Advanced Anomaly Detection for Network Intrusion Using Machine Learning

With the increasing number of cyber-attacks over networks, there is a demand for a more effective and efficient intrusion detection system (IDS) to secure systems. In this paper, we have mentioned the very high levels implementation of such model with Gaussian Naïve Bayes, Logistic regression, Random Forest. Emphasis is put on improving the accuracy of classification and lowering false- alarm rates, but we assess these methods to gauge their power in identifying malfeasant network activities. The models are trained and tested at NSL-KDD which is a standard benchmark dataset for IDS. Each algorithm is shown have computational efficiency, and can handle the multidimensional network traffic data. Logistic regression is a simple model because it is interpretable Though performance might get hampered due to its assumed feature independence. This is simply because of the fact that it ideal for quick and easy handling when classifying huge datasets which are Gaussian Naïve Bayes. With the help of several decision trees, Random Forest which is an ensemble-based technique has high accuracy and tolerance to noisy input. Results based on the testing have shown that Random Forest outperforms both Gaussian Naïve Bayes and Logistic Regression for detection accuracy and precision, in addition to providing similar computational accuracy. A comparative study is helpful in understanding these trade-offs and, based on it we can easily decide which algorithm to use for developing a real-time intrusion detection system.