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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 林宗男(Tsung-Nan Lin) | |
| dc.contributor.author | Hsing-Yu Shih | en |
| dc.contributor.author | 施星宇 | zh_TW |
| dc.date.accessioned | 2023-03-19T23:33:44Z | - |
| dc.date.copyright | 2022-09-19 | |
| dc.date.issued | 2022 | |
| dc.date.submitted | 2022-09-16 | |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/86033 | - |
| dc.description.abstract | 近年來線上金融服務的影響力變得越來越大，然而，惡意犯罪集團可能會控制線上金融服務的帳號以進行詐騙行為，由於這些惡意帳戶經常由相同的犯罪集團帳控，這類帳戶之間可以被觀察到不同於一般帳戶的關聯性，使得這些帳戶能被聚集形成一個帳戶群，藉由發掘這些可疑的帳戶群將可以達到偵測惡意帳戶的目的。 為了要發掘帳戶之間的關聯性以將帳戶群聚，過去的研究著重於使用單一身份特徵來確認帳戶背後是否由同一人或是同一集團所控制，然而單一身份特徵容易因為特徵可能因為巧合發生共用而造成關聯上雜訊，相對的，若使用多個身份特徵來建立帳戶關聯，因有更多證據可推論關聯性的存在，能有更少的關聯上的雜訊。 在這篇論文中，我們提出AI-URG演算法來偵測線上銀行服務中可疑的異常帳戶群，AI-URG基於不確定圖技術建立帳戶身份關聯，考慮到單一身份特徵對於帳戶存在關聯性提供的證據不足，以及考慮到身份特徵得對應與真實世界的身份實體仍存在不確定性，我們提出multi-factor identity modeling能將帳戶間因由同一群人所操控而存在關聯之機率以不確定圖表示。 為了要從帳戶關聯的不確定圖中偵測出可疑帳戶群，我們提出了DeepURGE，DeepURGE基於建立關聯圖中帳戶的特徵向量，來找出高相關性的帳戶群，此外，聚類後的高關聯性帳戶群並非皆為可疑帳戶群，我們基於惡意帳戶偵測的基礎知識設計了篩選策略，以從決定帳戶群是否為可疑帳戶群。 我們以真實世界線上銀行服務的資料集驗證AI-URG的有效性，結果顯示AI-URG可以有效的偵測資料集中78.2%已標示的惡意帳戶，且與其他方法比較，AI-URG能得到更高的F1 score(58.0%)以及更高的精確度(46.0%)。 | zh_TW |
| dc.description.abstract | Online banking has been increasingly important nowadays. Unfortunately, some malicious groups may control accounts to conduct fraud activities. Because the same criminal group holds malicious accounts, suspicious accounts form the communities and can be observed. Previous works focus on related accounts with single-factor identity to find those suspicious communities, while multi-factor identity is less susceptible to noise. In this work, we proposed AI-URG for detecting suspicious account groups with account identity uncertain graph. Because of the insufficient single-factor identity and uncertainty binding between accounts and identity, we model identity level relations with multi-factor identity modeling an uncertain graph. To detect suspicious account groups in the uncertain graph, we propose DeepURGE produce account representation and find the account communities. Since some communities are benign, we determine whether it is suspicious with a strategy based on domain knowledge. We evaluate AI-URG with a real-world dataset. The result shows that it can detect labeled suspicious accounts and outperform alternatives with higher F1 score(58.0%) and precision(46.0%). | en |
| dc.description.provenance | Made available in DSpace on 2023-03-19T23:33:44Z (GMT). No. of bitstreams: 1 U0001-1409202217303200.pdf: 5198906 bytes, checksum: dc2594a7e282bd39d36a03665a99c0be (MD5) Previous issue date: 2022 | en |
| dc.description.tableofcontents | 口試委員會審定書 i 致謝 iii 中文摘要 v Abstract vii 1 Introduction 1 2 Background 5 2.1 Online service malicious accounts detection 5 2.2 Web tracking 6 2.3 IP Dynamics 7 2.4 Uncertain Graph 8 3 Method 11 3.1 Account Identity Uncertain Graph 11 3.1.1 Framework of Multi-factor Identity Modeling 12 3.1.2 Coincident Identifier Probability Function of IP address 15 3.2 Suspicious Account Groups Detecting 19 3.2.1 Feature Learning Based Uncertain Graph Embedding: DeepURGE 20 3.2.2 Detecting Suspicious Account Group with Clusters 21 4 Results and Discussions 25 4.1 Dataset 25 4.2 Approach 28 4.3 Results 29 4.3.1 Comparison of AI-URG and Alternative 29 4.3.2 IP Duration Modeling and Comparison Between Connection Types 32 4.3.3 Find Communities with DeepURGE 32 4.4 Ablation Studies 33 4.4.1 Comparison of AI-URG and its Variants 33 4.4.2 Comparison of purning edges with different probability threshold 34 4.4.3 Comparison of selecting account groups with different group size 35 4.4.4 Comparison of different clustering approach in AI-URG 36 4.5 Discussion and Future Work 37 5 Conclusion 43 5.1 Conclusion 43 5.2 Acknowledgment 43 A Simulation of multi-factor account identity uncertain graph 45 B Proof of effectiveness of multi-factor identity to detect account groups 49 Bibliography 51 | |
| dc.language.iso | en | |
| dc.title | 基於帳戶身份關聯之不確定圖演算法於詐騙偵測 | zh_TW |
| dc.title | AI-URG: Account Identity Based Uncertain Graph Algorithm for Fraud Detection | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 110-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.author-orcid | 0000-0003-4111-3768 | |
| dc.contributor.oralexamcommittee | 鄧惟中(Wei-Chung Teng),陳俊良(Jiann-Liang Chen),沈上翔(Shan-Hsiang Shen) | |
| dc.subject.keyword | 詐騙偵測,不確定圖,帳戶關聯性網路,身份追蹤,節點嵌入, | zh_TW |
| dc.subject.keyword | fraud detection,uncertain graph,account identity relation,identity tracking,ode embedding, | en |
| dc.relation.page | 55 | |
| dc.identifier.doi | 10.6342/NTU202203410 | |
| dc.rights.note | 同意授權(全球公開) | |
| dc.date.accepted | 2022-09-19 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 電信工程學研究所 | zh_TW |
| dc.date.embargo-lift | 2022-09-19 | - |
| 顯示於系所單位： | 電信工程學研究所 | |
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| U0001-1409202217303200.pdf | 5.08 MB | Adobe PDF | 檢視/開啟 |
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