An Analytical Study of Machine Learning Techniques in Network Security

@article{1707346,
      author = {Dr. Abhishek Raghuvanshi},
      title = {An Analytical Study of Machine Learning Techniques in Network Security},
      journal = {Iconic Research And Engineering Journals},
      year = {2025},
      volume = {8},
      number = {9},
      pages = {98-111},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1707346.pdf},
      abstract = {Network security is one of the most critical concerns in the digital age, where cyber threats and attacks evolve rapidly. Traditional security mechanisms often fail to keep pace with these dynamic and sophisticated threats. In this context, machine learning (ML) has gained significant attention as a powerful tool for enhancing network security. This paper presents an analytical study of various machine learning techniques applied in network security, including intrusion detection, anomaly detection, malware classification, and traffic analysis. By reviewing current approaches, the paper aims to highlight the strengths, limitations, and challenges of integrating machine learning in network security systems. Furthermore, it explores the future trends of ML in protecting networks from emerging cyber threats.},
      month = {March},
  }