The use of machine learning in cyber security has become increasingly popular in recent years due to its potential to identify and mitigate cyber threats. In this paper, we explore the application of machine learning algorithms to detect cyber-attacks in network traffic data. We first preprocessed the data by applying feature engineering and scaling techniques. We then trained and tuned two models: A Random Forest and a Neural network. Our results show that both models performed exceptionally, achieving 100% accuracy and an F1 score on the test data. The Random Forest model achieved these results without any parameter tuning. At the same time, the neural network required careful tuning of its architecture and hyperparameters to evaluate the model’s performance using precision, recall, F1 score, and confusion matrix.

In conclusion, our findings demonstrate the potential of machine learning in detecting cyber-attacks in network traffic data. The high accuracy achieved by our models indicates that machine learning algorithms can effectively detect cyber threats in real-time effect. This has important implications for developing more robust and reliable cybersecurity systems in the future.