英语原文共 39 页
文献：Riveiro M, Pallotta G, Vespe M. Maritime anomaly detection: A review[J]. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Dvery, 2018.
原文：The surveillance of large sea areas normally requires the analysis of large volumes of heterogeneous, multidimensional and dynamic sensor data, in order to improve vessel traffic safety, maritime security and to protect the environment. Early detection of conflict situations at sea provides critical time to take appropriate action with, possibly before potential problems occur. In order to provide an overview of the state-of-the- art of research carried out on the analysis of maritime data for situation awareness, this paper presents a review of maritime anomaly detection. The found articles are categorized into four groups (1) data, (2) methods, (3) systems and (4) user aspects. We present a comprehensive summary of the works found in each category, and finally, we outline possible paths of investigation and challenges for maritime anomaly detection.
翻译：为了改善船舶交通安全，海上安全以及保护环境，在大型海域的监视通常需要分析大量的，多维和动态的传感器数据。 为了及时采取适当行动，尽可能早发现海上冲突情况是非常关键的，甚至可能在问题发生之前就做出相应的行动。 为了对海事数据进行分析所进行的研究的最新技术进行概述，本文对海洋异常检测进行了综合性陈述。 本文将相应的文章分为四类：（1）数据，（2）方法，（3）系统和（4）用户方面。 我们对每个类别中的工作进行了全面的总结，最后，我们概述了可能的研究方法和在海洋异常检测领域的挑战。
原文：
Terminology
In this section, we define relevant terms for the survey. Maritime situation
awareness is defined in COM-EU (2009) as the effective understanding of activity associated with the maritime domain that could impact the security, safety, economy, or environment [...]. The term anomaly is used in many domain areas with different meanings, thus, being a rather fuzzy concept which domain experts may have different notions of (Roy 2008). Anomaly is normally associated with terms of both a positive connotation, such as, normal (normalcy), usual, regular, typical, legal, interesting, and of a negative connotation, such as, abnormal, un- usual, irregular, rare, deviation, strange, special, illegal, threating, exceptional, peculiar, outlier, atypical, inconsistent, etc. (Roy 2008). Moreover, the adjectives may refer to a situation, activity, behavior, event, happening, etc. In general, an anomaly always represents a deviation from normality. Data-driven anomaly detection algorithms consider anomalies, at least in the first stage, as anything that does not match the models that characterize normal data. For example, Portnoy et al. (2001) provide the following definition [a]nomaly detection approaches build models of normal data and then attempt to detect deviations from the normal model in observed data. Most of the published work within anomaly detection has its focus on automatic methods, thus following Portnoy et al. 2001rsquo;s line of work. However, there is no definition of anomaly in Portnoy et al. (2001), nor what or which patterns are to be found in the data, making the evaluation of such methods a challenging task. Chandola et al. (2009) define anomaly as a pattern that does not conform to expected normal behavior; an anomaly detection approach, hence, defines a region representing normal behavior and declares any observation in the data which does not belong to this normal region as an anomaly. Many data mining techniques analyze data in order to find behavioral anomalies, which are defined as deviations from the normal behavior. For instance, Khatkhate et al. (2007) use the following definition within mechanical systems: [a]n anomaly is defined as deviation from the nominal behavior of a dynamical system and is of- ten associated with parametric and non-parametric changes that may gradually evolve in time.” Anomaly detection has been identified as an important group of techniques for detecting critical events in a wide range of data-intensive do- mains, where a majority of the data is considered normal (Chandola et al. 2009). As Ekman and Holst (2008) argue, anomaly detection says nothing about the detection approach, and it actually says nothing about what to detect.” Perhaps the appeal of ”anomaly detection” resides, both from a computational and human point of view, in the richness of its meanings, its complexity and in its vagueness.
翻译：
在本节中，我们定义了本文相关术语。 在COM-EU（2009）中 将 海事情况识别定义为：对可能影响安全性，安全性，经济性或环境的海洋领域相关活动的有效理解 。 在不同领域有不同的含义 ，因此，作为一个相当模糊的概念，不同领域专家可能有不同的理解（Roy 2008）。通常来说，异常与表正面的含义的术语相关联，例如正常，通常，常规，典型，合法，有趣。异常同时也和负面含义的属于相关联，例如，不常见，不规则，罕见，偏离，奇怪的，特殊的，非法的，威胁的，特殊的，奇特的，非典型的，不一致的等等（Roy 2008）。 此外，异常的形容词也可以和情境，活动，行为，事件，发生等相关。一般来说，一个异常总是表示偏离正常。 一般在初始阶段，数据驱动的异常检测算法将异常视为与表征正常数据的模型不匹配。 例如，Portnoy（2001）提供了一种定义： [a]异常检测一般是先构建正常数据模型，然后尝试通过使用待检测数据得到与正常模型间的偏差 。 在异常检测中发表的大部分工作都集中在自动识别的方法上，也就是遵循Portnoy（2001）等人的观点。 但是，Portnoy等人没有对异常进行定义，也没有在数据中找到对应的识别模式，使得对这些异常检测方法的评估成为一项具有挑战性的任务。 而Chandola（2009）将异常定义为不符合预期正常行为的模式，异常检测的方法中定义了正常行为的区域，并将数据中任何不属于该正常区域的观察判定为异常。 许多数据挖掘技术通过分析数据以发现行为异常，行为异常被定义为与正常行为的偏差。 例如，Khatkhate等人（2007）在机械系统中使用以下定义： 异常定义为与动态系统的行为的偏差，并且与可能随时间逐渐演变的参数和非参数变化相关联 。“ 异常检测已被确定为一项重要的技术，异常检测通常用于检测数据密集型领域中的关键事件，在这个情况下其中大多数数据被认为是正常的（Chandola等人，2009）。 正如Ekman 和 Holst（2008）所陈述的那样，异常检测和检测方法无关，它实际上并没有说明要检测什么。“ 从计算和人类的角度来看， 异常检测的吸引力在于其丰富的意义，复杂性和模糊性。
原文：
Classification of the anomaly detection methods: ways to extract the normalcy.
This section describes the normalcy extraction, which is a prerequisite to the application of any anomaly detection method. The way in which normalcy is derived, starts from the definition of the anomaly detection capability both in terms of the types of anomalies of interest and of the desired ac
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