摘要翻译：
传感器网络是能够监视物理或环境条件的无线设备的集合。这些设备（节点）被期望自主操作，电池供电，并且具有非常有限的计算能力。这使得保护传感器网络免受不当行为或可能的故障的任务成为一个具有挑战性的问题。在这篇文章中，我们讨论了人工免疫系统(AIS)作为检测不当行为的机制时的性能。我们指出：（一）为了避免安全缺陷，必须谨慎地应用自动识别系统的机制；（二）基因的选择及其相互作用对自动识别系统的性能有深远的影响；（三）随机产生的检测器不符合通信协议的限制；（四）数据流量模式似乎对整体性能没有显著影响。我们确定了一个特异性的基于MAC层的基因，显示出对检测特别有用；基因从节点的角度来衡量网络的性能。此外，我们还发现了一个有趣的基因互补特性；该特性利用了传感器网络的局部性，并将过多的通信负担从正常行为节点转移到行为不端节点。这些结果直接影响到传感器网络AIS的设计和传感器网络的工程设计。
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英文标题：
《AIS for Misbehavior Detection in Wireless Sensor Networks: Performance
  and Design Principles》
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作者：
Martin Drozda, Sven Schaust, Helena Szczerbicka
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最新提交年份：
2009
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Networking and Internet Architecture        网络和因特网体系结构
分类描述：Covers all aspects of computer communication networks, including network architecture and design, network protocols, and internetwork standards (like TCP/IP). Also includes topics, such as web caching, that are directly relevant to Internet architecture and performance. Roughly includes all of ACM Subject Class C.2 except C.2.4, which is more likely to have Distributed, Parallel, and Cluster Computing as the primary subject area.
涵盖计算机通信网络的所有方面，包括网络体系结构和设计、网络协议和网络间标准（如TCP/IP）。还包括与Internet体系结构和性能直接相关的主题，如web缓存。大致包括除C.2.4以外的所有ACM主题类C.2，后者更有可能将分布式、并行和集群计算作为主要主题领域。
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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：Cryptography and Security        密码学与安全
分类描述：Covers all areas of cryptography and security including authentication, public key cryptosytems, proof-carrying code, etc. Roughly includes material in ACM Subject Classes D.4.6 and E.3.
涵盖密码学和安全的所有领域，包括认证、公钥密码系统、携带证明的代码等。大致包括ACM主题课程D.4.6和E.3中的材料。
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一级分类：Computer Science        计算机科学
二级分类：Performance        性能
分类描述：Covers performance measurement and evaluation, queueing, and simulation. Roughly includes material in ACM Subject Classes D.4.8 and K.6.2.
涵盖性能测量和评估、排队和模拟。大致包括ACM主题课程D.4.8和K.6.2中的材料。
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英文摘要：
  A sensor network is a collection of wireless devices that are able to monitor physical or environmental conditions. These devices (nodes) are expected to operate autonomously, be battery powered and have very limited computational capabilities. This makes the task of protecting a sensor network against misbehavior or possible malfunction a challenging problem. In this document we discuss performance of Artificial immune systems (AIS) when used as the mechanism for detecting misbehavior.   We show that (i) mechanism of the AIS have to be carefully applied in order to avoid security weaknesses, (ii) the choice of genes and their interaction have a profound influence on the performance of the AIS, (iii) randomly created detectors do not comply with limitations imposed by communications protocols and (iv) the data traffic pattern seems not to impact significantly the overall performance.   We identified a specific MAC layer based gene that showed to be especially useful for detection; genes measure a network's performance from a node's viewpoint. Furthermore, we identified an interesting complementarity property of genes; this property exploits the local nature of sensor networks and moves the burden of excessive communication from normally behaving nodes to misbehaving nodes. These results have a direct impact on the design of AIS for sensor networks and on engineering of sensor networks.
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PDF链接：
https://arxiv.org/pdf/0906.3461