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Towards an autonomous resilience strategy the implementation of a self evolving rate limiter

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Abstract

Distributed Denial of Service (DDoS) attacks on network infrastructure are one of the major challenges facing network service providers. Despite the recent rise of low-volume application-level attacks, volume-based DDoS attacks still dominate, with peak traffic rates of 80Gbps being observed recently. This prompts the need for more efficient ways to deal with them. Meanwhile, service providers are struggling to acquire the right technology, resources and expertise to offer more resilient and reliable services. One of the solutions to help address this issue is to adopt an autonomous resilience strategy that systematically coordinates resilience related activities such as detecting and mitigating attacks. In this paper, we study an implementation of an autonomous traffic rate limiter - a function that can be used to mitigate DDoS attacks - that capitalises on the AnYa algorithm, an autonomous learning systems (ALS) algorithm that provides advanced features that are crucial to support an autonomous resilience strategy. These features include self-structuring and support for online learning. In our study, we experimentally show how remediation and recovery processes can be realized autonomously, in response to changes in the operational policy.