Analysis of Intrusion Detection in Cyber Attacks using Machine Learning Neural Networks

Three machine learning algorithms-Support Vector Machines (SVM), k-nearest Neighbours (KNN), and Random Forest (RF)-are analyzed in this research study for their potential use in the field of cybersecurity intrusion detection. With the increasing complexity of cyber threats, robust intrusion detection systems are essential to safeguard digital systems. Leveraging a comprehensive dataset of network activity, this research assesses the effectiveness of these algorithms in accurately identifying intrusions while minimizing false positives. The results showcase the strengths and limitations of each algorithm in terms of accuracy, speed, and adaptability. The results of this study show that machine learning may greatly improve cyber defenses. The necessity of cyber security has grown as our society has gotten more interconnected, as cyberattacks continue to grow in sophistication and variety. Traditional rule-based intrusion detection systems are often insufficient in the face of evolving threats. This research builds upon the foundation of intrusion detection by applying three machine learning algorithms: SVM, KNN, and RF. These algorithms offer diverse approaches to identify and respond to cyber intrusions. A dataset containing a mixture of normal network traffic and known attack patterns is utilized for training and evaluation. The study aims to assess the accuracy, speed, and adaptability of each algorithm for intrusion detection. The study's results reveal the distinct strengths and limitations of SVM, KNN, and RF in the context of intrusion detection. SVM, known for its accuracy, excelled at correctly classifying network activities, minimizing false positives, and reducing the risk of overlooking intrusions. KNN, leveraging proximity-based classification, demonstrated adaptability and simplicity. RF, with its ensemble approach, showcased efficiency and robustness in handling complex data. Each algorithm displayed varying degrees of adaptability, accuracy, and computational efficiency. The results provide insights into the trade-offs between these machine learning methods, offering guidance on selecting the most suitable approach based on specific cybersecurity requirements.