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首页> 外文会议>IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining >A Novel Approach for Detection and Ranking of Trendy and Emerging Cyber Threat Events in Twitter Streams
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A Novel Approach for Detection and Ranking of Trendy and Emerging Cyber Threat Events in Twitter Streams

机译：在Twitter流中检测和排名网络趋势和新兴网络威胁事件的新颖方法

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摘要

We present a new machine learning and text information extraction approach to detection of cyber threat events in Twitter that are novel (previously non-extant) and developing (marked by significance with respect to similarity with a previously detected event). While some existing approaches to event detection measure novelty and trendiness, typically as independent criteria and occasionally as a holistic measure, this work focuses on detecting both novel and developing events using an unsupervised machine learning approach. Furthermore, our proposed approach enables the ranking of cyber threat events based on an importance score by extracting the tweet terms that are characterized as named entities, keywords, or both. We also impute influence to users in order to assign a weighted score to noun phrases in proportion to user influence and the corresponding event scores for named entities and keywords. To evaluate the performance of our proposed approach, we measure the efficiency and detection error rate for events over a specified time interval, relative to human annotator ground truth.
机译：我们提出了一种新的机器学习和文本信息提取方法，用于检测Twitter中新颖（以前不存在）和发展中的网络威胁事件（通过与先前检测到的事件的相似性来进行标记）。尽管一些现有的事件检测方法通常以独立的标准（有时甚至是整体性的方法）来衡量新颖性和趋势性，但这项工作着重于使用无监督的机器学习方法来检测新颖的事件和发展中的事件。此外，我们提出的方法通过提取表征为命名实体，关键字或二者的推文术语，从而能够基于重要性得分对网络威胁事件进行排名。我们还估算影响力，以便为名词短语分配与用户影响力以及命名实体和关键字的相应事件分数成比例的加权分数。为了评估我们提出的方法的性能，我们测量了在指定时间间隔内相对于人类注释者地面真实性的事件的效率和检测错误率。

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