Protecting the Internet: How Smart Computers Detect Online Threats using Intrusion Detection System (IDS)

Abstract

Introduction— In today's internet-driven world, there's a growing threat of cyberattacks. To keep the internet safe, we need a powerful Intrusion Detection System (IDS). This system helps spot and stop online attacks. In this paper, we suggest a new way to do this using smart computer programs called Machine Learning Algorithms. We also use a special dataset called KDD-CUP-99 to see how well these programs can find internet attacks.Our tests show that boosting Algorithms do a great job compared to other computer programs. This research can help make the internet safer by improving our ability to detect and defend against online threats.

Objectives: Evaluate how well various machine learning algorithms can detect several kinds of cyber threats with the KDD-CUP-99 dataset or other similar data sets. This task will compare how boosting algorithms perform against traditional methods.

Methods: To improve discriminatory ability of selected features, this study took a multifold approach on feature selection using correlation-based, Principal Component Analysis (PCA)-based, Information Gain ratio-based modeling, and redundancy minimization methods. We used several classifiers such as: Decision Tree; Random Forest; Gaussian Naïve Bayes; Supervised Machine Learning Model (SVM); XGBoost; and Gaussian Naïve Bayes among others for intrusion detection testing. Decision Tree classifying algorithms had more interpretable results whereas Random Forests enhanced accuracy through ensemble learning while Gaussian Naïve Bayes was computationally efficient. In the process SVM was striving to determine best class division hyperplane.

Results: A variety of machine learning algorithms were experimented on to determine which were best at keeping out intruders, and the results showed that some worked well while others didn’t. Most notably, Gradient Boosting and XGBoost showed the best performance among them all.Research also showed that when compared to Support Vector Classifier (SVC),Logistic Regression,Decision Tree,Gaussian Naive Bayes (NB),Bernoulli Naive Bayes (BNB),Random Forest, and Light GBM, both SV and XG were able to detect intruders more effectively .These two methods ,particularly, demonstrate the importance of boosting in Intruder Detection System (IDS) performance enhancement by making it easier for attacks to be detected and stopped accurately.

Conclusions: Gradient Boosting and XGBoost are among the boosting algorithms used in the field of Intrusion Detection Systems. The two algorithms were found to be significantly powerful. Their power lies in their ability to effectively learn intricate styles and ensemble learning, which in turn help improve detection accuracy and make them more resistant to different kinds of cyberattacks. The most effective and consistent defense mechanisms against unwanted intrusions into networked devices are the advanced Machine Learning tools that boost algorithms like Boosting. In the future there should be more investigations on whether it would be possible for one to invest time as well as money( or other resources) on something so as not only ensure security but enhance safety too especially when it comes down for identification system such as mean square error.