Recognizing Network Threats Using Machine Learning: A Comprehensive Overview

References

[1] Tang D, Gao C, Li X, Liang W, Xiao S and Yang Q, “A Detection and Mitigation Scheme of LDoS Attacks via SDN Based on the FSS-RSR Algorithm,” IEEE Transactions on Network Science and Engineering, Vol.10, Issue.4, pp.1952 -1963, 2023, DOI: 10.1109/TNSE.2023.3236970

[2] Zhan S, Tang D, Man J, Dai R and Wang X, “Low-Rate DoS Attacks Detection Based on MAF-ADM,” MDPI Sensors, Vol.20, Issue.1, pp.189, 2020, https://doi.org/10.3390/s20010189.

[3] Liang Liu, Yue Yin, Zhijun Wu, Qingbo Pan and Meng Yue, “LDoS attack detection method based on traffic classification prediction,” IET information security, WILEY, Vol.16, Issue.2, pp.86-96, 2022, DOI: 10.1049/ise2.12046.

[4] Wu Zhijun, Li Wenjing, Liu Liang and Yue Meng, “Low-Rate DoS Attacks, Detection, Defense and Challenges: A Survey,” IEEE access, Vol.8, pp.43920-43943, 2020, DOI: 10.1109/ACCESS.2020.2976609.

[5] Wenwen Sun, Shaopeng Guan, Peng Wang, Qingyu Wu, “A hybrid deep learning model based low-rate DoS attack detection method for software defined network,” Emerging telecommunications technologies,Wiley,Vol.33,Issue.5,2022, https://doi.org/10.1002/ett.4443.

[6] Harun Surej Ilango, Maode Ma and Rong Su, “A FeedForward– Convolutional Neural Network to Detect Low-Rate DoS in IoT,” Engineering applications of artificial intelligence, Elsevier, Vol.114, 2022, https://doi.org/10.1016/j.engappai.2022.105059.

[7] Harun Surej Ilango, Maode Ma and Rong Su, “Low Rate DoS Attack Detection in IoT - SDN using Deep Learning,” IEEE international conference on iThings, Australia, 2022, DOI: 10.1109/iThings - GreenCom - CPSCom - SmartData- Cybermatics53846.2021.00031

[8] Yazhi Liu, Ding Sun, Rundong Zhang and Wei Li, “A Method for Detecting LDoS Attacks in SDWSN Based on Compressed Hilbert–Huang Transform and Convolutional Neural Networks,” MDPI Sensors, Vol.23, Issue.10, pp.4745, 2023, https://doi.org/10.3390/s23104745.

[9] D. Tang, S. Wang, B. Liu, W. Jin and J. Zhang, "GASF-IPP: Detection and Mitigation of LDoS Attack in SDN," IEEE Transactions on Services Computing, pp.1-12, 2023, DOI: 10.1109/TSC.2023.3266757.

[10] Xinmeng Li, Kai Zheng, Dan Tang, Zheng Qin, Zhiqing Zheng, Shihan Zhang, “LDoS Attack Detection Based on ASNNC-OFA Algorithm,” IEEE wireless communications and networking conference, China, 2021, DOI: 10.1109/WCNC49053.2021.9417400

[11] Dan tang, Jingwen chen, Xiyin wang, Siqi zhang,Yudong yan, “A new detection method for LDoS attacks based on data mining,” Future generation computer systems, Elsevier, Vol.16, Issue.128, pp.73-87, 2022, https://doi.org/10.1016/j.future.2021.09.039

[12] Wei shi, Dann tang, Sijia Zhan, Zheng Qin and Xiyin wang, “An approach for detecting LDoS attack based on cloud model,” Forensics of computer science, Springer, Vol.16, Issue.166821, 2022, https://doi.org/10.1007/s11704-022-0486-1.

[13] Naiji Zhang; Fehmi Jaafar; Yasir Malik, “Low-Rate DoS Attack Detection Using PSD Based Entropy and Machine Learning,” 6th IEEE international conference on cyber security and cloud computing, Paris, France, pp.59-62, 2019, DOI 10.1109/CSCloud/EdgeCom.2019.00020.

[14] Dan tang, Liu tang, Rui dai, Jingwen chen, Xiong Li and Joel J.P.C.Rodrigues, “MF-Adaboost: LDoS attack detection based on multi-features and improved Adaboost,”, Future generation computer systems, Elsevier, Vol.106, pp.347-359, 2020, https://doi.org/10.1016/j.future.2019.12.034.

[15] Jingtang Luo, Xiaolong Yang, Jin Wang, Jie Xu, Jian Sun and Keping Long, “On a Mathematical Model for Low-Rate Shrew,” IEEE Transactions on Information Forensics and Security, Vol.9, Issue.7, pp.1069-1083, 2014.

[16] Saurabh Chauhan, Dharamveer Singh, Atul Kumar Singh (2022) “Artificial Intelligence In The Military: An Overview Of The Capabilities, Applications, And Challenges”, Journal of Survey in Fisheries Sciences, Vol 9 (2) pp 984-991. https://doi.org/10.53555/sfs.v9i2.2911

[17] Kiran, Dharamveer Singh, Nitin Goyal, (2023) “Analysis Of How Digital Marketing Affect By Voice Search”, Journal of Survey in Fisheries Sciences, Vol. 30 (2) 407-412. https://doi.org/10.53555/sfs.v10i3.2890

[18] Yukti Tyagi, Dharamveer Singh, Ramander Singh, Sudhir Dawra (2024) “Analysis Of The Most Recent Trojans On The Android Operating System”, Educational Administration: Theory and Practice, Vol. 30(2) 1320-1327. https://doi.org/10.53555/kuey.v30i2.6846

[19] Shivanee Singh, Dharamveer Singh, Ravindra Chauhan (2023) “Manufacturing Industry: A Sustainability Perspective On Cloud And Edge Computing”, Journal of Survey in Fisheries Sciences, pp 1592-1598. https://doi.org/10.53555/sfs.v10i2.2889

@article{1709884,
      author = {Deen Mohd, Mohd Vakil},
      title = {Recognizing Network Threats Using Machine Learning: A Comprehensive Overview},
      journal = {Iconic Research And Engineering Journals},
      year = {2025},
      volume = {9},
      number = {1},
      pages = {1310-1315},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1709884.pdf},
      abstract = {The number of intelligent devices has increased at an unprecedented rate over the last ten years, and the spread of intelligent machines has increased dramatically in recent years. In order to guarantee constant communication amongst networked IoT devices, computer networks are essential. Unfortunately, the significant rise in the usage of smart devices has opened the door for significant unethical behavior within networks. The primary network danger under investigation in this study is the "Low Rate/Slow Denial of Service (LDoS) attack," which seriously jeopardizes the integrity of the internet. Due to the fact that these assaults do not produce large amounts of bandwidth or abrupt increases in network activity, identifying their source is quite difficult. This study investigates the use of machine learning to improve the detection.},
      keywords = {LDoS attack, DDoS attack, Anomaly detection, ML, RL, IDS, Hyper parameter optimization},
      month = {July},
  }