Machine Learning Algorithm for Intrusion Detection: Performance Evaluation and Comparative Analysis

Ensuring the security of a network or system is crucial in today’s digital age. One of the key measures to achieve this is intrusion detection, which involves identifying and stopping unauthorized access. With the increasing complexity of cyber threats, regular intrusion detection methods have been proven inadequate in providing robust security. Machine Learning (ML) algorithms have emerged as a reliable method to notice and prevent intrusion in real-time. Data mining techniques have been employed in intrusion detection systems (IDS) founded on ML to scrutinize network traffic and detect patterns and anomalies that may signify a possible threat. The data used in the model can be categorized into two types: labeled and unlabeled data. Labeled data is used to train the system to recognize known attacks, while unlabeled data is used to detect new and unknown attacks. There are several types of IDS that use ML algorithms. The proposed approach has several advantages over traditional rule-based IDS. This approach employs an ML technique that integrates various algorithms including Support Vector Machine (SVM), Random Forest (RF), Logistic Regression (LR), and Decision Tree (DT). The proposed approach’s comparative analysis will help provide a better intrusion detection method. It can provide a powerful tool for enhancing the safety of computer networks. By leveraging the power of ML algorithms, the proposed method can improve the accuracy and effectiveness of IDS and stay one step ahead of potential security threats. These algorithms can detect and prevent known and unknown attacks by utilizing labeled and unlabeled data. The comparative analysis of different ML approaches will provide valuable insights for developing robust IDS and better safeguarding our digital assets in an increasingly complex threat landscape.