Enhancing Network Security with AI/ML Algorithms Along with Cloud Deployment

  • Unique Paper ID: 170585
  • Volume: 11
  • Issue: 7
  • PageNo: 843-845
  • Abstract:
  • Early adopters of machine learning-powered network security solutions can gain a competitive edge by demonstrating a proactive and robust security posture. Machine learning can detect never-before-seen attacks (zero-day exploits) by recognizing unusual behavior, significantly enhancing a company's security posture. The KDD99 dataset is used as a base for the research. Network intrusion detection systems analyze network traffic to identify malicious activities. The activity for detection includes denial-of-service attacks, port scans, malware distribution, and unauthorized access attempts. Some of the algorithms that has been used while writing the python code are: XGBoost, LSTM and CNN. This will be an effective approach to any network-based systems. We can enact the algorithm deployment as a part of our daily cloud deployment.

Copyright & License

Copyright © 2025 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

BibTeX

@article{170585,
        author = {Apurba Chatterjee},
        title = {Enhancing Network Security with AI/ML Algorithms Along with Cloud Deployment},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {7},
        pages = {843-845},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=170585},
        abstract = {Early adopters of machine learning-powered network security solutions can gain a competitive edge by demonstrating a proactive and robust security posture. Machine learning can detect never-before-seen attacks (zero-day exploits) by recognizing unusual behavior, significantly enhancing a company's security posture. The KDD99 dataset is used as a base for the research. Network intrusion detection systems analyze network traffic to identify malicious activities. The activity for detection includes denial-of-service attacks, port scans, malware distribution, and unauthorized access attempts.  Some of the algorithms that has been used while writing the python code are: XGBoost, LSTM and CNN. This will be an effective approach to any network-based systems. We can enact the algorithm deployment as a part of our daily cloud deployment.},
        keywords = {Machine Learning, malware, algorithms, XGBoost, LSTM, CNN, cloud.},
        month = {December},
        }

Cite This Article

  • ISSN: 2349-6002
  • Volume: 11
  • Issue: 7
  • PageNo: 843-845

Enhancing Network Security with AI/ML Algorithms Along with Cloud Deployment

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