A Comprehensive Survey On Machine Learning-Based Intrusion Detection System for Vehicular Area Network Architectures

Yıl 2024, Cilt: 12 Sayı: 3, 1536 - 1556, 31.07.2024

Deniz Balta , Ünal Çavuşoğlu , Musa Balta

https://doi.org/10.29130/dubited.1372131

Öz

Today, the development of communication technologies causes changes in many different areas. One of these areas is VANET (Vehicular Area Network) application area. With the increase in usage areas in the VANET field, ensuring VANET network security has become more critical. Many different systems have been developed to detect attacks on VANET networks. Machine learning-based systems are one of the most widely used methods in developing these intrusion detection systems (IDS). In this article, research on machine learning-based VANET IDS, which has been done recently in the literature, has been carried out. First, VANET architecture and security requirements are presented, then a comprehensive literature summary is given, and comparisons are made on different parameters. As a result, it has been determined that many different machine learning models are used in IDSs and perform high-performance detection. In addition to the machine learning algorithm used in the performance of IDSs, it has been shown that many different parameters play a critical role in the performance. The paper aims to guide new studies in this field with the gains that will increase the performance of intrusion detection systems because of the literature comparison (considering criteria such as machine learning model, simulation tools, dataset, machine learning algorithm, and performance criteria).

Anahtar Kelimeler

IDS, Machine learning, Security, Vehicular networks

Araçsal Ağ Mimarisi İçin Makine Öğrenmesi Tabanlı Saldırı Tespit Sistemi Üzerine Kapsamlı Bir Araştırma

Öz

Günümüzde iletişim teknolojilerinin gelişmesi birçok farklı alanda değişimlere neden olmaktadır. Bu alanlardan biri de VANET (Araç Alan Ağı) uygulama alanıdır. VANET alanında kullanım alanlarının artmasıyla birlikte VANET ağ güvenliğinin sağlanması daha kritik hale gelmiştir. VANET ağlarına yapılan saldırıları tespit etmek için birçok farklı sistem geliştirilmiştir. Makine öğrenimi tabanlı sistemler, bu saldırı tespit sistemlerinin (STS- Intrusion Detection Systems IDS) geliştirilmesinde en yaygın kullanılan yöntemlerden biridir. Bu makalede literatürde son dönemde yapılan makine öğrenmesi tabanlı VANET IDS üzerine araştırmalar yapılmıştır. Öncelikle VANET mimarisi ve güvenlik gereksinimleri sunulmuş, ardından kapsamlı bir literatür özeti verilmiş ve farklı parametreler üzerinden karşılaştırmalar yapılmıştır. Sonuç olarak, saldırı tespit sistemlerinde birçok farklı makine öğrenmesi modelinin kullanıldığı ve yüksek performanslı tespit gerçekleştirdiği tespit edilmiştir. STS'nin performansında kullanılan makine öğrenmesi algoritmasının yanı sıra birçok farklı parametrenin de performansta kritik rol oynadığı gösterilmiştir. Makale, literatür karşılaştırması (makine öğrenme modeli, simülasyon araçları, veri seti, makine öğrenme algoritması ve performans kriterleri gibi kriterler dikkate alınarak) sayesinde saldırı tespit sistemlerinin performansını artıracak kazanımlarla bu alanda yapılacak yeni çalışmalara rehberlik etmeyi amaçlamaktadır.

Anahtar Kelimeler

IDS, Machine Learning, Security, Vehicular networks

Kaynakça

• [1] W. H. Organization et al., “Global status report on road safety 2018: summary,” World Health Organization, Tech. Rep., 2018.
• [2] M. Alam, J. Ferreira, and J. Fonseca, “Introduction to intelligent transportation systems,” in Intelligent transportation systems. Springer, 2016, pp. 1–17.
• [3] C. T. Barba, M. A. Mateos, P. R. Soto, A. M. Mezher, and M. A. Igartua, “Smart city for vanets using warning messages, traffic statistics and intelligent traffic lights,” in 2012 IEEE intelligent vehicles symposium. IEEE, 2012, pp. 902–907.
• [4] P. K. Singh, S. K. Nandi, and S. Nandi, “A tutorial survey on vehicular communication state of the art, and future research directions,” Vehicular Communications, vol. 18, p. 100164, 2019.
• [5] L. Sumi and V. Ranga, “An iot-vanet-based traffic management system for emergency vehicles in a smart city,” in Recent Findings in Intelligent Computing Techniques. Springer, 2018, pp. 23–31.
• [6] S. Abdelatif, M. Derdour, N. Ghoualmi-Zine, and B. Marzak, “Vanet: A novel service for predicting and disseminating vehicle traffic information,” International Journal of Communication Systems, vol. 33, no. 6, p. e4288, 2020.
• [7] A. M. De Souza, C. A. Brennand, R. S. Yokoyama, E. A. Donato, E. R. Madeira, and L. A. Villas, “Traffic management systems: A classification, review, challenges, and future perspectives,” International Journal of Distributed Sen-sor Networks, vol. 13, no. 4, p. 1550147716683612, 2017.
• [8] H. Hasrouny, A. E. Samhat, C. Bassil, and A. Laouiti, “Vanet security challenges and solutions: A survey,” Vehicular Communications, vol. 7, pp. 7–20, 2017.
• [9] M. S. Sheikh and J. Liang, “A comprehensive survey on vanet security services in traffic management system,” Wireless Communications and Mobile Computing, vol. 2019, 2019.
• [10] M. B. Mansour, C. Salama, H. K. Mohamed, and S. A. Hammad, “Vanet security and privacy-an overview,” International Journal of Network Security Its Applications (IJNSA) Vol, vol. 10, 2018.
• [11] R. Abassi, “Vanet security and forensics: Challenges and opportunities,” Wiley Interdisciplinary Reviews: Forensic Science, vol. 1, no. 2, p. e1324, 2019.
• [12] R. Hussain, F. Hussain, and S. Zeadally, “Integration of vanet and 5g security: A review of design and implementation issues,” Future Generation Computer Systems, vol. 101, pp. 843–864, 2019.
• [13] A. N. Upadhyaya and J. Shah, “Attacks on vanet security,” Int J Comp Eng Tech, vol. 9, no. 1, pp. 8–19, 2018.
• [14] M. A. Hezam, A. Junaid, A. Syed, M. Nazri, M. Warip, K. N. Fazira, K. Azir, and R. Nurul Hidayah, “Classification of security attacks in vanet: A review of requirements and perspectives,” 2018.
• [15] D. Manivannan, S. Moni, and S. Zeadally, “Secure authentication and privacy-preserving techniques in vehicular ad-hoc networks (vanets),” Vehicular Communications, vol. 25, p. 100247, 02 2020.
• [16] T. Nandy, M. Y. I. Idris, R. M. Noor, A. W. A. Wahab, S. Bhattacharyya, R. Kolandaisamy, and M. Yahuza, “A secure, privacy-preserving, and lightweight authentication scheme for vanets,” IEEE Sensors Journal, vol. 21, no. 18, pp. 20 998– 21 011, 2021.
• [17] M. A. Al-Shareeda, M. Anbar, I. H. Hasbullah, and S. Manickam, “Survey of authentication and privacy schemes in vehicular ad hoc networks,” IEEE Sensors Journal, vol. 21, no. 2, pp. 2422–2433, 2021.
• [18] S. M. Erfani, S. Rajasegarar, S. Karunasekera, and C. Leckie, “High-dimensional and large-scale anomaly detection using a linear one-class svm with deep learning,” Pattern Recognition, vol. 58, pp. 121–134, 2016.
• [19] Y. Yao, B. Xiao, G. Yang, Y. Hu, L. Wang, and X. Zhou, “Power control identification: A novel sybil attack detection scheme in vanets using rssi,” IEEE Journal on Selected Areas in Communications, vol. 37, no. 11, pp. 2588–2602, 2019.
• [20] O. A. Wahab, A. Mourad, H. Otrok, and J. Bentahar, “Ceap: Svm-based intelligent detection model for clustered vehicular ad hoc networks,” Expert Systems with Applications, vol. 50, pp. 40–54, 2016.
• [21] E. A. Shams, A. Rizaner, and A. H. Ulusoy, “Trust aware support vector machine intrusion detection and prevention system in vehicular ad hoc networks,” Computers Security, vol. 78, pp. 245–254, 2018.
• [22] E. A. Shams, A. H. Ulusoy, and A. Rizaner, “Performance analysis and comparison of anomaly-based intrusion detection in vehicular ad hoc networks.” Radioengineering, vol. 29, no. 4, 2020.
• [23] A. Alsarhan, A.-R. Al-Ghuwairi, I. T. Almalkawi, M. Alauthman, and A. Al-Dubai, “Machine learning-driven optimization for intrusion detection in smart vehicular networks,” Wireless Personal Communications, vol. 117, no. 4, pp. 3129–3152, 2021.
• [24] F. A Ghaleb, F. Saeed, M. Al-Sarem, B. Ali Saleh Al-rimy, W. Boulila, A. Eljialy, K. Aloufi, and M. Alazab, “Misbehavior-aware on-demand collaborative intrusion detection system using distributed ensemble learning for vanet,” Electronics, vol. 9, no. 9, p. 1411, 2020.
• [25] Y. Gao, H. Wu, B. Song, Y. Jin, X. Luo, and X. Zeng, “A distributed network intrusion detection system for distributed denial of service attacks in vehicular ad hoc network,” IEEE Access, vol. 7, pp. 154 560–154 571, 2019.
• [26] S. Sharma and A. Kaul, “Hybrid fuzzy multi-criteria decision making based multicluster head dolphin swarm optimized ids for vanet,” Vehicular Communications, vol. 12, pp. 23–38, 2018.
• [27] F. A. Ghaleb, M. A. Maarof, A. Zainal, B. A. S. Al-rimy, A. Alsaeedi, and W. Boulila, “Ensemble-based hybrid context-aware misbehavior detection model for vehicular ad hoc network,” Remote Sensing, vol. 11, no. 23, p. 2852, 2019.
• [28] A. Khraisat, I. Gondal, P. Vamplew, J. Kamruzzaman, and A. Alazab, “Hybrid intrusion detection system based on the stacking ensemble of c5 decision tree classifier and one class support vector machine,” Electronics, vol. 9, no. 1, p. 173, 2020.
• [29] W. L. Al-Yaseen, Z. A. Othman, and M. Z. A. Nazri, “Multi-level hybrid support vector machine and extreme learning machine based on modified k-means for intrusion detection system,” Expert Systems with Applications, vol. 67, pp. 296–303, 2017.
• [30] K. Adhikary, S. Bhushan, S. Kumar, and K. Dutta, “Hybrid algorithm to detect ddos attacks in vanets,” Wireless Personal Communications, vol. 114, no. 4, pp. 3613–3634, 2020.
• [31] H. M. Song, J. Woo, and H. K. Kim, “In-vehicle network intrusion detection using deep convolutional neural network,” Vehicular Communications, vol. 21, p. 100198, 2020.
• [32] J. Shu, L. Zhou, W. Zhang, X. Du, and M. Guizani, “Collaborative intrusion detection for vanets: a deep learning-based distributed sdn approach,”IEEE Transactions on Intelligent Transportation Systems, 2020.
• [33] V. N and P. Ganapathi, Traffic Analysis of UAV Networks Using Enhanced Deep Feed Forward Neural Networks (EDFFNN), 01 2020, pp. 219–244.
• [34] H. Bangui and B. Buhnova, “Recent advances in machine-learning driven intrusion detection in transportation: Survey,” Procedia Computer Science, vol. 184, pp. 877–886, 01 2021.
• [35] B. A. Bensaber, C. G. P. Diaz, and Y. Lahrouni, “Design and modeling an adaptive neuro-fuzzy inference system (anfis) for the prediction of a security index in vanet,” Journal of Computational Science, vol. 47, p. 101234, 2020.
• [36] A. H. Hamamoto, L. F. Carvalho, L. D. H. Sampaio, T. Abr ̃ao, and M. L. Proen ̧ca Jr, “Network anomaly detection system using genetic algorithm and fuzzy logic,” Expert Systems with Applications, vol. 92, pp. 390–402, 2018.
• [37] L. F. Carvalho, S. Barbon Jr, L. de Souza Mendes, and M. L. Proenca Jr, “Unsupervised learning clustering and self-organized agents applied to help network management,” Expert Systems with Applications, vol. 54, pp. 29–47, 2016.
• [38] J. Liang, M. Ma, M. Sadiq, and K.-H. Yeung, “A filter model for intrusion detection system in vehicle ad hoc networks: A hidden Markov methodology,” Knowledge-Based Systems, vol. 163, pp. 611–623, 2019.
• [39] R. Rabah, A. K. Abdelaziz, G.-Z. Nacira, C. Yacine, and M. Ghamri-Doudane, “Antibotv: A multilevel behaviour-based framework for botnets detection in vehicular networks,” Journal of Network and Systems Management, vol. 30, pp. 1–15, 03 2022.
• [40] J. Liang, J. Chen, Y. Zhu, and R. Yu, “A novel intrusion detection system for vehicular ad hoc networks (vanets) based on differences of traffic flow and position,” Applied Soft Computing, vol. 75, pp. 712–727, 2019.
• [41] B. Subba, S. Biswas, and S. Karmakar, “A game theory based multi layered intrusion detection framework for vanet,” Future Generation Computer Systems, vol. 82, pp. 12–28, 2018.
• [42] K. M. A. Alheeti, A. Gruebler, and K. D. McDonald-Maier, “On the detection of grey hole and rushing attacks in self-driving vehicular networks,” in 2015 7th Computer science and electronic engineering conference (CEEC). IEEE, 2015, pp. 231–236.
• [43] F. A. Ghaleb, A. Zainal, M. A. Rassam, and F. Mohammed, “An effective misbehavior detection model using artificial neural network for vehicular ad hoc network applications,” in 2017 IEEE Conference on Application, Information and Network Security (AINS). IEEE, 2017, pp. 13–18.
• [44] Y. Yu, L. Guo, Y. Liu, J. Zheng, and Y. Zong, “An efficient sdn-based ddos attack detection and rapid response platform in vehicular networks,” IEEE access, vol. 6, pp. 44 570–44 579, 2018.
• [45] D. A. Schmidt, M. S. Khan, and B. T. Bennett, “Spline-based intrusion detection for vanet utilizing knot flow classification,” Internet Technology Letters, vol. 3, no. 3, p. e155, 2020.
• [46] M. Idhammad, K. Afdel, and M. Belouch, “Semi-supervised machine learning approach for ddos detection,” Applied Intelligence, vol. 48, no. 10, pp. 3193–3208, 2018.
• [47] C. A. Kerrache, N. Lagraa, C. T. Calafate, and A. Lakas, “Tfdd: A trust-based framework for reliable data delivery and dos defense in vanets,” Vehicular Communications, vol. 9, pp. 254–267, 2017.
• [48] G. Raja, S. Anbalagan, G. Vijayaraghavan, S. Theerthagiri, S. V. Suryanarayan, and X.-W. Wu, “Sp-cids: Secure and private collaborative ids for vanets,” IEEE Transactions on Intelligent Transportation Systems, 2020.
• [49] J. Nadarajan and J. Kaliyaperumal, “Qos aware and secured routing algorithm using machine intelligence in next generation vanet,” International Journal of System Assurance Engineering and Management, pp. 1–12, 2021.
• [50] C. G. P. Diaz, B. A. Bensaber, and Y. Lahrouni, “Design and modeling an adaptive neuro-diffuse system (anfis) for the prediction of a security index in vanet,” in 2019 IEEE Symposium on Computers and Communications (ISCC). IEEE, 2019, pp.1040–1045.
• [51] A. Alsarhan, M. Alauthman, E. Alshdaifat, A.-R. Al-Ghuwairi, and A. Al-Dubai, “Machine learning-driven optimization for svm-based intrusion detection system in vehicular ad hoc networks,” Journal of Ambient Intelligence and Humanized Computing, pp. 1–10, 2021.
• [52] H. Bangui, M. Ge, and B. Buhnova, “A hybrid machine learning model for intrusion detection in vanet,” Computing, 08 2021.
• [53] J. Liang, Q. Lin, J. Chen, and Y. Zhu, “A filter model based on hidden generalized mixture transition distribution model for intrusion detection system in vehicle ad hoc networks,” IEEE Transactions on Intelligent Transportation Systems, vol. 21, no. 7, pp. 2707–2722, 2020.
• [54] A. Gad, A. Nashat, and T. Barkat, “Intrusion detection system using machine learning for vehicular ad hoc networks based on ton-iot dataset,” IEEE Access, vol. 9, pp. 142 206–142 217, 10 2021.
• [55] S. Ercan, M. Ayaida, and N. Messai, “Misbehavior detection for position falsification attacks in vanets using machine learning,” IEEE Access, vol. PP, pp. 1–1, 12 2021. [56] T. Jo, Machine Learning Foundations: Supervised, Unsupervised, and Advanced Learning. Springer Nature, 2021.
• [57] T. Issariyakul and E. Hossain, “Introduction to network simulator 2 (ns2),” in Introduction to network simulator NS2. Springer, 2009, pp. 1–18.
• [58] G. F. Riley and T. R. Henderson, “The ns-3 network simulator,” in Modeling and tools for network simulation. Springer, 2010, pp. 15–34.
• [59] P. Fernandes and U. Nunes, “Platooning of autonomous vehicles with intervehicle communications in sumo traffic simulator,” in 13th International IEEE Conference on Intelligent Transportation Systems. IEEE, 2010, pp. 1313–1318.
• [60] J. Harri, F. Filali, C. Bonnet, and M. Fiore, “Vanetmobisim: generating realistic mobility patterns for vanets,” in Proceedings of the 3rd international workshop on Vehicular ad hoc networks, 2006, pp. 96–97.
• [61] M. Tavallaee, E. Bagheri, W. Lu, and A. A. Ghorbani, “A detailed analysis of the kdd cup 99 data set,” in 2009 IEEE symposium on computational intelligence for security and defense applications. IEEE, 2009, pp. 1–6.
• [62] L. Dhanabal and S. Shantharajah, “A study on nsl-kdd dataset for intrusion detection system based on classification algorithms,” International journal of advanced research in computer and communication engineering, vol. 4, no. 6, pp. 446–452, 2015.
• [63] S. Meftah, T. Rachidi, and N. Assem, “Network based intrusion detection using the unsw-nb15 dataset,” International Journal of Computing and Digital Systems, vol. 8, no. 5, pp. 478–487, 2019.
• [64] B. Coifman and L. Li, “A critical evaluation of the next generation simulation (ngsim) vehicle trajectory dataset,” Transportation Research Part B: Methodological, vol. 105, pp. 362–377, 2017.
• [65] A. Tharwat, “Classification assessment methods,” Applied Computing and Informatics, 2020.
• [66] A. Javaid, Q. Niyaz, W. Sun, and M. Alam, “A deep learning approach for network intrusion detection system,” Eai Endorsed Transactions on Security and Safety, vol. 3, no. 9, p. e2, 2016.