Makine öğrenme metodolojilerini kullanarak yetkisiz erişime dayanıklı, sağlam bir bilgisayar ağ mimarisi geliştirin

As the use of IT spreads rapidly into new areas, the necessity to ensure the security of these systems has grown. Cyberattacks have also become much more sophisticated as a result of the widespread availability of information technology. Consequently, traditional security measures like SIDS have failed to identify new types of assaults. Intrusion Detection Systems (IDS) make it possible to track and gather harmful data inside a network. The majority of IDSs rely on signatures to identify potential threats. They use a set of rules—either manually entered by the administrator or created automatically by the system—to identify and respond to known threats. To maintain the availability of services at all times, network security experts focus on both preventing and responding to intrusion attempts. To find and categorize suspicious actions, security professionals employ tools like IDS. Hence, to protect privacy, security, and the ongoing delivery of services, it is crucial that the IDS continually keeps up-to-date with the most recent intrusion attack signatures. Important factors to consider while evaluating IDS performance are its speed and its capacity to learn new assaults. This study demonstrates how several Machine Learning techniques may be evaluated using the Knowledge Discovery and Data Mining (KDD) dataset, which is also called Knowledge Discovery in Databases. The primary focus is on creating a comprehensive and representative dataset for experimentation, with a strong emphasis on KDD. For this analysis, we have chosen to use the K-Nearest Neighbor (KNN) and Multilayer Perceptron (MLP) classifiers. The KNN classifier has shown the highest accuracy in recognizing and classifying all types of KDD dataset attacks (DOS, R2L, U2R, NORMAL, and PROBE), both for binary class (NORMAL vs. ABNORMAL) and multi-class scenarios. The experimental findings utilizing the proposed KNN and MLP models showed that the accuracy of binary classification using KNN and MLP was 99% and 97% respectively. Furthermore, the multi-class classification produced improved results compared to earlier work, with reported high-level accuracies of 92% and 87% respectively. This thesis investigates the effectiveness of using deep learning techniques, namely MNN and MLP designs, for network flow-based intrusion detection.