Evaluating a Deep Learning Model for Cyberattack Detection Based on Network Traffic

In the current digital era, Distributed Denial of Service (DDoS) attacks can be recognized as a prevalent and perilous network threat, posing substantial risks to both organizations and individuals. Consequently, the timely identification and notification of DDoS attacks play a vital role in their mitigation and reduction of ensuing harm. Long Short-Term Memory (LSTM) networks, a prevalent form of deep neural network, find extensive application in tasks like natural language processing and forecasting time series data. This study suggests the use of LSTM networks to train and categorize network data features, determining their association with cyberattacks. The effectiveness of our approach in detecting and signaling cyberattacks, especially DDoS attacks, is confirmed through experiments on real-world datasets.