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This summary is machine-generated.

This study introduces a new model to combat distributed denial-of-service (DDoS) attacks in Internet of Things (IoT) networks. It enhances cybersecurity by using federated learning for near-real-time, accurate detection and mitigation closer to the attack source.

Keywords:
DDoSHIDPSNIDSdeep learningfederated learningfog computing

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Area of Science:

  • Cybersecurity and Network Infrastructure
  • Distributed Systems and Cloud Computing
  • Internet of Things (IoT) Security

Background:

  • Internet of Things (IoT) devices present significant security vulnerabilities, making them targets for cyber-attacks like distributed denial-of-service (DDoS).
  • DDoS attacks compromise network availability and cause financial losses by overwhelming digital infrastructure with malicious traffic.
  • Existing security measures often struggle to effectively mitigate attacks originating within local networks or targeting IoT ecosystems.

Purpose of the Study:

  • To develop and evaluate a novel model for mitigating DDoS attacks in corporate local networks, focusing on early detection and prevention.
  • To enhance the security of Internet of Things (IoT) environments by implementing a decentralized detection and mitigation system.
  • To improve the accuracy and efficiency of cyber-attack detection while preserving data privacy in distributed IoT infrastructures.

Main Methods:

  • Implementation of Host Intrusion Detection Systems (HIDS) and Network Intrusion Detection Systems (NIDS) for comprehensive anomaly identification.
  • Integration of a Host Intrusion Detection and Prevention System (HIDPS) within a fog computing architecture for real-time threat response.
  • Application of federated learning with NIDS, enabling local data analysis and collaborative detection of anomalous traffic across IoT devices.

Main Results:

  • The proposed model achieved a detection accuracy of 89.753% in identifying anomalous traffic within a decentralized IoT infrastructure.
  • The distributed architecture effectively prevented volumetric attack traffic from reaching critical network points, enhancing overall system resilience.
  • Federated learning minimized the impact of single points of failure and reduced the computational workload on individual devices, improving system efficiency and privacy.

Conclusions:

  • The developed model offers an efficient and accurate solution for near-real-time detection and mitigation of DDoS attacks in IoT local networks.
  • Federated learning combined with intrusion detection systems provides a robust framework for enhancing cybersecurity in decentralized environments.
  • This research contributes to strengthening the protection of IoT networks against malicious traffic and improving the overall security posture of digital infrastructure.