Published Paper Details:

: Effective intrusion detection systems (IDS) rely on comprehensive and realistic data sets that emulate real-world network events. Traditionally, the KDD-CUP 99 data set has been utilized for this purpose; however, it has been criticized for its limitations, leading to the adoption of the improved NSL-KDD data set. This research investigates the performance of various classification algorithms and data mining techniques, including decision trees, in identifying anomalies within network traffic using the NSL-KDD data set. We explore the interplay between network protocols in the protocol stack and the intrusion tactics employed by malicious actors to generate unusual network patterns. The analysis leverages the capabilities of the data mining tool WEKA 3.9.5 for preprocessing and preliminary examination, while Python is employed for implementing the classification algorithms. Experimental results indicate that our proposed model, particularly using decision trees, demonstrates high efficacy and robustness, achieving an accuracy of 0.997% as evaluated through metrics such as precision, false-positive rate. Our study begins by detailing the shortcomings of the KDD-CUP 99 data set and the enhancements introduced with the NSL-KDD data set. We then describe the preprocessing steps undertaken using WEKA 3.9.5 to prepare the data for analysis. Various classification algorithms, including decision trees, support vector machines, and neural networks, are implemented in Python to classify network traffic. The performance of these algorithms is rigorously evaluated, with a particular focus on their ability to accurately distinguish between normal and anomalous traffic. By understanding these relationships, we aim to enhance the detection capabilities of IDS. The results of our experiments are promising. The proposed model not only achieves high accuracy and precision but also maintains a low false-positive rate, which is crucial for practical deployment in real-world networks. The integration of WEKA for data preprocessing and Python for implementing sophisticated classification algorithms showcases a powerful approach to detecting network anomalies. Future research will focus on refining these models and exploring additional data sets to further enhance the detection performance of IDS, ensuring they remain effective against evolving cyber threats. By continuously updating the data sets and refining the models, we can better anticipate and counteract the innovative strategies employed by cyber attackers. This ongoing improvement will help maintain the integrity and security of network systems in an increasingly interconnected and vulnerable digital landscape.