摘要翻译:
如果计算机节点受到病毒、蠕虫或后门的感染,那么对于该节点所关联的整个网络结构来说,这是一个安全风险。现有的网络入侵检测系统(NIDS)为识别这类受感染节点提供了一定的支持,但需要大量的通信和计算能力。在这篇文章中,我们提出了一种新的方法,称为AGNOSCO来支持通过人工蚁群来识别感染节点。结果表明,AGNOSCO在正确识别受感染节点的同时,克服了通信和计算能力的问题。
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英文标题:
《AGNOSCO - Identification of Infected Nodes with artificial Ant Colonies》
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作者:
Michael Hilker and Christoph Schommer
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最新提交年份:
2008
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分类信息:
一级分类:Computer Science 计算机科学
二级分类:Artificial Intelligence 人工智能
分类描述:Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域,除了视觉、机器人、机器学习、多智能体系统以及计算和语言(自然语言处理),这些领域有独立的学科领域。特别地,包括专家系统,定理证明(尽管这可能与计算机科学中的逻辑重叠),知识表示,规划,和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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一级分类:Computer Science 计算机科学
二级分类:Multiagent Systems 多智能体系统
分类描述:Covers multiagent systems, distributed artificial intelligence, intelligent agents, coordinated interactions. and practical applications. Roughly covers ACM Subject Class I.2.11.
涵盖多Agent系统、分布式人工智能、智能Agent、协调交互。和实际应用。大致涵盖ACM科目I.2.11类。
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英文摘要:
If a computer node is infected by a virus, worm or a backdoor, then this is a security risk for the complete network structure where the node is associated. Existing Network Intrusion Detection Systems (NIDS) provide a certain amount of support for the identification of such infected nodes but suffer from the need of plenty of communication and computational power. In this article, we present a novel approach called AGNOSCO to support the identification of infected nodes through the usage of artificial ant colonies. It is shown that AGNOSCO overcomes the communication and computational power problem while identifying infected nodes properly.
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PDF链接:
https://arxiv.org/pdf/0805.0785