Abstract
Effective intrusion detection is one of the key concerns in increasing the sizes of the network. A network intrusion detection system is commonly used as a tool to detect and protect various networks. False-positive detection is one of the key issues because it can distract various organizations from legitimate securities. In this work, we have applied a firefly algorithm-based machine learning model to improve intrusion detection. Firefly-DT-based proposed model giving better classification accuracy in comparison to other KNN, random forest, and neural network models. Further, the firefly algorithm is used to find the optimal structure of the decision tree (DT) by minimizing the false positive and maximizing the true-positive intrusion detection rate. The NSL-KDD dataset is used as a benchmark to observe the outcomes of the proposed model. The performance analysis shows the usefulness of the model, and the comparative study also concludes the same.
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Ranjan, P., Singh, S.K. (2022). A Model on Intrusion Detection Using Firefly Algorithm and Classical Machine Learning. In: Gupta, D., Sambyo, K., Prasad, M., Agarwal, S. (eds) Advanced Machine Intelligence and Signal Processing. Lecture Notes in Electrical Engineering, vol 858. Springer, Singapore. https://doi.org/10.1007/978-981-19-0840-8_16
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DOI: https://doi.org/10.1007/978-981-19-0840-8_16
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