• Akkireddy Tulasimani
• P.S.L. Sravani
• Pinninti Ramana
• K. Harshith Sai Chandra Varma
• Behara Tapana

The accessibility and security of online services face significant challenges due to DDoS attacks. Traditional detection and mitigation techniques struggle to address Low-rate (LDDOS) and high-rate (HDDOS) DDoS attacks, owing to their distinct traffic patterns and intensities. This paper presents a machine-learning (ML) approach for detecting and responding to these attacks in real time. The system employs supervised learning algorithms to examine the network traffic, identify crucial features, and accurately detect malicious requests. The use of feature engineering helps minimize false positives, thereby enhancing classification accuracy. An automated mitigation framework was incorporated into the system, consisting of transfer.py and receiver.py components. Transfer.py securely transmits the attack data to receiver.py, which then predicts the attack type and blocks malicious traffic. Additionally, the receiver.py module generates alerts to inform administrators about the attack and actions taken. The proposed ML-based method enhances the detection accuracy, maintains low false-positive rates, and adapts dynamically to emerging threats. This research assesses the system's scalability, efficiency, and practical application, thereby strengthening cybersecurity resilience against LDDOS and HDDOS attacks.

Feature Engineering, Real-time mitigation, Alert system, DDoS attack Mitigation

"Lightweight ML-Based System for Detecting and Mitigating LDDOS and HDDOS Attacks", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 3, page no.e523-e530, March-2025, Available :http://www.jetir.org/papers/JETIR2503473.pdf

"Lightweight ML-Based System for Detecting and Mitigating LDDOS and HDDOS Attacks", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 3, page no. ppe523-e530, March-2025, Available at : http://www.jetir.org/papers/JETIR2503473.pdf