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In the current digital landscape, the risk to the integrity of data and networks is ever-present, making cybersecurity indispensable. This research proposes a system that detects Intrusion using Machine Learning (IDS) to ensure network security. The goal is to have a system that detects Intrusion (IDS) that can reliably detect invasive behavior in network traffic. This dataset is ready to be analysed with a one-hot encoder, normalization, and data preprocessor. An IDS uses binary classification to distinguish between an intrusion and normal activity and multiclass classifications. Machine learning techniques are evaluated including SVM (Support Vector Machine), KNN (K Nearest Neighbors), Multi-Layer Perceptron and Discriminant Analysis. According to the results, technology effectively detects intrusions and significantly reduces false positives. The study underlines the importance of machine learning in enhancing cybersecurity, leading to an active approach to protect data and the virtual space.

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Machine Learning-Based Intrusion Detection System

  • 会议论文

  • 发表日期：

    2026年

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  • Rashmi Ashtagi,
  • Pratiksha Chopade,
  • Atharva Chivate,
  • Shreeshail Chitpur,
  • Isha Dashetwar,
  • Dhiraj Jadhav

摘要

In the current digital landscape, the risk to the integrity of data and networks is ever-present, making cybersecurity indispensable. This research proposes a system that detects Intrusion using Machine Learning (IDS) to ensure network security. The goal is to have a system that detects Intrusion (IDS) that can reliably detect invasive behavior in network traffic. This dataset is ready to be analysed with a one-hot encoder, normalization, and data preprocessor. An IDS uses binary classification to distinguish between an intrusion and normal activity and multiclass classifications. Machine learning techniques are evaluated including SVM (Support Vector Machine), KNN (K Nearest Neighbors), Multi-Layer Perceptron and Discriminant Analysis. According to the results, technology effectively detects intrusions and significantly reduces false positives. The study underlines the importance of machine learning in enhancing cybersecurity, leading to an active approach to protect data and the virtual space.