Topics for: A Comprehensive Analysis of Machine Learning Models for Better Accuracy: Improving DDoS Attack Detection

Discuss the main challenge of using ML models: accuracy. If the ML model cries wolf too often (a False Alarm or False Positive), it can block real customers. Conversely, if it misses a real attack (a False Negative), the system crashes. The goal is to choose and fine-tune models to minimize these errors and get the best, most reliable detection.

Focus on the role of Machine Learning (ML) models. Discuss how we "train" an ML model by showing it huge amounts of normal and attack traffic. The model then learns to find hidden patterns (the "fingerprint") that define a DDoS attack, helping security systems achieve better accuracy by spotting the attack sooner than a human or a basic program could.

Discuss the basics of a DDoS attack: millions of requests flooding a website or server to crash it. The main problem is that this traffic often looks like normal user traffic. Discuss why traditional firewalls fail and why we need smarter tools like ML to tell the difference between a sudden surge of real users and a malicious flood.