GANs for Semi-Supervised Opinion Spam Detection

Stanton, Gray; Irissappane, Athirai A.

doi:10.24963/ijcai.2019/723

Cited by 35 publications

(6 citation statements)

References 3 publications

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“…Li et al [46] proposed "CS-GAN", a model that combines reinforcement learning, generative adversarial networks, and recurrent neural networks to generate category sentences. Stanton et al [47] implemented a generative adversarial network that uses a limited set of labeled data to detect spam in online posts.…”

Section: Related Workmentioning

confidence: 99%

An Evolutionary Computation Approach for Twitter Bot Detection

et al. 2022

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Bot accounts are automated software programs that act as legitimate human profiles on social networks. Identifying these kinds of accounts is a challenging problem due to the high variety and heterogeneity that bot accounts exhibit. In this work, we use genetic algorithms and genetic programming to discover interpretable classification models for Twitter bot detection with competitive qualitative performance, high scalability, and good generalization capabilities. Specifically, we use a genetic programming method with a set of primitives that involves simple mathematical operators. This enables us to discover a human-readable detection algorithm that exhibits a detection accuracy close to the top state-of-the-art methods on the TwiBot-20 dataset while providing predictions that can be interpreted, and whose uncertainty can be easily measured. To the best of our knowledge, this work is the first attempt at adopting evolutionary computation techniques for detecting bot profiles on social media platforms.

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Section: Related Workmentioning

confidence: 99%

An Evolutionary Computation Approach for Twitter Bot Detection

et al. 2022

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“…Most consumers read product reviews as a consideration to buy a product [1]. The textual content in product reviews plays a significant role in controlling consumers' behavior and product demand variation [2].…”

Section: Introductionmentioning

confidence: 99%

“…1 )} =1 , and 3 = {( 3 ,3 )} = +1 + define as product review text with Type-1 labeled and product review text with Type-3 labeled, respectively. Type-1 data are labeled based on shingling method and Type-3 spam text are manually labeled.…”

mentioning

confidence: 99%

Text Generation with Content and Structure-Based Preprocessing in Imbalanced Data of Product Review

Zaqiyah¹,

Purwitasari²,

Fatichah³

2021

IJIES

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Spam detection frequently categorizes product reviews as spam and non-spam. The spam reviews may contain texts of fake reviews and non-review statements describing unrelated things about products. Most of the publicly available spam reviews are labelled as fake reviews, while non-spam texts that are not fake reviews could contain non-review statements. It is crucial to notice those non-review statements since they convey misperception to consumers. Non-review statements are hardly found, and those statements of large and long texts often need to be manually labelled, which is time-consuming. Because of the rareness in finding non-review statements, there is an imbalanced condition between non-spam as a major class and spam that consists of the non-review statement as a minor class. Augmenting fake reviews to add spam texts is ineffective because they have similar content to non-spam such as some opinion words of product features. Thus, the text generation of non-review statements is preferable for adding spam texts. Some text generation issues are the frequent neural network-based methods require much learning data, and the existing pre-trained models produce texts with different contexts to non-review statements. The augmented texts should have similar content and context represented by the structure of the non-review statement. Therefore, we propose a text generation model with content and structure-based preprocessing to produce non-review statements, which is expected to overcome imbalanced data and give better spam detection results in product reviews. Structure-based preprocessing identifies the feature structures of non-opinion words from part-of-speech tags. Those features represent the context of spam reviews in unlabeled texts. Then, content-based preprocessing appoints selected topic modeling results of non-review statements from fake reviews. Our experiments resulted an improvement on the metric value of ± 0.04, called as BLEU (Bi-Lingual Evaluation Understudy) score, for the correspondence evaluation between generated and trained texts. The metric value indicates that the generated texts are not quite identical to the trained texts of non-review statements. However, those additional texts combined with the original spam texts gave better spam detection results with an increasing value of more than 40% on average recall score.

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“…Adversarial models have been recently used to evade machine learning classifiers. GAN has been used in intrusion detection [12], malware detection [9], and spam detection [31], and phishing [14].…”

Section: Introductionmentioning

confidence: 99%

Bypassing Detection of URL-based Phishing Attacks Using Generative Adversarial Deep Neural Networks

Aleroud

Karabatis

2020

Proceedings of the Sixth International Workshop on Security and Privacy Analytics

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The URL components of web addresses are frequently used in creating phishing detection techniques. Typically, machine learning techniques are widely used to identify anomalous patterns in URLs as signs of possible phishing. However, adversaries may have enough knowledge and motivation to bypass URL classification algorithms by creating examples that evade classification algorithms. This paper proposes an approach that generates URL-based phishing examples using Generative Adversarial Networks. The created examples can fool Blackbox phishing detectors even when those detectors are created using sophisticated approaches such as those relying on intra-URL similarities. These created instances are used to deceive Blackbox machine learning-based phishing detection models. We tested our approach using actual phishing datasets. The results show that GAN networks are very effective in creating adversarial phishing examples that can fool both simple and sophisticated machine learning phishing detection models.

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GANs for Semi-Supervised Opinion Spam Detection

Cited by 35 publications

References 3 publications

An Evolutionary Computation Approach for Twitter Bot Detection

An Evolutionary Computation Approach for Twitter Bot Detection

Text Generation with Content and Structure-Based Preprocessing in Imbalanced Data of Product Review

Bypassing Detection of URL-based Phishing Attacks Using Generative Adversarial Deep Neural Networks

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