Intelligent Intrusion Detection System using Supervised Learning
Going digital involves networking with so many connected devices, so network security becomes a critical task for everyone. But an intrusion detection system can help us to detect malicious activity in a system or network. But generally, intrusion detection systems (IDS) are not reliable and sustainable also they require more resources. In recent years so many machine learning methods are proposed to give higher accuracy with minimal false alerts. But analyzing those huge traffic data is still challenging. So, in this article, we proposed a technique using the Support Vector Machine & Naive Bayes algorithm, by using this we can solve the classification problem of the intrusion detection system. For evaluating our proposed method, we use NSL-KDD and UNSW-NB15 dataset. And after getting the result we see that the SVM works better than the Naive Bayes algorithm on that dataset.
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