International Journal of Computer Networks and Applications (IJCNA)

Published By EverScience Publications

ISSN : 2395-0455

MENU
International Journal of Computer Networks and Applications (IJCNA)

International Journal of Computer Networks and Applications (IJCNA)

Published By EverScience Publications

ISSN : 2395-0455

Security Vulnerabilities in VANETs and SDN-based VANETs: A Study of Attacks

Author NameAuthor Details

Upinder Kaur, Aparna N. Mahajan, Sunil Kumar, Kamlesh Dutta

Upinder Kaur[1]

Aparna N. Mahajan[2]

Sunil Kumar[3]

Kamlesh Dutta[4]

[1]Maharaja Agrasen University, Baddi (Himachal Pradesh), India.

[2]Maharaja Agrasen University, Baddi (Himachal Pradesh), India.

[3]Guru Jambheshwar University of Science & Technology, Hisar (Haryana), India.

[4]National Institute of Technology, Hamirpur (Himachal Pradesh), India.

Cite this Article

DOI: 10.22247/ijcna/2024/47

Pages : 774-802

 Download PDF

78

Views

Abstract

Vehicular ad hoc networks (VANETs) suffer from several challenges due to advancements in technology such as 5G and the continuous growth in the number of vehicles. These challenges include handling scalability, traffic management, security, flexibility etc. The researchers believe that integrating the Software Defined Networking (SDN) architecture with traditional VANETs could result in a promising solution that will address the aforementioned challenges of traditional VANETs by leveraging the inherent features of SDN. These features include programmable network behavior, virtualization, centralized control mechanism, and network automation. The integration of SDN with VANETs helps in partially mitigating the security attacks associated with traditional VANETs. However, the dynamic and distributed nature of VANETs, when integrated with the centralized control mechanism of SDN introduces new kind of security vulnerabilities. This paper presents a comprehensive review of security vulnerabilities and attacks in both traditional VANETs as well as in SDN-based VANETs. The review begins by categorizing the security attacks against VANETs in accordance with the network communication protocol stack. The potential impacts of these attacks on the operation of vehicular ad hoc networks are being assessed. The paper then explores the extent to which integrating the SDN can mitigate security vulnerabilities in traditional VANETs. Finally, this paper presents the study related to the security attacks against SDN-based VANETs in accordance with the three-layer architecture of SDN. These attacks demonstrate how the unique features (central control mechanism, programmability, and flow-based controlling) of SDN introduce new security vulnerabilities in SDN-based VANETs across the control, data, and application planes. The potential impacts of these attacks on the operation of SDN-based VANETs architecture are also being assessed. Through a systematic classification and analysis of security attacks in VANETs as well as in SDN-based VANETs, this paper will serve as a valuable resource for researchers in understanding the security landscape of VANETs as well as SDN-based VANETs, and motivate them to design the effective defensive solutions to secure the network operations of these critical systems.

Index Terms

VANETs

SDN

SDN-based VANETs

security vulnerabilities

security attacks

encryption and authentication

intrusion detection systems

Reference

  1. 1.
    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, Jun. 2020.
  2. 2.
    S. A. Abdel Hakeem, A. A. Hady, and H. Kim, “Current and Future Developments to Improve 5G-New Radio Performance in Vehicle-to-Everything Communications,” Telecommunication Systems, vol. 75, no. 3, pp. 331–353, Aug. 2020.
  3. 3.
    S. Sharma, A. Kaul, S. Ahmed, and S. Sharma, “A Detailed Tutorial Survey on VANETs: Emerging Architectures, Applications, Security Issues, and Solutions,” International Journal of Communication Systems, vol. 34, no. 14, pp. 4905, Aug. 2021.
  4. 4.
    H. Taslimasa, S. Dadkhah, E. C. P. Neto, P. Xiong, S. Ray, and A. A. Ghorbani, “Security Issues in Internet of Vehicles (IoV): A Comprehensive Survey,” Internet of Things, vol. 22, p. 100809, 2023.
  5. 5.
    F. Cunha, L. Villas, A. Boukerche, G. Maia, A. Viana, R. A. Mini, and A. A. Loureiro, “Data Communication in VANETs: Protocols, Applications and Challenges,” Ad Hoc Networks, vol. 44, pp. 90–103, 2016.
  6. 6.
    K. Vermani, A. Noliya, S. Kumar, and K. Dutta, “Ensemble Learning Based Malicious Node Detection in SDN based VANETs,” Journal of Information Systems Engineering and Business Intelligence, vol. 9, no. 2, pp. 136–146, Nov. 2023.
  7. 7.
    M. Arif, G. Wang, O. Geman, V. E. Balas, P. Tao, A. Brezulianu, and J. Chen, “SDN based VANETs, Security Attacks, Applications, and Challenges,” Applied Sciences, vol. 10, no. 9, p. 3217, 2020.
  8. 8.
    R. Sultana, J. Grover, and M. Tripathi, “Security of SDN based Vehicular Ad Hoc Networks: State-of-the-Art and Challenges,” Vehicular Communications, vol. 27, p. 100284, 2021.
  9. 9.
    M. J. Patil and K. P. Adhiya, "A Comprehensive Study on VANET Security," in Proceedings of the International Conference on Advancements in Smart Computing and Information Security, Cham, Switzerland: Springer Nature, pp. 363–382, 2023.
  10. 10.
    J. T. Penttinen, “5G Explained: Security and Deployment of Advanced Mobile Communications,” John Wiley & Sons, 2019.
  11. 11.
    S. A. Abdel Hakeem, A. A. Hady, and H. Kim, “5G-V2X: Standardization, Architecture, Use Cases, Network-Slicing, and Edge-Computing,” Wireless Networks, vol. 26, no. 8, pp. 6015–6041, Jul. 2020.
  12. 12.
    K. Nisar, E. R. Jimson, M. H. A. Hijazi, I. Welch, R. Hassan, A. H. M. Aman, and S. Khan, “A Survey on the Architecture, Application, and Security of Software-Defined Networking: Challenges and Open Issues,” Internet of Things, vol. 12, p. 100289, 2020.
  13. 13.
    C. Caba and J. Soler, “APIs for QoS Configuration in Software Defined Networks,” in Proceedings of the 2015 1st IEEE Conference on Network Softwarization (NetSoft), pp. 1-5, IEEE, 2015.
  14. 14.
    F. Holik and J. Horalek, “Implementing SDN into Computer Network Lessons,” Journal of Telecommunication, Electronic and Computer Engineering, vol. 9, no. 1–3, pp. 65–69, 2017.
  15. 15.
    M. M. Islam, M. T. R. Khan, M. M. Saad, and D. Kim, “Software-Defined Vehicular Network (SDVN): A Survey on Architecture and Routing,” Journal of Systems Architecture, vol. 114, p. 101961, 2021.
  16. 16.
    S. Kumar and K. Dutta, “Direct Trust-Based Security Scheme for RREQ Flooding Attack in Mobile Ad Hoc Networks,” International Journal of Electronics, vol. 104, no. 6, pp. 1034–1049, 2017.
  17. 17.
    B. Yu, C. Z. Xu, and B. Xiao, “Detecting Sybil Attacks in VANETs,” Journal of Parallel and Distributed Computing, vol. 73, no. 6, pp. 746–756, 2013.
  18. 18.
    W. B. Jaballah, M. Conti, and C. Lal, “Security and Design Requirements for Software-Defined VANETs,” Computer Networks, vol. 169, p. 107099, 2020.
  19. 19.
    M. Arif, G. Wang, M. Zakirul Alam Bhuiyan, T. Wang, and J. Chen, “A Survey on Security Attacks in VANETs: Communication, Applications, and Challenges,” Vehicular Communications, vol. 19, p. 100179, Oct. 2019.
  20. 20.
    S. Kumar and K. Dutta, “Securing Mobile Ad Hoc Networks,” International Journal of Handheld Computing Research, vol. 7, no. 1, pp. 26–76, Jan. 2016.
  21. 21.
    L. Mokdad, J. Ben-Othman, and A. T. Nguyen, “DJAVAN: Detecting Jamming Attacks in Vehicle Ad Hoc Networks,” Performance Evaluation, vol. 87, pp. 47–59, 2015.
  22. 22.
    D. P. Choudhari and S. S. Dorle, “Maximization of Packet Delivery Ratio for DADCQ Protocol After Removal of Eavesdropping and DDoS Attacks in VANET,” in 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT), pp. 1-8. IEEE, 2019.
  23. 23.
    M. Nandhini, “Transport Safety in VANET by Detecting GPS Spoofing Attack Using Two Navigators,” International Journal of Advanced Research in Computer Science, vol. 8, no. 3, 2017.
  24. 24.
    S. R. Shetty and D. H. Manjaiah, “A Comprehensive Study of Security Attack on VANET,” in Data Management, Analytics and Innovation: Proceedings of ICDMAI 2021, Vol. 2, pp. 407-428. Springer Singapore, 2022.
  25. 25.
    S. Kumar and K. S. Mann, “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.
  26. 26.
    R. Mishra, A. Singh, and R. Kumar, “VANET Security: Issues, Challenges and Solutions,” in 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT), pp. 1050-1055. IEEE, 2016.
  27. 27.
    S. Kumar and K. Dutta, “Security Issues in Mobile Ad Hoc Networks: A Survey,” in Security, Privacy, Trust, and Resource Management in Mobile and Wireless Communications, pp. 176-221. IGI Global, 2014.
  28. 28.
    F. Altaf, K. Prateek, and S. Maity, “Beacon Non-Transmission Attack and Its Detection in Intelligent Transportation Systems,” Internet of Things, p. 100602, Aug. 2022.
  29. 29.
    M. A. Mimi, “Using Clustering Scheme: Prevent Reply Attack in Vehicular Ad-Hoc Networks (VANET),” Journal of Discrete Mathematical Sciences and Cryptography, vol. 25, no. 7, pp. 2007-2014, 2022.
  30. 30.
    K. Verma, H. Hasbullah, and A. Kumar, “Prevention of DoS Attacks in VANET,” Wireless Personal Communications, vol. 73, no. 1, pp. 95–126, Apr. 2013.
  31. 31.
    A. M. R. Tolba, “Trust-Based Distributed Authentication Method for Collision Attack Avoidance in VANETs,” IEEE Access, vol. 6, pp. 62747–62755, 2018.
  32. 32.
    F. Ahmad, A. Adnane, V. N. Franqueira, F. Kurugollu, and L. Liu, “Man-in-the-Middle Attacks in Vehicular Ad-Hoc Networks: Evaluating the Impact of Attackers’ Strategies,” Sensors, vol. 18, no. 11, p. 4040, 2018.
  33. 33.
    P. K. Ravula, S. Uppalapati, and G. R. Karri, “An Early Detection and Prevention of Wormhole Attack Using Dynamic Threshold Value in VANET,” International Journal of Vehicle Information and Communication Systems, vol. 9, no. 2, pp. 201–225, 2024.
  34. 34.
    M. A. Mimi, “Using Clustering Scheme: Prevent Reply Attack in Vehicular Ad-Hoc Networks (VANET),” Journal of Discrete Mathematical Sciences and Cryptography, vol. 25, no. 7, pp. 2007-2014, 2022.
  35. 35.
    R. K. Dhanaraj, S. H. Islam, and V. Rajasekar, “A Cryptographic Paradigm to Detect and Mitigate Blackhole Attack in VANET Environments,” Wireless Networks, vol. 28, no. 7, pp. 3127-3142, 2022.
  36. 36.
    S. Kumar and K. Dutta, “Intrusion Detection Technique for Black Hole Attack in Mobile Ad Hoc Networks,” International Journal of Information Privacy, Security and Integrity, vol. 2, no. 2, pp. 81-101, 2015.
  37. 37.
    P. Remya Krishnan and P. Arun Raj Kumar, “Detection and Mitigation of Smart Blackhole and Gray Hole Attacks in VANET Using Dynamic Time Warping,” Wireless Personal Communications, vol. 124, no. 1, pp. 931-966, 2022.
  38. 38.
    F. Alifo, D. Martin, and M. Awinsongya, “Wormhole Attack Vulnerability Assessment of MANETs: Effects on Routing Protocols and Network Performance,” International Journal of Computer Applications, vol. 185, no. 48, pp. 1-8, 2023.
  39. 39.
    S. Kumar and K. Dutta, “Trust-Based Intrusion Detection Technique to Detect Selfish Nodes in Mobile Ad Hoc Networks,” Wireless Personal Communications, vol. 101, pp. 2029-2052, 2018.
  40. 40.
    S. Kumar, K. Dutta, and G. Sharma, “A Detailed Survey on Selfish Node Detection Techniques for Mobile Ad Hoc Networks,” in 2016 Fourth International Conference on Parallel, Distributed and Grid Computing (PDGC), pp. 122-127. IEEE, 2016.
  41. 41.
    N. Jyothi and R. Patil, “An Optimized Deep Learning-Based Trust Mechanism in VANET for Selfish Node Detection,” International Journal of Pervasive Computing and Communications, vol. 18, no. 3, pp. 304–318, 2021.
  42. 42.
    A. H. Magsi, L. V. Yovita, A. Ghulam, G. Muhammad, and Z. Ali, “A Content Poisoning Attack Detection and Prevention System in Vehicular Named Data Networking,” Sustainability, vol. 15, no. 14, p. 10931, 2023.
  43. 43.
    Y. Pramitarini, R. H. Y. Perdana, T. N. Tran, K. Shim, and B. An, “A Hybrid Price Auction-Based Secure Routing Protocol Using Advanced Speed and Cosine Similarity-Based Clustering Against Sinkhole Attack in VANETs,” Sensors, vol. 22, no. 15, p. 5811, 2022.
  44. 44.
    S. Khan, I. Sharma, M. Aslam, M. Z. Khan, and S. Khan, “Security Challenges of Location Privacy in VANETs and State-of-the-Art Solutions: A Survey,” Future Internet, vol. 13, no. 4, p. 96, Apr. 2021.
  45. 45.
    S. Ercan, M. Ayaida, and N. Messai, “Misbehavior Detection for Position Falsification Attacks in VANETs Using Machine Learning,” IEEE Access, vol. 10, pp. 1893–1904, 2022.
  46. 46.
    H. Mustafa, W. Xu, A. R. Sadeghi, and S. Schulz, “End-to-End Detection of Caller ID Spoofing Attacks,” IEEE Transactions on Dependable and Secure Computing, vol. 15, no. 3, pp. 423–436, 2016.
  47. 47.
    K. N. Tripathi, G. Jain, A. M. Yadav, and S. C. Sharma, “Entity-Centric Combined Trust (ECT) Algorithm to Detect Packet Dropping Attack in Vehicular Ad Hoc Networks (VANETs),” in Next Generation Information Processing System: Proceedings of ICCET 2020, Volume 2, pp. 23–33. Springer Singapore, 2021.
  48. 48.
    Y. Xie, Y. Li, and Y. Wang, “Research on Detection Method of Malicious Node Based on Flood Attack in VANET,” in 6GN for Future Wireless Networks: Third EAI International Conference, 6GN 2020, Tianjin, China, August 15-16, 2020, Proceedings 3, pp. 373–383. Springer International Publishing.
  49. 49.
    R. S. Raghav, R. Danu, A. Ramalingam, and G. K. Kumar, “Detection of Node Impersonation for Emergency Vehicles in VANET,” International Journal of Engineering Research & Technology (IJERT), vol. 2, pp. 3383–3389, 2013.
  50. 50.
    W. Xiong, R. Wang, Y. Wang, Y. Wei, F. Zhou, and X. Luo, “Improved Certificateless Aggregate Signature Scheme Against Collusion Attacks for VANETs,” IEEE Systems Journal, vol. 17, no. 1, pp. 1098–1109, 2022.
  51. 51.
    A. Kumar, N. Sharma, and A. Kumar, “End-to-End Authentication Based Secure Communication in Vehicular Ad Hoc Networks (VANET),” Journal of Discrete Mathematical Sciences and Cryptography, vol. 25, no. 1, pp. 219–229, 2022.
  52. 52.
    K. Vamshi Krishna and K. Ganesh Reddy, “Classification of Distributed Denial of Service Attacks in VANET: A Survey,” Wireless Personal Communications, vol. 132, no. 2, pp. 933–964, 2023.
  53. 53.
    K. Adhikary, S. Bhushan, S. Kumar, and K. Dutta, “Evaluating the Performance of Various Machine Learning Algorithms for Detecting DDoS Attacks in VANETs,” International Journal of Control and Automation, vol. 12, no. 5, pp. 478–486, 2019.
  54. 54.
    K. Adhikary, S. Bhushan, S. Kumar, and K. Dutta, “Decision Tree and Neural Network Based Hybrid Algorithm for Detecting and Preventing DDoS Attacks in VANETs,” International Journal of Innovative Technology and Exploring Engineering, vol. 5, pp. 669–675, 2020.
  55. 55.
    K. Adhikary, S. Bhushan, S. Kumar, and K. Dutta, “Evaluating the Performance of Various SVM Kernel Functions Based on Basic Features Extracted from KDDCUP'99 Dataset by Random Forest Method for Detecting DDoS Attacks,” Wireless Personal Communications, vol. 123, pp. 3127–3145, 2022.
  56. 56.
    K. Adhikary, S. Bhushan, S. Kumar, and K. Dutta, “Evaluating the Impact of DDoS Attacks in Vehicular Ad-Hoc Networks,” International Journal of Security, Privacy and Pervasive Computing (IJSPPC), vol. 12, no. 4, pp. 1–18, 2020.
  57. 57.
    G. Nayak, A. Mishra, U. Samal, and B. K. Mishra, “Depth Analysis on DoS & DDoS Attacks,” in Wireless Communications and Security, pp. 159–182, 2022.
  58. 58.
    J. Grover, “Security of Vehicular Ad Hoc Networks Using Blockchain: A Comprehensive Review,” Vehicular Communications, vol. 34, p. 100458, 2022.
  59. 59.
    B. Karthiga, D. Durairaj, N. Nawaz, T. K. Venkatasamy, G. Ramasamy, and A. Hariharasudan, “Intelligent Intrusion Detection System for VANET Using Machine Learning and Deep Learning Approaches,” Wireless Communications and Mobile Computing, vol. 2022, Article ID 5069104, 2022.
  60. 60.
    S. Kumar, K. Dutta, and A. Garg, “FJADA: Friendship Based Jellyfish Attack Detection Algorithm for Mobile Ad Hoc Networks,” Wireless Personal Communications, vol. 101, pp. 1901–1927, 2018.
  61. 61.
    A. Garg, S. Kumar, and K. Dutta, “An Analytical Survey of State of the Art JellyFish Attack Detection and Prevention Techniques,” in 2016 Fourth International Conference on Parallel, Distributed and Grid Computing (PDGC), pp. 38–43. IEEE, 2016.
  62. 62.
    B. K. Pattanayak, O. Pattnaik, and S. Pani, “Dealing with Sybil Attack in VANET,” in Intelligent and Cloud Computing: Proceedings of ICICC 2019, Volume 1, pp. 471–480. Springer Singapore, 2021.
  63. 63.
    K. Vamshi Krishna and K. Ganesh Reddy, “Classification of Distributed Denial of Service Attacks in VANET: A Survey,” Wireless Personal Communications, vol. 132, no. 2, pp. 933–964, 2023.
  64. 64.
    S. A. Asra, “Security Issues of Vehicular Ad Hoc Networks (VANET): A Systematic Review,” TIERS Information Technology Journal, vol. 3, no. 1, pp. 17–27, 2022.
  65. 65.
    P. Mundhe, S. Verma, and S. J. C. S. Venkatesan, “A Comprehensive Survey on Authentication and Privacy-Preserving Schemes in VANETs,” Computer Science Review, vol. 41, p. 100411, 2021.
  66. 66.
    S. Dong, H. Su, Y. Xia, F. Zhu, X. Hu, and B. Wang, “A Comprehensive Survey on Authentication and Attack Detection Schemes that Threaten It in Vehicular Ad-Hoc Networks,” IEEE Transactions on Intelligent Transportation Systems, vol. 24, no. 12, pp. 13573–13602, 2023.
  67. 67.
    A. S. Mustafa, M. M. Hamdi, H. F. Mahdi, and M. S. Abood, “VANET: Towards Security Issues Review,” in 2020 IEEE 5th International Symposium on Telecommunication Technologies (ISTT), pp. 151–156. IEEE, 2020.
  68. 68.
    C. Zhao, J. S. Gill, P. Pisu, and G. Comert, “Detection of False Data Injection Attack in Connected and Automated Vehicles via Cloud-Based Sandboxing,” IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 7, pp. 9078–9088, 2021.
  69. 69.
    A. Al-Sabaawi, K. Al-Dulaimi, E. Foo, and M. Alazab, “Addressing Malware Attacks on Connected and Autonomous Vehicles: Recent Techniques and Challenges,” in Malware Analysis Using Artificial Intelligence and Deep Learning, pp. 97–119.
  70. 70.
    M. Kim, I. Jang, S. Choo, J. Koo, and S. Pack, “Collaborative Security Attack Detection in Software-Defined Vehicular Networks,” in 2017 19th Asia-Pacific Network Operations and Management Symposium (APNOMS), pp. 19–24. IEEE, 2017.
  71. 71.
    J. Zhang, K. Zheng, D. Zhang, and B. Yan, “AATMS: An Anti-Attack Trust Management Scheme in VANET,” IEEE Access, vol. 8, pp. 21077–21090, 2020.
  72. 72.
    K. V. Krishna and K. G. Reddy, “VANET Vulnerabilities Classification and Countermeasures: A Review,” Majlesi Journal of Electrical Engineering, vol. 16, no. 3, pp. 63–83, 2022.
  73. 73.
    M. Adnan, J. Iqbal, A. Waheed, N. U. Amin, M. Zareei, A. Umer, and E. M. Mohamed, “Towards the Design of Efficient and Secure Architecture for Software-Defined Vehicular Networks,” Sensors, vol. 21, no. 11, p. 3902, 2021.
  74. 74.
    O. S. Al-Heety, Z. Zakaria, M. Ismail, M. M. Shakir, S. Alani, and H. Alsariera, “A Comprehensive Survey: Benefits, Services, Recent Works, Challenges, Security, and Use Cases for SDN-VANET,” IEEE Access, vol. 8, pp. 91028–91047, 2020.
  75. 75.
    R. Amin, I. Pali, and V. Sureshkumar, “Software-Defined Network Enabled Vehicle to Vehicle Secured Data Transmission Protocol in VANETs,” Journal of Information Security and Applications, vol. 58, p. 102729, 2021.
  76. 76.
    D. Tang, Z. Zheng, C. Yin, B. Xiong, Z. Qin, and Q. Yang, “FTODefender: An Efficient Flow Table Overflow Attacks Defending System in SDN,” Expert Systems with Applications, vol. 237, p. 121460, 2024.
  77. 77.
    Y. Gautam, B. P. Gautam, and K. Sato, “Experimental Security Analysis of SDN Network by Using Packet Sniffing and Spoofing Technique on POX and Ryu Controller,” in 2020 International Conference on Networking and Network Applications (NaNA), pp. 394–399. IEEE, 2020.
  78. 78.
    J. Cao, Q. Li, R. Xie, K. Sun, G. Gu, M. Xu, and Y. Yang, “The CrossPath Attack: Disrupting the SDN Control Channel via Shared Links,” in 28th USENIX Security Symposium (USENIX Security 19), pp. 19–36.
  79. 79.
    A. Di Maio, M. R. Palattella, R. Soua, L. Lamorte, X. Vilajosana, J. Alonso-Zarate, and T. Engel, “Enabling SDN in VANETs: What is the Impact on Security?” Sensors, vol. 16, no. 12, p. 2077, 2016.
  80. 80.
    A. N. Alhaj and N. Dutta, “Analysis of Security Attacks in SDN Network: A Comprehensive Survey,” in Contemporary Issues in Communication, Cloud, and Big Data Analytics: Proceedings of CCB 2020, pp. 27–37.
  81. 81.
    H. Shafiq, R. A. Rehman, and B. S. Kim, “Services and Security Threats in SDN Based VANETs: A Survey,” Wireless Communications and Mobile Computing, vol. 2018, p. 8631851, 2018.
  82. 82.
    S. Gao, Z. Li, B. Xiao, and G. Wei, “Security Threats in the Data Plane of Software-Defined Networks,” IEEE Network, vol. 32, no. 4, pp. 108–113, 2018.
  83. 83.
    A. S. Alshra’a and J. Seitz, “Using Inspector Device to Stop Packet Injection Attack in SDN,” IEEE Communications Letters, vol. 23, no. 7, pp. 1174–1177, 2019.
  84. 84.
    M. Erritali, B. Cherkaoui, H. Ezzikouri, and A. Beni-hssane, “Detection of the Black Hole Attack on SDN based VANET Network,” in Distributed Sensing and Intelligent Systems: Proceedings of ICDSIS 2020, pp. 67–74. Springer International Publishing, 2022.
  85. 85.
    N. M. Yungaicela-Naula, C. Vargas-Rosales, J. A. Pérez-Díaz, and D. F. Carrera, “A Flexible SDN based Framework for Slow-Rate DDoS Attack Mitigation by Using Deep Reinforcement Learning,” Journal of Network and Computer Applications, vol. 205, p. 103444, 2022.
  86. 86.
    J. C. C. Chica, J. C. Imbachi, and J. F. B. Vega, “Security in SDN: A Comprehensive Survey,” Journal of Network and Computer Applications, vol. 159, p. 102595, 2020.
  87. 87.
    V. Kiruthika, S. Padmapriya, M. Ganga, and V. Anupriya, “Mitigation of Cyber Attacks in Software Defined Networking Framework,” in 2023 First International Conference on Advances in Electrical, Electronics and Computational Intelligence (ICAEECI), pp. 1–6. IEEE, 2023.
  88. 88.
    T. A. Jawad, A. N. Mahmood, and A. N. Hameed, “Detecting Man-in-the-Middle Attacks via Hybrid Quantum-Classical Protocol in Software-Defined Networks,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 31, no. 1, pp. 205–211, 2023.
  89. 89.
    X. Huang, K. Xue, Y. Xing, D. Hu, R. Li, and Q. Sun, “FSDM: Fast Recovery Saturation Attack Detection and Mitigation Framework in SDN,” in 2020 IEEE 17th International Conference on Mobile Ad Hoc and Sensor Systems (MASS), pp. 329–337. IEEE, 2020.
  90. 90.
    I. Al Salti and N. Zhang, “LINK-GUARD: An Effective and Scalable Security Framework for Link Discovery in SDN Networks,” IEEE Access, vol. 10, pp. 130233–130252, 2022.
  91. 91.
    H. Zhou, Y. Zheng, X. Jia, and J. Shu, “Collaborative Prediction and Detection of DDoS Attacks in Edge Computing: A Deep Learning-Based Approach with Distributed SDN,” Computer Networks, vol. 225, p. 109642, 2023.
  92. 92.
    J. Wang, Y. Tan, J. Liu, and Y. Zhang, “Topology Poisoning Attack in SDN-Enabled Vehicular Edge Network,” IEEE Internet of Things Journal, vol. 7, no. 10, pp. 9563–9574, 2020.
  93. 93.
    J. Wang, Y. Tan, and J. Liu, “Topology Poisoning Attacks and Countermeasures in SDN-Enabled Vehicular Networks,” in GLOBECOM 2020 - 2020 IEEE Global Communications Conference, pp. 1–6. IEEE, 2020.
  94. 94.
    H. Zhou, C. Wu, C. Yang, P. Wang, Q. Yang, Z. Lu, and Q. Cheng, “SDN-RDCD: A Real-Time and Reliable Method for Detecting Compromised SDN Devices,” IEEE/ACM Transactions on Networking, vol. 26, no. 5, pp. 2048–2061, 2018.
  95. 95.
    E. M. Onyema, M. A. Kumar, S. Balasubarmanian, S. Bharany, A. U. Rehman, E. T. Eldin, and M. Shafiq, “A Security Policy Protocol for Detection and Prevention of Internet Control Message Protocol Attacks in Software Defined Networks,” Sustainability, vol. 14, no. 19, p. 11950, 2022.
  96. 96.
    S. Wang, S. Chandrasekharan, K. Gomez, S. Kandeepan, A. Al-Hourani, and M. R. Asghar, “SECOD: SDN Secure Control and Data Plane Algorithm for Detecting and Defending Against DoS Attacks,” in NOMS 2018 - 2018 IEEE/IFIP Network Operations and Management Symposium, pp. 1–5. IEEE, 2018.
  97. 97.
    Z. A. Bhuiyan, S. Islam, M. M. Islam, A. A. Ullah, F. Naz, and M. S. Rahman, “On the (In) Security of the Control Plane of SDN Architecture: A Survey,” IEEE Access, 2023.
  98. 98.
    R. Deb and S. Roy, “A Comprehensive Survey of Vulnerability and Information Security in SDN,” Computer Networks, vol. 206, p. 108802, 2022.
  99. 99.
    M. P. Singh and A. Bhandari, “New-Flow Based DDoS Attacks in SDN: Taxonomy, Rationales, and Research Challenges,” Computer Communications, vol. 154, pp. 509–527, 2020.
  100. 100.
    H. Li, D. Li, X. Zhang, G. Shou, Y. Hu, and Y. Liu, “A Security Management Architecture for Time Synchronization Towards High Precision Networks,” IEEE Access, vol. 9, pp. 117542–117553, 2021.
  101. 101.
    Y. Liu, Y. Wang, and H. Feng, “A Novel Link Fabrication Attack Detection Method for Low-Latency SDN Networks,” Journal of Information Security and Applications, vol. 84, p. 103807, 2024.
  102. 102.
    Q. Li, Y. Chen, P. P. Lee, M. Xu, and K. Ren, “Security Policy Violations in SDN Data Plane,” IEEE/ACM Transactions on Networking, vol. 26, no. 4, pp. 1715–1727, 2018.
  103. 103.
    K. Thimmaraju, G. Rétvári, and S. Schmid, “Virtual Network Isolation: Are We There Yet?” in Proceedings of the 2018 Workshop on Security in Softwarized Networks: Prospects and Challenges, pp. 1–7, Budapest, Hungary, 2018.
  104. 104.
    I. A. Valdovinos, J. A. Pérez-Díaz, K. K. R. Choo, and J. F. Botero, “Emerging DDoS Attack Detection and Mitigation Strategies in Software-Defined Networks: Taxonomy, Challenges and Future Directions,” Journal of Network and Computer Applications, vol. 187, p. 103093, 2021.
  105. 105.
    K. Shinan, K. Alsubhi, A. Alzahrani, and M. U. Ashraf, “Machine Learning-Based Botnet Detection in Software-Defined Network: A Systematic Review,” Symmetry, vol. 13, no. 5, p. 866, 2021.
  106. 106.
    H. Zhou, C. Wu, C. Yang, P. Wang, Q. Yang, Z. Lu, and Q. Cheng, “SDN-RDCD: A Real-Time and Reliable Method for Detecting Compromised SDN Devices,” IEEE/ACM Transactions on Networking, vol. 26, no. 5, pp. 2048–2061, 2018.
  107. 107.
    J. Cao, R. Xie, K. Sun, Q. Li, and G. Gu, “When Match Fields Do Not Need to Match: Buffered Packets Hijacking in SDN,” in Proceedings of the Network and Distributed System Security Symposium (NDSS'20), San Diego, CA, USA, 2020.
  108. 108.
    S. Scott-Hayward, C. Kane, and S. Sezer, “Operationcheckpoint: SDN Application Control,” in Proceedings of the 2014 IEEE 22nd International Conference on Network Protocols, pp. 618–623, IEEE, 2014.
  109. 109.
    K. Bhushan and B. B. Gupta, “Distributed Denial of Service (DDoS) Attack Mitigation in Software Defined Network (SDN)-Based Cloud Computing Environment,” Journal of Ambient Intelligence and Humanized Computing, vol. 10, pp. 1985–1997, 2019.
  110. 110.
    J. Kim, M. Seo, S. Lee, J. Nam, V. Yegneswaran, and P. Porras, “Enhancing Security in SDN: Systematizing Attacks and Defenses from a Penetration Perspective,” Computer Networks, p. 110203, 2024.
  111. 111.
    Y. Maleh, Y. Qasmaoui, K. El Gholami, Y. Sadqi, and S. Mounir, “A Comprehensive Survey on SDN Security: Threats, Mitigations, and Future Directions,” Journal of Reliable Intelligent Environments, vol. 9, no. 2, pp. 201–239, 2023.
  112. 112.
    H. Polat, M. Turkoglu, and O. Polat, “Deep Network Approach with Stacked Sparse Autoencoders in Detection of DDoS Attacks on SDN based VANET,” IET Communications, vol. 14, no. 22, pp. 4089–4100, 2020.
  113. 113.
    F. Bensalah, N. Elkamoun, and Y. Baddi, “SDNStat-Sec: A Statistical Defense Mechanism Against DDoS Attacks in SDN based VANET,” in Advances on Smart and Soft Computing: Proceedings of ICACIn 2020, pp. 527–540. Springer Singapore, 2021.
  114. 114.
    R. P. Nayak, S. Sethi, S. K. Bhoi, K. S. Sahoo, T. A. Tabbakh, and Z. A. Almusaylim, “TBDDoSA-MD: Trust-Based DDoS Misbehave Detection Approach in Software-Defined Vehicular Network (SDVN),” Computers, Materials & Continua, vol. 69, no. 3, pp. 2985–2999, 2021.
  115. 115.
    G. De Biasi, L. F. Vieira, and A. A. Loureiro, “Sentinel: Defense Mechanism Against DDoS Flooding Attack in Software Defined Vehicular Network,” in 2018 IEEE International Conference on Communications (ICC), pp. 1–6. IEEE, 2018.
  116. 116.
    G. O. Anyanwu, C. I. Nwakanma, J. M. Lee, and D. S. Kim, “RBF-SVM Kernel-Based Model for Detecting DDoS Attacks in SDN Integrated Vehicular Network,” Ad Hoc Networks, vol. 140, p. 103026, 2023.
  117. 117.
    M. A. Setitra and M. Fan, “Detection of DDoS Attacks in SDN based VANET Using Optimized TabNet,” Computer Standards & Interfaces, vol. 90, p. 103845, 2024.
  118. 118.
    M. Türko?lu, H. Polat, C. Koçak, and O. Polat, “Recognition of DDoS Attacks on SD-VANET Based on Combination of Hyperparameter Optimization and Feature Selection,” Expert Systems with Applications, vol. 203, p. 117500, 2022.
  119. 119.
    P. K. Singh, S. K. Jha, S. K. Nandi, and S. Nandi, “ML-Based Approach to Detect DDoS Attack in V2I Communication Under SDN Architecture,” in TENCON 2018-2018 IEEE Region 10 Conference, pp. 0144–0149. IEEE, 2018.
  120. 120.
    J. S. Weng, J. Weng, Y. Zhang, W. Luo, and W. Lan, “BENBI: Scalable and Dynamic Access Control on the Northbound Interface of SDN based VANET,” IEEE Transactions on Vehicular Technology, vol. 68, no. 1, pp. 822–831, 2018.
  121. 121.
    H. Vasudev and D. Das, “A Trust Based Secure Communication for Software Defined VANETs,” in 2018 International Conference on Information Networking (ICOIN), pp. 316–321. IEEE, 2018.
  122. 122.
    O. S. Al-Heety, Z. Zakaria, M. Ismail, M. M. Shakir, S. Alani, and H. Alsariera, “A Comprehensive Survey: Benefits, Services, Recent Works, Challenges, Security, and Use Cases for SDN-VANET,” IEEE Access, vol. 8, pp. 91028–91047, 2020.
  123. 123.
    B. A. Reddy, K. S. Sahoo, and M. Bhuyan, “Securing P4-SDN Data Plane Against Flow Table Modification Attack,” in NOMS 2024-2024 IEEE Network Operations and Management Symposium, pp. 1–5. IEEE, 2024.
  124. 124.
    I. M. Varma and N. Kumar, “A Comprehensive Survey on SDN and Blockchain-Based Secure Vehicular Networks,” Vehicular Communications, p. 100663, 2023.
  125. 125.
    A. Akhunzada and M. K. Khan, “Toward Secure Software Defined Vehicular Networks: Taxonomy, Requirements, and Open Issues,” IEEE Communications Magazine, vol. 55, no. 7, pp. 110–118, 2017.
  126. 126.
    K. K. Karmakar, V. Varadharajan, and U. Tupakula, “Mitigating Attacks in Software Defined Networks,” Cluster Computing, vol. 22, pp. 1143–1157, 2019.
SCOPUS
SCImago Journal & Country Rank

Announcements

Archives

Downloads

Contact Us

Dr.M. Milton Joe
EverScience Publications,
Nagercoil, Tamilnadu,India

Talk to Us

+91 9442777224

Email Us

editor@ijcna.org

info@ijcna.org

info@everscience.org

Quick Links

Follow Us

Copyright © 2023; EverScience Publications

Designed & Maintained By Shiro Software Solutions