Automated Detection and Mitigation of DDoS Attacks and Flash Crowds Using Machine Learning for High-Traffic Web Servers: A Review of Automated Cyber Security Attack Analysis

When many genuine users open a web service at once, for example when examination results are announced, the server can overload in the same way as under a distributed denial of service (DDoS) attack.The two events need opposite responses, yet both raise request volume sharply.This paper reviews twelve recent studies, together with earlier work they build on, covering machine learning for intrusion, DDoS, malware and ransomware detection.It examines how they analyze traffic and logs, reconstruct attack sequences and support automated defense.A comparison by objective, method, evidence and limitation shows that most systems stop at detection, are evaluated on a single dataset, and rarely connect detection to mitigation.Only one reviewed study models attack lifecycles, and none of the reviewed studies, as far as we could verify, treats a genuine flash crowd as a class separate from attack traffic.Based on these gaps, we outline a research direction: a traffic classifier that separates normal, flash crowd and DDoS traffic and drives rate limiting, queuing and blocking automatically.No experimental results are claimed.