Development of a unified system for real-time prediction and detection of DDoS attacks (Multi-Task Learning & Streaming AI)

CONTEXT With the explosion of network traffic and the increasing sophistication of DDoS attacks, cybersecurity solutions must be able to anticipate malicious behavior while detecting anomalies in real time. The internship aims to develop a unified multi-task (MTL) architecture that can simultaneously predict the probability of a DDoS attack and the future intensity of malicious traffic. INTERNSHIP OBJECTIVES Design, implement, and evaluate a hybrid model capable of performing detection and forecasting in real time using a Multi-Task Learning architecture. REQUIRED TASKS • Design an MTL architecture (classification head + forecasting head) • Integrate advanced models: LSTM, GRU, xLSTM-Time, Transformers, XGBoost/LightGBM • Develop a real-time pipeline (inference < 50 ms) • Deploy a real-time REST API + interactive SOC dashboard DELIVERABLES • Operational MTL model • Complete real-time pipeline • REST API + SOC Dashboard • Scientific report