基於混合式監督學習法之線上社群網路異常偵測

A Hybrid Supervised Learning Approach for Anomaly Detection in Social Networks

謝芷玲(Chi-Leng Che)

指導教授 ： 李永銘

國立交通大學/管理學院/資訊管理研究所/碩士(2018年)

摘要

隨著線上社群網路的迅速發展，世界各地都在各個社交平台上提供的大量信息。儘管大部分的資訊都是有用的，但在平台上仍然存在着一些實體利用異常用戶傳播惡意內容（如垃圾郵件或謠言以實現其金錢或政治目標）。本文中，我們提出了根據用戶的個人資料，以及過去發布的動態的資料去建立異常用戶偵測機制。除了以往文獻考慮的特徵之外，我們還設計了幾個與近似重複內容（包括詞彙相似度，語義相似度）相關的特徵，並結合以監督式學習方法以提高異常用戶檢測的準確性

關鍵字

線上社群網路 ； 異常偵測 ； 近似重複計算 ； 監督式學習

並列摘要

With the rapid development of online social network (OSN), worldwide connections between each individual turn into much stronger. A huge number of information provided by some entities around the world are well dispersed in OSN every day. Most of those are useful but not all as anomalous entities utilize anomaly users to spread malicious content (like spam or rumors to achieve their pecuniary or political aims. In this paper, we propose a mechanism to detect such anomaly users according to the user profile and tweet content of each user. Besides the features considered in the past literature, we design several new features related to near-duplicate content (including lexical similarity, semantic similarity) to enhance the precision of detecting anomaly users. Utilizing the data by public honeypot dataset, the proposed approach deals with supervised learning approach to carry out the detection task.

並列關鍵字

Online Social Network ； Anomaly Detection ； Near-Duplicate Computing ； Supervised Learning Approach

參考文獻

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