Identifying Malicious Users in the Offshore Leaks Networks via Structural Node Representation Learning
Identifying Malicious Users in the Offshore Leaks Networks via Structural Node Representation Learning
复制标题
通过结构节点表示学习识别离岸泄漏网络中的恶意用户
DOI：
10.1109/bigdata52589.2021.9671914
复制
发表时间：
2021
期刊：
影响因子：
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通讯作者：
A. Cuzzocrea
中科院分区：
文献类型：
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作者：
Brian Daley;Edoardo Serra;A. Cuzzocrea
从2013年开始，国际调查记者联盟发布了一系列网络，称为离岸泄密网络，详细介绍了离岸账户的实体和交易信息。通过与已知的实体黑名单相互参照，能够在所提供的网络中查明非法个人和交易。在机器学习研究中，Offshore Leaks Networks从大型数据库中提取数据，对高维空间中的许多节点进行分类。节点分类的主要问题是非法实体并不总是已知的，并且已经设计了技术来解决这个问题，例如中心性和基于结构的学习。在本文中，SparseStruct-由Serra等人开发的算法。[1]-被证明可以实现最佳结果。这是因为它使用了结构节点表征学习技术，能够识别图中的特定结构模式。该技术实现了0.61和0.81之间的AUROC分数，其中四个分数中的三个是所有分类器的最高分数。
Starting in 2013, the International Consortium of Investigative Journalists released a series of networks, known as the Offshore Leaks Networks, detailing the information of entities and transactions of offshore accounts. Through cross-referencing with known blacklists of entities, illicit individuals and transactions were able to be identified in the networks provided. In machine learning research, the Offshore Leaks Networks draws off of large databases of data to classify many nodes in high dimensional space. The chief problem with node classification is that the illicit entities are not always known, and techniques have been devised to tackle this problem, such as centrality and structural-based learning. In this paper, SparseStruct—the algorithm developed by Serra et al. [1]— is shown to achieve the best results. This is because it uses a structural node representational learning technique able to identify specific structural patterns in the graph. This technique achieved AUROC scores of between 0.61 and 0.81, with three of the four scores being the top score of all classifiers compared.
DeepTrust：在基于意见的系统中检测可信用户的自动框架
DOI：
10.1145/3374664.3375744
发表时间：
2020
期刊：
CODASPY'20: Tenth ACM Conference on Data and Application Security and Privacy
影响因子：
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作者：
Serra, Edoardo;Shrestha, Anu;Spezzano, Francesca;Squicciarini, Anna
通讯作者：
Squicciarini, Anna