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A comprehensive review of Network Attacks with Emphasis on DoS and DDoS Detection and Mitigation strategies

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Author Affiliations

  • 1School of Studies in Computer Science & I.T., Pt. Ravishankar Shukla University, Raipur, Chhattisgarh, India
  • 2School of Studies in Computer Science & I.T., Pt. Ravishankar Shukla University, Raipur, Chhattisgarh, India

Res. J. Computer & IT Sci., Volume 14, Issue (1), Pages 1-10, June,20 (2026)

Abstract

This paper presents a comprehensive review of major network attacks and their mitigation strategies, emphasizing the growing threat of Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks. It examines essential cyber security principles—confidentiality, integrity, availability, authenticity, and non-repudiation—and outlines vulnerabilities, exploits, and classifications of active and passive attacks. The study reviews prominent attack vectors such as malware, ARP spoofing, IP spoofing, and Man-in-the-Middle (MITM) attacks, alongside conventional defense mechanisms including cryptographic algorithms, access control, and authentication protocols. Through an extensive literature survey, various mitigation frameworks based on statistical, cryptographic, and machine learning techniques are analyzed. The review identifies persistent challenges in detecting and mitigating DDoS attacks, particularly their distributed nature and stealth characteristics that evade conventional Intrusion Detection Systems (IDS) and firewalls. Findings highlight the necessity for adaptive, intelligent, and mathematically robust defense mechanisms, improved datasets, and standardized evaluation metrics to enhance resilience against evolving cyber threats.

References

  1. Report (2022)., At 9.8 GB per month, India has the highest data usage per smartphone., The Hindu, Jun. 19, 2019. Accessed: Jul. 11, 2022. Available: https://www.thehindu. com/
  2. T. W. Edgar and D. O. Manz (2017)., Research methods for cyber security., Cambridge, MA: Syngress, an imprint of Elsevier.
  3. W. Stallings (2011)., Network security essentials: applications and standards., 4th ed. Boston: Prentice Hall.
  4. C.R. Kothari (2004)., Research methodology: methods & techniques., New Delhi: New Age International (P) Ltd., Publishers.
  5. J. Kaur and K. . R. Ramkumar (2021)., The recent trends in cyber security: A review., J. King Saud Univ. - Comput. Inf. Sci., p. S1319157821000203.
  6. Sun, N., Li, C. T., Chan, H., Le, B. D., Islam, M. Z., Zhang, L. Y., ... & Armstrong, W. (2022)., Defining security requirements with the common criteria: Applications, adoptions, and challenges., IEEE Access, 10, 44756-44777.
  7. Lin, H., Yan, Z., Chen, Y., & Zhang, L. (2018)., A survey on network security-related data collection technologies., IEEe Access, 6, 18345-18365.
  8. Shankarapani, M. K., Ramamoorthy, S., Movva, R. S., & Mukkamala, S. (2011)., Malware detection using assembly and API call sequences., Journal in computer virology, 7(2), 107-119.
  9. Takase, H., Kobayashi, R., Kato, M., & Ohmura, R. (2020)., A prototype implementation and evaluation of the malware detection mechanism for IoT devices using the processor information., International Journal of Information Security, 19(1), 71-81.
  10. Lv, S., Wang, J., Yang, Y., & Liu, J. (2018)., Intrusion prediction with system-call sequence-to-sequence model., IEEE Access, 6, 71413-71421.
  11. Divakaran, D. M., Fok, K. W., Nevat, I., & Thing, V. L. (2017)., Evidence gathering for network security and forensics., Digital Investigation, 20, S56-S65.
  12. Li, Y., Huang, G. Q., Wang, C. Z., & Li, Y. C. (2019)., Analysis framework of network security situational awareness and comparison of implementation methods., EURASIP Journal on Wireless Communications and Networking, 2019(1), 205.
  13. Gregorczyk, M., Żórawski, P., Nowakowski, P., Cabaj, K., & Mazurczyk, W. (2020)., Sniffing detection based on network traffic probing and machine learning., IEEE Access, 8, 149255-149269.
  14. Jazi, H. H., Gonzalez, H., Stakhanova, N., & Ghorbani, A. A. (2017)., Detecting HTTP-based application layer DoS attacks on web servers in the presence of sampling., Computer Networks, 121, 25-36.
  15. Mohmand, M. I., Hussain, H., Khan, A. A., Ullah, U., Zakarya, M., Ahmed, A., ... & Haleem, M. (2022)., A machine learning-based classification and prediction technique for DDoS attacks., IEEE Access, 10, 21443-21454.
  16. Abubakar, R., Aldegheishem, A., Majeed, M. F., Mehmood, A., Maryam, H., Alrajeh, N. A., ... & Jawad, M. (2020)., An effective mechanism to mitigate real-time DDoS attack., IEEE Access, 8, 126215-126227.
  17. Simpson, S., Shirazi, S. N., Marnerides, A., Jouet, S., Pezaros, D., & Hutchison, D. (2018)., An inter-domain collaboration scheme to remedy DDoS attacks in computer networks., IEEE Transactions on Network and Service Management, 15(3), 879-893.
  18. Khalaf, B. A., Mostafa, S. A., Mustapha, A., Mohammed, M. A., & Abduallah, W. M. (2019)., Comprehensive review of artificial intelligence and statistical approaches in distributed denial of service attack and defense methods., IEEE Access, 7, 51691-51713.
  19. Halladay, J., Cullen, D., Briner, N., Warren, J., Fye, K., Basnet, R., ... & Doleck, T. (2022)., Detection and characterization of DDoS attacks using time-based features., IEEE Access, 10, 49794-49807.
  20. Nashat, D., Khairy, S., & Hassan, M. M. (2021)., Detection of application layer DDoS attack based on SIS epidemic model., IEEE Access, 9, 159827-159832.
  21. Ghaben, A., Anbar, M., Hasbullah, I. H., & Karuppayah, S. (2021)., Mathematical approach as qualitative metrics of distributed denial of service attack detection mechanisms., IEEE Access, 9, 123012-123028.
  22. Wang, X., Ma, X., Peng, J., Li, J., Xue, L., Hu, W., & Feng, L. (2021)., On modeling link flooding attacks and defenses., IEEE Access, 9, 159198-159217.
  23. Dimolianis, M., Pavlidis, A., & Maglaris, V. (2021)., Signature-based traffic classification and mitigation for DDoS attacks using programmable network data planes., IEEE Access, 9, 113061-113076.
  24. Badhib, A., Alshehri, S., & Cherif, A. (2021)., A robust device-to-device continuous authentication protocol for the internet of things., IEEE Access, 9, 124768-124792.
  25. Alhothaily, A., Hu, C., Alrawais, A., Song, T., Cheng, X., & Chen, D. (2017)., A secure and practical authentication scheme using personal devices., IEEE access, 5, 11677-11687.
  26. Shin, D., Yun, K., Kim, J., Astillo, P. V., Kim, J. N., & You, I. (2019)., A security protocol for route optimization in DMM-based smart home IoT networks., IEEE Access, 7, 142531-142550.
  27. Hussain, S. S., Farooq, S. M., & Ustun, T. S. (2019)., Analysis and implementation of message authentication code (MAC) algorithms for GOOSE message security., IEEE Access, 7, 80980-80984.
  28. Mishra, N., & Pandya, S. (2021)., Internet of things applications, security challenges, attacks, intrusion detection, and future visions: A systematic review., IEEE Access, 9, 59353-59377.
  29. Harbi, Y., Aliouat, Z., Refoufi, A., & Harous, S. (2021)., Recent security trends in internet of things: A comprehensive survey., IEEE Access, 9, 113292-113314.
  30. Zaman, S., Alhazmi, K., Aseeri, M. A., Ahmed, M. R., Khan, R. T., Kaiser, M. S., & Mahmud, M. (2021)., Security threats and artificial intelligence based countermeasures for internet of things networks: a comprehensive survey., Ieee Access, 9, 94668-94690.