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In the dynamic field of cybersecurity, safeguarding network integrity is paramount. IDS plays a pivotal role in this endeavour but faces challenges such as decreased detection rates, accuracy issues, and false alarms. Machine learning techniques can help by telling the difference between normal and abnormal network behavior. Deep learning is especially good at this because it can pull out the most useful traits from raw data. This essay looks at how Machine Learning techniques can be used to make Intrusion Detection Systems (IDS) work better. Some of the methods that were looked into are Decision Tree (DT), Support Vector Machine (SVM), Logistic Regression (LR), Random Forest, XG Boost, Gradient Boosting, and Light GBM. In their experimental work, authors have shown the algorithm accuracy as 99% on given dataset, which is considerably good and satisfactory classification rate. The authors also found that precision and recall rate during their experimentation as 0.63–0.99% and 0.53–0.99 respectively, which seems to be quite satisfactory values. It also indicates that it will be helpful in identifying system intrusion or any type of suspicious activity taking place in the system. Through this study, the authors found that Machine-learning has an important role in strengthening cyber-security in all fields.

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Leveraging Machine Learning Algorithms for Enhanced Network Intrusion Detection and Categorization (NIDC)

  • 会议论文

  • 发表日期：

    2025年

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  • Harshita Gupta,
  • Deepak Arora,
  • Shivam Tiwari

摘要

In the dynamic field of cybersecurity, safeguarding network integrity is paramount. IDS plays a pivotal role in this endeavour but faces challenges such as decreased detection rates, accuracy issues, and false alarms. Machine learning techniques can help by telling the difference between normal and abnormal network behavior. Deep learning is especially good at this because it can pull out the most useful traits from raw data. This essay looks at how Machine Learning techniques can be used to make Intrusion Detection Systems (IDS) work better. Some of the methods that were looked into are Decision Tree (DT), Support Vector Machine (SVM), Logistic Regression (LR), Random Forest, XG Boost, Gradient Boosting, and Light GBM. In their experimental work, authors have shown the algorithm accuracy as 99% on given dataset, which is considerably good and satisfactory classification rate. The authors also found that precision and recall rate during their experimentation as 0.63–0.99% and 0.53–0.99 respectively, which seems to be quite satisfactory values. It also indicates that it will be helpful in identifying system intrusion or any type of suspicious activity taking place in the system. Through this study, the authors found that Machine-learning has an important role in strengthening cyber-security in all fields.