Machine Learning Based Model and Solution for Network Flow Attack Detection

Network attacks pose major risks to computer system and data security and integrity. The LuFlow22 dataset was used in this study to detect flow-based network threats. We use machine learning classification approaches, specifically the set of machine learning algorithms comprises Decision Trees, Random Forests, Support Vector Machines (SVM), and KNearest Neighbors (KNN) are all examples of machine learning algorithms, to assess their effectiveness in detecting and mitigating network assaults. The Decision Tree algorithm emerges as the greatest performer in terms of precision, regularly achieving greater accuracy in producing positive predictions. The results highlight the Decision Tree algorithm’s dependability and effectiveness in detecting flow-based network attacks. Furthermore, we address possible future uses of this method, such as its integration with real-time network intrusion detection systems to strengthen network security and protect against emerging threats. The study illustrates the importance of using machine learning in network security and provides the groundwork for future more powerful and adaptive intrusion detection systems.