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Camouflaged Chinese Spam Content Detection with Semi-supervised Generative Active Learning
2020
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
unpublished
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We propose a Semi-supervIsed GeNerative Active Learning (SIGNAL) model to address the imbalance, efficiency, and text camouflage problems of Chinese text spam detection task. ...
To the best of our knowledge, this is the first work to integrate active learning and semisupervised generative learning for text spam detection. ...
Conclusion In this paper, we propose a SIGNAL model for Chinese text spam detection. SIGNAL integrates active learning and semi-supervised generative learning into a unified framework. ...
doi:10.18653/v1/2020.acl-main.279
fatcat:vd5q67cgmnddrbqg3hk2gofk34
Social Fraud Detection Review: Methods, Challenges and Analysis
[article]
2021
arXiv
pre-print
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With this framework, a comprehensive overview of approaches is presented including supervised, semi-supervised, and unsupervised learning. ...
The supervised approaches for fraud detection are introduced and categorized into two sub-categories; classical, and deep learning. ...
Social Fraud in Comparison with Other Types of Spam Compared with other spam contents (e.g., email spam, insults, threats, malicious links, and fake news), fraud review detection is more challenging. ...
arXiv:2111.05645v1
fatcat:qp3zuv74lbaq3hw2ajxm6lfkim
Fraud Detection in Online Product Review Systems via Heterogeneous Graph Transformer
2021
IEEE Access
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This observation demonstrates the advan- tage of semi-supervised learning, where a small fraction of supervised signals is enough to optimize model parameters and generate informative node representation ...
The whole model is trained in a semi-supervised manner.
V. ...
doi:10.1109/access.2021.3084924
fatcat:wzzwnmdptnfm5hvarripls7heu
Fake Reviewer Group Detection in Online Review Systems
[article]
2021
arXiv
pre-print
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Previous methods tackle this problem by detecting malicious individuals, ignoring the fact that the spam activities are often formed in groups, where individuals work collectively to write fake reviews ...
First, cohensive groups are detected with modularity-based graph convolutional networks. ...
Welling, “Semi-supervised classification with graph [39] A. Grover and J. ...
arXiv:2112.06403v1
fatcat:vf6ku3uezva23fuoi6kohnooay
Contextual Multi-View Query Learning for Short Text Classification in User-Generated Data
[article]
2021
arXiv
pre-print
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We propose a novel multi-view active learning model, called Context-aware Co-testing with Bagging (COCOBA), to address these issues in the classification tasks tailored for a query word--e.g., detecting ...
Mining user-generated content--e.g., for the early detection of outbreaks or for extracting personal observations--often suffers from the lack of enough training data, short document length, and informal ...
Overcoming practical issues of deep active learn- 2020. Camouflaged Chinese spam content detection
ing and its applications on named entity recognition. ...
arXiv:2112.02611v1
fatcat:t4c63auyqndwrpvx6xs3afeqoq
A review of machine learning approaches to Spam filtering
2009
Expert systems with applications
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In this paper, we present a comprehensive review of recent developments in the application of machine learning algorithms to Spam filtering, focusing on both textual-and image-based approaches. ...
Instead of considering Spam filtering as a standard classification problem, we highlight the importance of considering specific characteristics of the problem, especially concept drift, in designing new ...
In the semi-supervised scenario, two classifiers were simultaneously trained, with the features being split for each one. ...
doi:10.1016/j.eswa.2009.02.037
fatcat:gf5z34w6arcdzh2w36tgefqppa
A Comprehensive Survey on Graph Anomaly Detection with Deep Learning
[article]
2021
arXiv
pre-print
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For the advent of deep learning, graph anomaly detection with deep learning has received a growing attention recently. ...
, and social spam. ...
When label information is available/partiallyavailable, supervised/semi-supervised detection models could be effectively trained. ...
arXiv:2106.07178v4
fatcat:efargsqnxndqbfqat2q5iz54u4
Online Social Deception and Its Countermeasures for Trustworthy Cyberspace: A Survey
[article]
2020
arXiv
pre-print
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Cyber attackers have exploited the sophisticated features of SNSs to carry out harmful OSD activities, such as financial fraud, privacy threat, or sexual/labor exploitation. ...
of social deception; (ii) types of OSD attacks and their unique characteristics compared to other social network attacks and cybercrimes; (iii) comprehensive defense mechanisms embracing prevention, detection ...
spam to non-spam ratios and
continuous sampling method, ground
truth from commercial tool
[21]
Naïve Bayes, logistic regression, RF and semi-
supervised spam detection
Hashtag, content, user ...
arXiv:2004.07678v1
fatcat:k4a6siywefb6lhkmyn67lmoqwe
Online Social Deception and Its Countermeasures: A Survey
2020
IEEE Access
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Based on this survey, we provide insights into the effectiveness of countermeasures and the lessons learned from the existing literature. ...
Cyber attackers have exploited the sophisticated features of SNSs to carry out harmful OSD activities, such as financial fraud, privacy threat, or sexual/labor exploitation. ...
[91] detected user profiles across multiple OSNs in a supervised learning classifier. ...
doi:10.1109/access.2020.3047337
fatcat:xw2rr2sjnrdf3nk4vfuowrkizy
Financial Cybercrime: A Comprehensive Survey of Deep Learning Approaches to Tackle the Evolving Financial Crime Landscape
2021
IEEE Access
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Welling, “Semi-supervised classification with graph //dx.doi.org/10.1016/j.jnca.2015.11.016
convolutional networks,” 5th International Conference on Learning Rep ...
active learning,” 2020. ...
doi:10.1109/access.2021.3134076
fatcat:lm2upcaoabbnbie6r4sfzhjh4y
Machine Bias: Artificial Intelligence and Discrimination
2019
Social Science Research Network
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For instance, artificial intelligence is used to detect spam emails and to do so, it needs to learn the difference between spam and non-spam emails. ...
Spam email fighting aims to detect spam emails and automatically move them into junk folder. ...
doi:10.2139/ssrn.3439702
fatcat:54zv7vvppne7dn6gcfh3phxt5e
Real-Time Detection Method for Surface Defects of Stamping Parts Based on Template Matching*
2019
IOP Conference Series: Earth and Environment
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Studies, focusing on network security, have experienced four main stages: idealized design for ensuring security, auxiliary examination and passive defense, active analysis and strategy formulation, and ...
He has been engaged in software development for 4 years in active network, and later served as general manager in medium-sized software enterprises. He has rich theoretical and practical experience. ...
This method is generally associated with the alarm mechanism of IDS and is widely used in intrusion detection. ...
doi:10.1088/1755-1315/252/2/022076
fatcat:rqbvta44rrcivoyjzy5pebwdwe
Analysis framework of network security situational awareness and comparison of implementation methods
2019
EURASIP Journal on Wireless Communications and Networking
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Studies, focusing on network security, have experienced four main stages: idealized design for ensuring security, auxiliary examination and passive defense, active analysis and strategy formulation, and ...
He has been engaged in software development for 4 years in active network, and later served as general manager in medium-sized software enterprises. He has rich theoretical and practical experience. ...
This method is generally associated with the alarm mechanism of IDS and is widely used in intrusion detection. ...
doi:10.1186/s13638-019-1506-1
fatcat:lzamend3krbfhmntenrhmywx2u
D1.1 - State of the Art Analysis
2021
Zenodo
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The deliverable starts with an overview of AI applications and edge computing market trends. ...
Then, the deliverable provides a background on AI applications design, also considering some advanced design trends (e.g., Network Architecture Search, Federated Learning, Deep Neural Networks partitioning ...
A possible solution comes from a learning paradigm which sits between the unsupervised and supervised learning ones, which can be defined as semi-supervised learning. ...
doi:10.5281/zenodo.6372377
fatcat:f6ldfuwivbcltew4smiiwphfty
SIENNA D4.4: Ethical Analysis of AI and Robotics Technologies
2020
Zenodo
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The experience focuses on the algorithm's learning process and can be divided into supervised and unsupervised learning. 78 Supervised learning is the most common form. ...
humans. 328, 329 In the supervised and semi-autonomous systems, the need to hand off control from robot to human at various points of operation present challenges with safety implications. 330 Some of ...
doi:10.5281/zenodo.4068082
fatcat:xiucqv6opng6rbit25lyfemzm4
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