Journal of Cybersecurity and Information Management

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https://doi.org/10.54216/JCIM

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Volume 15 , Issue 1 , PP: 251-269, 2025 | Cite this article as | XML | Html | PDF | Full Length Article

Anomaly Detection Improvement in Computer Communication Networks using Machine Learning Techniques

Hiba A.Tarish 1 *

  • 1 Civil Engineering Department, University of Technology-Iraq, Baghdad, 10066, Iraq - (hiba.a.tarish@uotechnology.edu.iq)
  • Doi: https://doi.org/10.54216/JCIM.150120

    Received: April 12, 2024 Revised: June 11, 2024 Accepted: August 07, 2024
    Abstract

    The issue of force misfortune in wireless sensor networks is one of the fundamental points and central defects that should be defeated in building any coordinated computer information trade and communications framework. Where numerous new examinations have given the idea that talk about this point and recommended various techniques and systems of their sorts, proficiency, and intricacy to take care of the issue of energy misfortune in far off sensors in advanced wireless sensor networks. The WSN networks rely upon the sixth-generation innovations by giving a better system than the pace of sending and getting data and giving permitting all over; likewise, the sixth generation crossing points embrace a smart technique for information transmission in WSNs. Sixth generation is the option in contrast to the fifth-generation cellular technique, where 6G frameworks can apply a larger number of frequencies than 5G frameworks and produce a lot higher transmission capacity with lower idleness. In this review, the hardships experienced in terahertz (THz) advances in wireless sensor networks will be demonstrated, including way obstacles that are viewed as the primary test; Additionally, the attention will be on tracking down answers for keep up with the best and least energy misfortune in the WSN networks by proposing machine learning systems that will show exceptional outcomes through effectiveness measures and ideal energy venture.

    Keywords :

    Wireless Sensor Networks (WSNs) , Machine Learning Techniques , 6G Frameworks , 5G Frameworks , key word 5 , THz Advancements

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    Cite This Article As :
    A.Tarish, Hiba. Anomaly Detection Improvement in Computer Communication Networks using Machine Learning Techniques. Journal of Cybersecurity and Information Management, vol. , no. , 2025, pp. 251-269. DOI: https://doi.org/10.54216/JCIM.150120
    A.Tarish, H. (2025). Anomaly Detection Improvement in Computer Communication Networks using Machine Learning Techniques. Journal of Cybersecurity and Information Management, (), 251-269. DOI: https://doi.org/10.54216/JCIM.150120
    A.Tarish, Hiba. Anomaly Detection Improvement in Computer Communication Networks using Machine Learning Techniques. Journal of Cybersecurity and Information Management , no. (2025): 251-269. DOI: https://doi.org/10.54216/JCIM.150120
    A.Tarish, H. (2025) . Anomaly Detection Improvement in Computer Communication Networks using Machine Learning Techniques. Journal of Cybersecurity and Information Management , () , 251-269 . DOI: https://doi.org/10.54216/JCIM.150120
    A.Tarish H. [2025]. Anomaly Detection Improvement in Computer Communication Networks using Machine Learning Techniques. Journal of Cybersecurity and Information Management. (): 251-269. DOI: https://doi.org/10.54216/JCIM.150120
    A.Tarish, H. "Anomaly Detection Improvement in Computer Communication Networks using Machine Learning Techniques," Journal of Cybersecurity and Information Management, vol. , no. , pp. 251-269, 2025. DOI: https://doi.org/10.54216/JCIM.150120