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September 10, 2025Open Access

Reinforcement Learning and Anomaly Detection as a Defense for Edge-Based DDoS Attacks

TXTeo Rong XuanHo Chi Minh National Academy of Politics LSLijuan ShengHengyang Normal University CYChunhui YuanPeking University

Key Points

The proposed model enhances defense against DDoS attacks by using reinforcement learning for resource management.
Integrating Akamai Prolexic and AI-driven threat detection helps mitigate malicious traffic effectively.
While beneficial for large enterprises, the model may be challenging to implement for smaller organizations.
Edge computing offers flexibility and proximity to users, but it also faces unique security vulnerabilities.

Abstract

Edge computing has emerged as a solution to minimize cloud computing’s latency by placing servers physically closer to the users. However, unlike centralized servers utilized in cloud computing, edge servers are limited by resources and as a decentralized network, is vulnerable to cyberattacks such as Distributed Denial of Services (DDoS) attack. This paper proposes a model based on AWS’s architecture with additional components. The added components are reinforcement learning (RL) based resource management for optimizing VM instances and granting defence systems with more resource, the implementation of Akamai Prolexic alongside with an AI-driven threat detection and response system to mitigation malicious traffic before it reaches internal system. The proposed model is finically costly and difficult to implement for small-medium enterprises however, in large enterprises like AWS and Google Cloud, the performance gains several advantages such as adaptability, scalability, a proactive defence system, etc.

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