (2018)
Ghori, M.R., Zamli, K.Z., Quosthoni, N., Hisyam, M., Montaser, M.: Vehicular ad-hoc network (vanet). In: 2018 IEEE International Conference on Innovative Research and Development (ICIRD). pp. 1–6. IEEE (2018)
Gou, Z., Yamaguchi, S., Gupta, B.: Analysis of various security issues and challenges in cloud computing environment: a survey. In: Identity Theft: breakthroughs in Research and Practice, pp. 221–247. IGI global (2017)
Gu, K., Dong, X., Jia, W.: Malicious node detection scheme based on correlation of data and network topology in fog computing-based vanets. IEEE Trans. Cloud Comput. (2020)
Gudivada, A., Philips, J., Tabrizi, N.: Developing concept enriched models for big data processing within the medical domain. Int. J. Softw. Sci. Comput. Intell. (IJSSCI) 12(3), 55–71 (2020)
Gupta, B.B., Joshi, R.C., Misra, M.: An efficient analytical solution to thwart DDoS attacks in public domain. In: Proceedings of the International Conference on Advances in Computing, Communication and Control. pp. 503–509 (2009)
Ji, X., Huang, K., Jin, L., Tang, H., Liu, C., Zhong, Z., You, W., Xu, X., Zhao, H., Wu, J., et al.: Overview of 5G Secur. Technolo. Sci. China Inf. Sci. 61(8), 1–25 (2018)
Jover, R.P.: The current state of affairs in 5g security and the main remaining security challenges (2019). arXiv preprint arXiv:1904.08394
Kamarudin, M.H., Maple, C., Watson, T.: Hybrid feature selection technique for intrusion detection system. Int. J. High Perform. Comput. Netw. 13(2), 232–240 (2019)
Kaushik, S., Gandhi, C.: Ensure hierarchal identity based data security in cloud environment. Int. J. Cloud Appl. Comput. (IJCAC) 9(4), 21–36 (2019)
Khare, A.K., Rana, J., Jain, R.: Detection of wormhole, blackhole and ddos attack in manet using trust estimation under fuzzy logic methodology. Int. J. Comput. Netw. Inf. Secur. 9(7), 29 (2017)
Khayyat, M., Alshahrani, A., Alharbi, S., Elgendy, I., Paramonov, A., Koucheryavy, A.: Multilevel service-provisioning-based autonomous vehicle applications. Sustainability 12(6), 2497 (2020)
Khayyat, M., Elgendy, I.A., Muthanna, A., Alshahrani, A.S., Alharbi, S., Koucheryavy, A.: Advanced deep learning-based computational offloading for multilevel vehicular edge-cloud computing networks. IEEE Access 8, 137052–137062 (2020)
Kolandaisamy, R., Md Noor, R., Ahmedy, I., Ahmad, I., Reza Z’aba, M., Imran, M., Alnuem, M.: A multivariant stream analysis approach to detect and mitigate DDoS attacks in vehicular ad hoc networks. Wirel. Commun. Mob. Comput. 2018 (2018)
Kolandaisamy, R., Noor, R.M., Kolandaisamy, I., Ahmedy, I., Kiah, M.L.M., Tamil, M.E.M., Nandy, T.: A stream position performance analysis model based on ddos attack detection for cluster-based routing in vanet. J. Ambient Intell. Hum. Comput. 1–14 (2020)
Kumar, S., Mann, K.S.: Detection of multiple malicious nodes using entropy for mitigating the effect of denial of service attack in vanets. In: 2018 4th International Conference on Computing Sciences (ICCS), pp. 72–79. IEEE (2018)
Li, S., Da Xu, L., Zhao, S.: 5G internet of things: a survey. J. Ind. Inf. Integr. 10, 1–9 (2018)
Liu, Y., Peng, J., Kang, J., Iliyasu, A.M., Niyato, D., Abd El-Latif, A.A.: A secure federated learning framework for 5G networks. IEEE Wirel. Commun. 27(4), 24–31 (2020)
Mani, N., Moh, M., Moh, T.S.: Defending deep learning models against adversarial attacks. Int. J. Softw. Sci. Comput. Intell. (IJSSCI) 13(1), 72–89 (2021)
Mukherjee, M., Matam, R., Shu, L., Maglaras, L., Ferrag, M.A., Choudhury, N., Kumar, V.: Security and privacy in fog computing: challenges. IEEE Access 5, 19293–19304 (2017)
Nkenyereye, L., Liu, C.H., Song, J.: Towards secure and privacy preserving collision avoidance system in 5G fog based internet of vehicles. Future Gener. Comput. Syste. 95, 488–499 (2019)
Peng, H., Shen, X.S.: Deep reinforcement learning based resource management for multi-access edge computing in vehicular networks. IEEE Trans. Netw. Sci. Eng. (2020)
Ponikwar, C., Hof, H.J.: Overview on security approaches in intelligent transportation systems (2015). arXiv preprint arXiv:1509.01552
RoselinMary, S., Maheshwari, M., Thamaraiselvan, M.: Early detection of DOS attacks in vanet using attacked packet detection algorithm (APDA). In: 2013 International Conference on Information Communication and Embedded Systems (ICICES). pp. 237–240. IEEE (2013)
Rudraraju, C.: Simulation of Detecting and Preventing DDoS in Vehicular Ad-hoc Networks (VANETS). Ph.D. thesis, Dublin, National College of Ireland (2020)
Sarivougioukas, J., Vagelatos, A.: Modeling deep learning neural networks with denotational mathematics in ubihealth environment. Int. J. Softw. Sci. Comput. Intell. (IJSSCI) 12(3), 14–27 (2020)
Sarrab, M., Alshohoumi, F.: Assisted-fog-based framework for IoT-based healthcare data preservation. Int. J. Cloud Appl. Comput. (IJCAC) 11(2), 1–16 (2021)
Schinianakis, D.: Alternative security options in the 5G and IoT era. IEEE Circuits Syst. Mag. 17(4), 6–28 (2017)
Sedik, A., Hammad, M., Abd El-Samie, F.E., Gupta, B.B., Abd El-Latif, A.A.: Efficient deep learning approach for augmented detection of coronavirus disease. Neural Comput. Appl. 1–18 (2021)
Shakshuki, E.M., Kang, N., Sheltami, T.R.: EAACK—a secure intrusion-detection system for MANETs. IEEE Trans. Ind. Electron. 60(3), 1089–1098 (2012)
Shidaganti, G.I., Inamdar, A.S., Rai, S.V., Rajeev, A.M.: SCEF: a model for prevention of DDOS attacks from the cloud. Int. J. Cloud Appl. Comput. (IJCAC) 10(3), 67–80 (2020)
Singh, A., Sharma, P.: A novel mechanism for detecting dos attack in vanet using enhanced attacked packet detection algorithm (eapda). In: 2015 2nd International Conference on Recent Advances in Engineering Computational Sciences (RAECS), pp. 1–5 (2015)
Singh, A., Kumar, R.: A two-phase load balancing algorithm for cloud environment. Int. J. Softw. Sci. Comput. Intell. (IJSSCI) 13(1), 38–55 (2021)
Sk, A., Masilamani, V.: A novel digital watermarking scheme for data authentication and copyright protection in 5G networks. Comput. Electr. Eng. 72, 589–605 (2018)
Sodhro, A.H., Pirbhulal, S., Sangaiah, A.K., Lohano, S., Sodhro, G.H., Luo, Z.: 5G-based transmission power control mechanism in fog computing for internet of things devices. Sustainability 10(4), 1258 (2018)
Sommer, C., German, R., Dressler, F.: Bidirectionally coupled network and road traffic simulation for improved IVC analysis. IEEE Trans. Mob. Comput. 10(1), 3–15 (2010)
Sun, J., Gu, Q., Zheng, T., Dong, P., Valera, A., Qin, Y.: Joint optimization of computation offloading and task scheduling in vehicular edge computing networks. IEEE Access 8, 10466–10477 (2020)
Tanwar, S., Vora, J., Tyagi, S., Kumar, N., Obaidat, M.S.: A systematic review on security issues in vehicular ad hoc network. Secur. Priv. 1(5), e39 (2018)
Tupakula, U., Varadharajan, V., Mishra, P.: Securing SDN controller and switches from attacks. Int. J. High Perform. Comput. Netw. 14(1), 77–91 (2019)
Varga, A.: A practical introduction to the omnet++ simulation framework. In: Recent Advances in Network Simulation, pp. 3–51. Springer (2019)
Wang, J., Feng, D., Zhang, S., Tang, J., Quek, T.Q.: Computation offloading for mobile edge computing enabled vehicular networks. IEEE Access 7, 62624–62632 (2019)
Wang, X., Ning, Z., Wang, L.: Offloading in internet of vehicles: a fog-enabled real-time traffic management system. IEEE Trans. Ind. Inform. 14(10), 4568–4578 (2018)
Xu, X., Xue, Y., Qi, L., Yuan, Y., Zhang, X., Umer, T., Wan, S.: An edge computing-enabled computation offloading method with privacy preservation for internet of connected vehicles. Future Gener. Comput. Syst. 96, 89–100 (2019)
Yan, Z., Xie, H., Zhang, P., Gupta, B.B.: Flexible data access control in D2D communications. Future Gener. Comput. Syst. 82, 738–751 (2018)
Yang, S., Yin, D., Song, X., Dong, X., Manogaran, G., Mastorakis, G., Mavromoustakis, C.X., Batalla, J.M.: Security situation assessment for massive MIMO systems for 5G communications. Future Gener. Comput. Syst. 98, 25–34 (2019)
Ye, H., Li, G.Y., Juang, B.H.F.: Deep reinforcement learning based resource allocation for V2V communications. IEEE Trans. Veh. Technol. 68(4), 3163–3173 (2019)
Zhan, W., Luo, C., Wang, J., Wang, C., Min, G., Duan, H., Zhu, Q.: Deep-reinforcement-learning-based offloading scheduling for vehicular edge computing. IEEE Internet Things J. 7(6), 5449–5465 (2020)
Zhang, W.Z., Elgendy, I.A., Hammad, M., Iliyasu, A.M., Du, X., Guizani, M., Abd El-Latif, A.A.: Secure and optimized load balancing for multi-tier IoT and edge-cloud computing systems. IEEE Internet Things J. (2020)
Zhao, J., Li, Q., Gong, Y., Zhang, K.: Computation offloading and resource allocation for cloud assisted mobile edge computing in vehicular networks. IEEE Trans. Veh. Technol. 68(8), 7944–7956 (2019)
Zhou, H., Chen, X., He, S., Chen, J., Wu, J.: DRAIM: a novel delay-constraint and reverse auction-based incentive mechanism for WiFi offloading. IEEE J. Sel. Areas Commun. 38(4), 711–722 (2020)
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Gaurav, A., Gupta, B.B., Peñalvo, F.J.G., Nedjah, N., Psannis, K. (2022). DDoS Attack Detection in Vehicular Ad-Hoc Network (VANET) for 5G Networks. In: Abd El-Latif, A.A., Abd-El-Atty, B., Venegas-Andraca, S.E., Mazurczyk, W., Gupta, B.B. (eds) Security and Privacy Preserving for IoT and 5G Networks. Studies in Big Data, vol 95. Springer, Cham. https://doi.org/10.1007/978-3-030-85428-7_11
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