Abstract
In the past decade, there is a terrific growth on Internet and at the same time, we have seen an increase in malicious attacks on government, corporate, military, financial organizations. To overcome these attacks, Intrusion Detection Systems (IDSs) are developed and accepted by many institutions to keep a monitor on intrusion and additional harmful behavior. However, these IDSs still have some obstacles that are low detection accuracy, False Negatives (FN) and False Positives (FP). To overcome these problems, Machine Learning (ML) techniques are used which help in increasing the intrusion detection accuracy and greatly decreases the false negative rate and false positive rate. In this paper, we have considered five algorithms for evaluation, namely Decision Tree (D-tree), Random Forest (RF), Gradient Boosting (GB), AdaBoost (AB), Gaussian Naïve Bayes (GNB) on UNSW-NB15 dataset. And we found that Random Forest is the best classifier based on the following metrics detection accuracy, F1 score, and false positive rate.
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Khan, S., Sivaraman, E., Honnavalli, P.B. (2020). Performance Evaluation of Advanced Machine Learning Algorithms for Network Intrusion Detection System. In: Dutta, M., Krishna, C., Kumar, R., Kalra, M. (eds) Proceedings of International Conference on IoT Inclusive Life (ICIIL 2019), NITTTR Chandigarh, India. Lecture Notes in Networks and Systems, vol 116. Springer, Singapore. https://doi.org/10.1007/978-981-15-3020-3_6
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DOI: https://doi.org/10.1007/978-981-15-3020-3_6
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