Intrusion Detection System - Build Classifier Model

<p><strong>Intrusion Detection Systems (IDS) </strong>play a crucial role in network security by monitoring network traffic for suspicious activity and potential threats. With the increasing frequency and sophistication of cyber-attacks, an IDS can help organizations identify unauthorized access attempts, malware, and other security breaches in real-time. The ability to detect and respond to intrusions quickly is vital for preventing data breaches and minimizing damage.</p><p></p><p>With the rise of network attacks, detecting intrusions early is crucial for preventing data breaches and minimizing damage. <strong>Traditional signature-based intrusion detection is no longer sufficient,</strong> as new attack methods evolve constantly. The challenge is to develop an Intrusion Detection System (IDS) that can identify abnormal network activity through <strong>machine learning techniques</strong>, which are capable of recognizing unknown threats</p><p></p><p>The objective of this lab activity is to apply machine learning techniques (Logistic Regression, K Nearest Neighbor (KNN), Naïve Bayes, Decision Tree, and Random Forest) to the <strong>NSL-KDD dataset</strong> to build an IDS that can classify network traffic as either <strong>normal</strong> or <strong>malicious (attack)</strong>. Then evaluate and <strong>compare </strong>model performance in terms of accuracy and efficiency.</p>