基于属性网络的交互式异常检测

Kaize Ding, Jundong Li, Huan Liu
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引用次数: 107

摘要

在属性网络上执行异常检测涉及寻找模式或行为明显偏离大多数参考节点的节点。它的成功可以很容易地在许多现实世界的应用中找到,例如网络入侵检测,意见垃圾检测和系统故障诊断,仅举几例。尽管他们在经验上取得了成功,但由于地面真值异常的昂贵标记成本,绝大多数现有的努力都是在无监督的情况下进行的。事实上,在许多情况下,人类对数据的少量先验知识通常是毫不费力地获得的,并且将其纳入学习过程已被证明可以有效地推进许多重要的学习任务。此外,由于新的异常类型可能会随着时间的推移不断出现,特别是在敌对的环境中,人类专家的兴趣也会随着检测到的异常类型而相应改变。传统的异常检测算法通常应用于批处理环境,无法与环境进行交互,这给传统的异常检测算法带来了进一步的挑战。为了解决上述问题,在本文中,我们研究了在交互式设置下的属性网络异常检测问题,通过允许系统主动与人类专家进行交流,对地面真实异常进行有限数量的查询。我们的目标是在给定的预算用完后,最大限度地呈现给人类专家的真实异常。沿着这条线,我们通过原则性的多臂强盗框架来制定问题,并开发了一种新的协作上下文强盗算法,命名为GraphUCB。特别是,我们开发的算法:(1)在联合框架中显式地对节点属性和节点依赖关系进行无缝建模;(2)处理查询不同类型异常时的勘探开发困境。在真实世界数据集上的大量实验表明,所提出的算法比最先进的算法有所改进。
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Interactive Anomaly Detection on Attributed Networks
Performing anomaly detection on attributed networks concerns with finding nodes whose patterns or behaviors deviate significantly from the majority of reference nodes. Its success can be easily found in many real-world applications such as network intrusion detection, opinion spam detection and system fault diagnosis, to name a few. Despite their empirical success, a vast majority of existing efforts are overwhelmingly performed in an unsupervised scenario due to the expensive labeling costs of ground truth anomalies. In fact, in many scenarios, a small amount of prior human knowledge of the data is often effortless to obtain, and getting it involved in the learning process has shown to be effective in advancing many important learning tasks. Additionally, since new types of anomalies may constantly arise over time especially in an adversarial environment, the interests of human expert could also change accordingly regarding to the detected anomaly types. It brings further challenges to conventional anomaly detection algorithms as they are often applied in a batch setting and are incapable to interact with the environment. To tackle the above issues, in this paper, we investigate the problem of anomaly detection on attributed networks in an interactive setting by allowing the system to proactively communicate with the human expert in making a limited number of queries about ground truth anomalies. Our objective is to maximize the true anomalies presented to the human expert after a given budget is used up. Along with this line, we formulate the problem through the principled multi-armed bandit framework and develop a novel collaborative contextual bandit algorithm, named GraphUCB. In particular, our developed algorithm: (1) explicitly models the nodal attributes and node dependencies seamlessly in a joint framework; and (2) handles the exploration-exploitation dilemma when querying anomalies of different types. Extensive experiments on real-world datasets show the improvement of the proposed algorithm over the state-of-the-art algorithms.
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