Conference Paper

An adaptive approach to network resilience: Evolving challenge detection and mitigation.

DOI: 10.1109/DRCN.2011.6076900 Conference: 8th International Workshop on the Design of Reliable Communication Networks, DRCN 2011, Krakow, Poland, 10-12 October, 2011
Source: DBLP

ABSTRACT It is widely agreed that computer networks need to become more resilient to a range of challenges that can seriously impact their normal operation. Challenges include malicious attacks, misconfigurations, accidental faults and operational overloads. As part of an overall strategy for network resilience, a crucial requirement is the identification of challenges in real-time, followed by the application of appropriate remedial action. In this paper, we motivate and describe a novel solution that enables the progressive multi-stage deployment of resilience strategies, based on incomplete challenge and context information. Policies are used to orchestrate the interactions between various resilience mechanisms, which incrementally identify the nature of a challenge and deploy appropriate remediation mechanisms. We demonstrate the benefits of this approach via simulation of a resource starvation attack on an Internet Service Provider infrastructure. By initially using lightweight detection and then progressively applying more heavyweight analysis, a key contribution of our work is the ability to mitigate a challenge as early as possible and rapidly detect its root cause. The approach we propose in this paper has the flexibility, reproducibility and extensibility needed to assist in the identification and remediation of various network challenges in the future.

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    • "Despite the multitude of mechanisms and techniques available, it is often not clear how these should be combined and coordinated in complex multi-service networks. We found that the published state-of-the-art in challenge detection and classification varies in the resources that are required, the timeliness and accuracy of their operation, and the challenges they can effectively operate with [12]. For example, localised detection in fluctuations of traffic volumes can give a rapid and relatively lightweight indication of the onset of challenges, such as DDoS attacks or flash crowd events, whereas a sophisticated classification system can yield more accurate information about the challenge, e.g., the identification of malicious flows, over a longer period of time. "
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    • "While it could be perceived that data is at its most vulnerable state during its transmission across the SoS, we look at data in its entirety and recognise there are many weaknesses that can truly affect data within SoS. Data is not just at risk from malicious attacks by outsiders, data can be at risk from legitimate user error, components within the SoS, the physical structure of the network and Internet, as well as natural disasters [22]. For example data can become corrupt via system components during its creation and processing, also malicious attackers from within the SoS can alter, corrupt or delete data just as easily as a legitimate users unintentional actions [23]. "
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    • "The framework presented in this paper builds on parts of our previous efforts on network resilience [1], [2]. It also employs research done independently by the authors. "
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    ABSTRACT: Network resilience strategies aim to maintain ac-ceptable levels of network operation in the face of challenges, such as malicious attacks, operational overload or equipment failures. Often the nature of these challenges requires resilience strategies comprising mechanisms across multiple protocol layers and in disparate locations of the network. In this paper, we address the problem of resilience management and advocate that a new approach is needed for the design and evaluation of resilience strategies. To support the realisation of this approach we propose a framework that enables (1) the offline evaluation of resilience strategies to combat several types of challenges, (2) the generalisa-tion of successful solutions into reusable patterns of mechanisms, and (3) the rapid deployment of appropriate patterns when challenges are observed at run-time. The evaluation platform permits the simulation of a range of challenge scenarios and the resilience strategies used to combat these challenges. Strategies that can successfully address a particular type of challenge can be promoted to become resilience patterns. Patterns can thus be used to rapidly deploy resilience configurations of mechanisms when similar challenges are detected in the live network.
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