Attacks where threat actors use machine learning and automation to make DDoS campaigns more effective and harder to mitigate. Some examples include:
- Adaptive volumetric attacks — AI adjusts traffic patterns in real time to evade detection thresholds and signature-based mitigation tools
- Smarter target selection — ML models analyze infrastructure to identify the most vulnerable and high-impact attack surfaces automatically
- Behaviorally mimicking botnets — AI-generated traffic that closely resembles legitimate user behavior, making L7 attacks significantly harder to filter
- Automated attack orchestration — reducing the skill floor for launching sophisticated, multi-vector attacks simultaneously