Metric learning aims to learn distances from the data, which enhances the
performance of similarity-based algorithms. An author style detection task is a
metric learning problem, where learning style features with small intra-class
variations and larger inter-class differences is of great importance to achieve
better performance. Recently, metric learning based on softmax loss has been
used successfully for style detection. While softmax loss can produce separable
representations, its discriminative power is relatively poor. In this work, we
propose NBC-Softmax, a contrastive loss based clustering technique for softmax
loss, which is more intuitive and able to achieve superior performance. Our
technique meets the criterion for larger number of samples, thus achieving
block contrastiveness, which is proven to outperform pair-wise losses. It uses
mini-batch sampling effectively and is scalable. Experiments on 4 darkweb
social forums, with NBCSAuthor that uses the proposed NBC-Softmax for author
and sybil detection, shows that our negative block contrastive approach
constantly outperforms state-of-the-art methods using the same network
architecture.
Our code is publicly available at : this https URL

本文提出 NBC-Softmax 对比损失聚类技术，应用于作者风格检测和 Sybil 检测，相较于现有方法性能更优。

NBC-Softmax: 暗网作者指纹识别和迁移跟踪

NBC-Softmax : Darkweb Author fingerprinting and migration tracking

Sybil detection in social networks is a basic security research problem.
Structure-based methods have been shown to be promising at detecting Sybils.
Existing structure-based methods can be classified into Random Walk (RW)-based
methods and Loop Belief Propagation (LBP)-based methods. RW-based methods
cannot leverage labeled Sybils and labeled benign users simultaneously, which
limits their detection accuracy, and/or they are not robust to noisy labels.
LBP-based methods are not scalable and cannot guarantee convergence. In this
work, we propose SybilSCAR, a novel structure-based method to detect Sybils in
social networks. SybilSCAR is Scalable, Convergent, Accurate, and Robust to
label noise. We first propose a framework to unify RW-based and LBP-based
methods. Under our framework, these methods can be viewed as iteratively
applying a (different) local rule to every user, which propagates label
information among a social graph. Second, we design a new local rule, which
SybilSCAR iteratively applies to every user to detect Sybils. We compare
SybilSCAR with state-of-the-art RW-based and LBP-based methods theoretically
and empirically. Theoretically, we show that, with proper parameter settings,
SybilSCAR has a tighter asymptotical bound on the number of Sybils that are
falsely accepted into a social network than existing structure-based methods.
Empirically, we perform evaluation using both social networks with synthesized
Sybils and a large-scale Twitter dataset (41.7M nodes and 1.2B edges) with real
Sybils. Our results show that 1) SybilSCAR is substantially more accurate and
more robust to label noise than state-of-the-art RW-based methods; 2) SybilSCAR
is more accurate and one order of magnitude more scalable than state-of-the-art
LBP-based methods.

本文提出了 SybilSCAR，一种新的基于结构的 Sybil 检测方法，其在理论和实验中都展现出比基于 Random Walk 和 Loop Belief Propagation 的现有方法更高的准确性，更强的鲁棒性，更高的可扩展性。

基于结构的局部规则传播策略在社交网络中的 Sybil 检测

Structure-based Sybil Detection in Social Networks via Local Rule-based  Propagation

Popular User-Review Social Networks (URSNs)---such as Dianping, Yelp, and
Amazon---are often the targets of reputation attacks in which fake reviews are
posted in order to boost or diminish the ratings of listed products and
services. These attacks often emanate from a collection of accounts, called
Sybils, which are collectively managed by a group of real users. A new advanced
scheme, which we term elite Sybil attacks, recruits organically highly-rated
accounts to generate seemingly-trustworthy and realistic-looking reviews. These
elite Sybil accounts taken together form a large-scale sparsely-knit Sybil
network for which existing Sybil fake-review defense systems are unlikely to
succeed. In this paper, we conduct the first study to define, characterize, and
detect elite Sybil attacks. We show that contemporary elite Sybil attacks have
a hybrid architecture, with the first tier recruiting elite Sybil workers and
distributing tasks by Sybil organizers, and with the second tier posting fake
reviews for profit by elite Sybil workers. We design ElsieDet, a three-stage
Sybil detection scheme, which first separates out suspicious groups of users,
then identifies the campaign windows, and finally identifies elite Sybil users
participating in the campaigns. We perform a large-scale empirical study on ten
million reviews from Dianping, by far the most popular URSN service in China.
Our results show that reviews from elite Sybil users are more spread out
temporally, craft more convincing reviews, and have higher filter bypass rates.
We also measure the impact of Sybil campaigns on various industries (such as
cinemas, hotels, restaurants) as well as chain stores, and demonstrate that
monitoring elite Sybil users over time can provide valuable early alerts
against Sybil campaigns.

本篇论文研究了虚假评论和 Sybil 攻击，通过设计了一种三阶段的 Sybil 检测方案，可以检测出有潜在威胁的用户组，识别 Sybil 活动窗口，并识别参与活动的精英 Sybil 用户，同时研究表明精英 Sybil 攻击对各个行业的影响，并展示了对精英 Sybil 用户进行监测所提供的有价值的预警。