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An orchestration approach for unwanted Internet traffic identification

机译：不需要的Internet流量识别的编排方法

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摘要

A simple examination of Internet traffic shows a wide mix of relevant and unwanted traffic. The latter is becoming increasingly harmful to network performance and service availability, while often consuming precious network and processing resources. Coordinated attacks, such as distributed denial-of-services (DDoS), large-scale scans, and worm outbreaks, occur in multiple networks simultaneously and become extremely difficult to detect using an individual detection engine. This paper presents the specification of a new orchestration-based approach to detect, and, as far as possible, to limit the actions of these coordinated attacks. Core to the proposal is a framework that coordinates the receiving of a multitude of alerts and events from detectors, evaluates this input to detect or prove the existence of anomalies, and consequently chooses the best action course. This framework is named Orchestration-oriented Anomaly Detection System (OADS). We also describe an OADS prototype implementation of the proposed infrastructure and analyze initial results obtained through experimentation with this prototype.
机译：对Internet流量的简单检查显示，相关流量和有害流量之间存在广泛的混合。后者对网络性能和服务可用性的危害越来越大，同时经常消耗宝贵的网络和处理资源。协同攻击，例如分布式拒绝服务（DDoS），大规模扫描和蠕虫爆发，会同时在多个网络中发生，并且使用单个检测引擎进行检测变得极为困难。本文介绍了一种新的基于业务流程的方法的规范，该方法可以检测并尽可能限制这些协同攻击的行为。该提案的核心是一个框架，该框架协调从检测器接收大量警报和事件，评估此输入以检测或证明异常的存在，并因此选择最佳的操作过程。该框架称为面向业务流程的异常检测系统（OADS）。我们还描述了所提议基础结构的OADS原型实现，并分析了通过对该原型进行试验获得的初步结果。

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