The Case for Robust Adaptation: Autonomic Resource Management is a Vulnerability

Joseph Noor, A. Ali-Eldin, Luis Garcia, C. Rao, Venkateswara Dasari, Deepak Ganesan, B. Jalaeian, P. Shenoy, M. Srivastava
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Abstract

Autonomic resource management for distributed edge computing systems provides an effective means of enabling dynamic placement and adaptation in the face of network changes, load dynamics, and failures. However, adaptation in-and-of-itself offers a side channel by which malicious entities can extract valuable information. An attacker can take advantage of autonomic resource management techniques to fool a system into misallocating resources and crippling applications. Using a few scenarios, we outline how attacks can be launched using partial knowledge of the resource management substrate - with as little as a single compromised node. We argue that any system that provides adaptation must consider resource management as an attack surface. As such, we propose ADAPT2, a framework that incorporates concepts taken from Moving-Target Defense and state estimation techniques to ensure correctness and obfuscate resource management, thereby protecting valuable system and application information from leaking.
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稳健适应的理由:自主资源管理是一个弱点
分布式边缘计算系统的自主资源管理提供了一种有效的方法,可以在面对网络变化、负载动态和故障时实现动态放置和适应。然而,自适应本身提供了一个侧面通道,恶意实体可以通过该通道提取有价值的信息。攻击者可以利用自主资源管理技术欺骗系统,使其资源分配不当,从而破坏应用程序。通过几个场景,我们概述了如何利用资源管理底层的部分知识发起攻击——只需一个受损的节点。我们认为,任何提供自适应的系统都必须将资源管理视为攻击面。因此,我们提出了ADAPT2,这是一个框架,它结合了来自移动目标防御和状态估计技术的概念,以确保正确性和混淆资源管理,从而保护有价值的系统和应用程序信息免受泄漏。
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