Classification of Network Anomalies Using a Multilayer Perceptron

The multilayer perceptron (MLP) is an effective tool for classifying network anomalies. This paper examines the methodology for applying MLP to this task, covering data preprocessing, neural network architecture selection and configuration, and training and optimization using modern techniques. The KDD Cup 99 dataset is used as an example. Despite the emergence of more sophisticated deep learning architectures, MLP remains relevant due to its implementation simplicity, computational efficiency, and potential for high accuracy with proper data preparation and tuning. The paper also discusses the challenges of integrating MLP and other machine learning methods into operational intrusion detection systems, such as processing large-scale data streams, handling class imbalance, adapting to evolving threats, and interpreting model decisions. Future research directions include improving preprocessing, investigating hybrid models, enhancing robustness, and exploring explainable AI and federated learning paradigms for MLP application in distributed networks.