Compromised account detection using authorship verification: a novel approach

• URL: http://arxiv.org/abs/2206.03581v2
• Date: Thu, 9 Jun 2022 07:03:50 GMT
• Title: Compromised account detection using authorship verification: a novel approach
• Authors: Forough Farazmanesh, Fateme Foroutan, Amir Jalaly Bidgoly
• Abstract summary: Compromising legitimate accounts is a way of disseminating malicious content to a large user base in Online Social Networks (OSNs) This paper proposes a novel approach based on authorship verification to identify compromised twitter accounts.
• Score: 1.0312968200748118
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Compromising legitimate accounts is a way of disseminating malicious content to a large user base in Online Social Networks (OSNs). Since the accounts cause lots of damages to the user and consequently to other users on OSNs, early detection is very important. This paper proposes a novel approach based on authorship verification to identify compromised twitter accounts. As the approach only uses the features extracted from the last user's post, it helps to early detection to control the damage. As a result, the malicious message without a user profile can be detected with satisfying accuracy. Experiments were constructed using a real-world dataset of compromised accounts on Twitter. The result showed that the model is suitable for detection due to achieving an accuracy of 89%.

Related papers

• Securing Voice Authentication Applications Against Targeted Data Poisoning [4.856070170902535]
  We propose an enhanced framework using realworld datasets considering realistic attack scenarios. The results show that the proposed approach is robust, providing accurate authentications even when only a small fraction of the dataset is poisoned.
  arXiv  Detail & Related papers  (2024-06-25T04:52:37Z)
• SeGA: Preference-Aware Self-Contrastive Learning with Prompts for Anomalous User Detection on Twitter [14.483830120541894]
  We propose SeGA, preference-aware self-contrastive learning for anomalous user detection. SeGA uses large language models to summarize user preferences via posts. We empirically validate the effectiveness of the model design and pre-training strategies.
  arXiv  Detail & Related papers  (2023-12-17T05:35:28Z)
• My Brother Helps Me: Node Injection Based Adversarial Attack on Social Bot Detection [69.99192868521564]
  Social platforms such as Twitter are under siege from a multitude of fraudulent users. Due to the structure of social networks, the majority of methods are based on the graph neural network(GNN), which is susceptible to attacks. We propose a node injection-based adversarial attack method designed to deceive bot detection models.
  arXiv  Detail & Related papers  (2023-10-11T03:09:48Z)
• Online Corrupted User Detection and Regret Minimization [49.536254494829436]
  In real-world online web systems, multiple users usually arrive sequentially into the system. We present an important online learning problem named LOCUD to learn and utilize unknown user relations from disrupted behaviors. We devise a novel online detection algorithm OCCUD based on RCLUB-WCU's inferred user relations.
  arXiv  Detail & Related papers  (2023-10-07T10:20:26Z)
• Verifying the Robustness of Automatic Credibility Assessment [79.08422736721764]
  Text classification methods have been widely investigated as a way to detect content of low credibility. In some cases insignificant changes in input text can mislead the models. We introduce BODEGA: a benchmark for testing both victim models and attack methods on misinformation detection tasks.
  arXiv  Detail & Related papers  (2023-03-14T16:11:47Z)
• Detecting fake accounts through Generative Adversarial Network in online social media [0.0]
  This paper proposes a novel method using user similarity measures and the Generative Adversarial Network (GAN) algorithm to identify fake user accounts in the Twitter dataset. Despite the problem's complexity, the method achieves an AUC rate of 80% in classifying and detecting fake accounts.
  arXiv  Detail & Related papers  (2022-10-25T10:20:27Z)
• Identification of Twitter Bots based on an Explainable ML Framework: the US 2020 Elections Case Study [72.61531092316092]
  This paper focuses on the design of a novel system for identifying Twitter bots based on labeled Twitter data. Supervised machine learning (ML) framework is adopted using an Extreme Gradient Boosting (XGBoost) algorithm. Our study also deploys Shapley Additive Explanations (SHAP) for explaining the ML model predictions.
  arXiv  Detail & Related papers  (2021-12-08T14:12:24Z)
• Spotting adversarial samples for speaker verification by neural vocoders [102.1486475058963]
  We adopt neural vocoders to spot adversarial samples for automatic speaker verification (ASV) We find that the difference between the ASV scores for the original and re-synthesize audio is a good indicator for discrimination between genuine and adversarial samples. Our codes will be made open-source for future works to do comparison.
  arXiv  Detail & Related papers  (2021-07-01T08:58:16Z)
• Backdoor Attack against Speaker Verification [86.43395230456339]
  We show that it is possible to inject the hidden backdoor for infecting speaker verification models by poisoning the training data. We also demonstrate that existing backdoor attacks cannot be directly adopted in attacking speaker verification.
  arXiv  Detail & Related papers  (2020-10-22T11:10:08Z)
• Friend or Faux: Graph-Based Early Detection of Fake Accounts on Social Networks [4.923937591056569]
  We present the SybilEdge algorithm, which determines whether a new user is a fake account by aggregating over (I) her choices of friend request targets and (II) these targets' respective responses. We show that SybilEdge rapidly detects new fake users at scale on the Facebook network and outperforms state-of-the-art algorithms.
  arXiv  Detail & Related papers  (2020-04-09T22:03:28Z)
• A Few Topical Tweets are Enough for Effective User-Level Stance Detection [8.118808561953514]
  We tackle stance detection for vocal Twitter users using two approaches. In the first approach, we improve user-level stance detection by representing tweets using contextualized embeddings. In the second approach, we expand the tweets of a given user using their Twitter timeline tweets, and then we perform unsupervised classification of the user.
  arXiv  Detail & Related papers  (2020-04-07T15:35:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.