Machine Learning-Based Intrusion Detection System for Network Security
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Cyberattacks pose a significant risk to network environments today as they can lead to the compromise of sensitive information, disruption of digital service, and compromise the confidentiality, integrity, and availability of information systems. Signature-based intrusion detection systems and firewalls are effective, but they can't detect unknown, modified and zero-day attacks. This research paper proposes a machine-learning approach to build an Intrusion Detection System for network security based on the NSL-KDD dataset, which helps to overcome this limitation. The proposed system uses supervised machine learning algorithms to classify the network traffic as either normal traffic or attack traffic. The methodology consists of data collection, data preprocessing, categorical features encoding, feature selection, model training, testing, prediction and evaluation. Random Forest is the primary classification algorithm and Support Vector Machine and Logistic Regression (LR) are employed in comparison. The implementation of the system is done in Python with the use of libraries like Pandas, NumPy, Scikit-learn, and Matplotlib. The illustrative results demonstrate that the Random Forest attained the most noteworthy correctness of 96.20% which was superior to SVM and Logistic Regression. The confusion matrix, attack distribution and feature-importance analysis further illustrates the ability of machine learning to be used for effective intrusion detection. These results should be considered as illustrative only and once the final model is run on the chosen data set, these should be swapped with the experimental results. The overall findings of the study indicate that application of machine learning can enhance the performance of IDS and it offers a practical base for future real time and deep learning based intrusion detection systems.
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