A lightweight machine learning approach for DDoS detection and classification

Nature·Apr 10

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Researchers are exploring the use of machine learning approaches for detecting and classifying distributed denial of service (DDoS) attacks. Various methods are being proposed, including lightweight models and ensemble feature fusion, to improve detection and mitigation in different environments, such as internet of things (IoT) networks and cloud computing. The approaches aim to enhance computational efficiency and effectiveness in identifying and responding to DDoS attacks.

Full coverage

Machine learning based approach to intrusion detection in internet of things environments

Frontiers·Mar 17

Identification and detection of DDoS attack on smart home infrastructure using machine learning models

Nature·Jan 17

Design of an AI-driven secure 5G-SDN framework with federated reinforcement learning for anomaly detection, mitigation, and attack forensics

Frontiers·Jan 19

NIDD-enabled lightweight intrusion detection for effective DDoS mitigation in 5G and beyond

Nature·Nov 24

A Two-Phase Detection Method Based on Ensemble Feature Fusion for Detecting Distributed Denial of Service (DDoS) Attacks in Cloud Computing Using Deep Learning Algorithm

Wiley Online Library·Feb 9

Evaluating machine learning approaches for multiple attack classification with improved computational efficiency in IoT networks

Nature·Nov 14

A dataset-centric review of IoT and IIoT intrusion detection: realism, evaluation biases, and future research directions

Frontiers·Jun 22

PSO-DT based BagDT: a robust lightweight ensemble framework for efficient feature selection and DDoS attack detection in IoT environment

Nature·Oct 16

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