Abstract: The rapid growth of online data transmission has increased the demand for stronger data security. Intrusion Detection Systems (IDS) are essential for identifying virtual security threats by using advanced technologies, especially Machine Learning Algorithms, to swiftly detect and categorize attacks in real-time and determine the most accurate algorithm for attack classification.
The current setup uses various intrusion detection algorithms, with a focus on improving performance through advanced algorithms like the Ensemble Learning and Discriminate Analysis. Unlike the existing approach that primarily relies on accuracy, we have used performance parameters such as Accuracy, Precision, Recall, and F1-Measure for evaluating the performance of the models. This comprehensive analysis aims to improve intrusion detection, offering a deeper understanding of algorithm effectiveness, and increasing confidence in the system's intrusion detection capabilities.
Keywords: Machine Learning, Datasets, Feature Selection, Machine Learning algorithms, Intrusion Detection System
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DOI:
10.17148/IJARCCE.2024.134112
