惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

MongoDB | Blog
MongoDB | Blog
J
Java Code Geeks
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
D
DataBreaches.Net
腾讯CDC
GbyAI
GbyAI
I
InfoQ
博客园 - Franky
G
Google Developers Blog
Last Week in AI
Last Week in AI
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
V
Visual Studio Blog
Vercel News
Vercel News
博客园_首页
MyScale Blog
MyScale Blog
Martin Fowler
Martin Fowler
N
Netflix TechBlog - Medium
V
V2EX
T
The Blog of Author Tim Ferriss
M
MIT News - Artificial intelligence
雷峰网
雷峰网
H
Hackread – Cybersecurity News, Data Breaches, AI and More
大猫的无限游戏
大猫的无限游戏
The GitHub Blog
The GitHub Blog

cs.SI updates on arXiv.org

Hiding in Plain Sight: Finding MAHA on Reddit Prism: Structural Symmetry Scanning via Duality-Constrained Laplacian Projection MV-Gate: Insider Threat Detection via Multi-View Behavioral Statistics and Semantic Modeling Algorithmic Cultivation: How Social Media Feeds Shape User Language Universal Dynamics of Punctuated Progress AI-Mediated Communication Can Steer Collective Opinion CitePrism: Human-in-the-Loop AI for Citation Auditing and Editorial Integrity Explainable Detection of Depression Status Shifts from User Digital Traces Can Visual Mamba Improve AI-Generated Image Detection? An In-Depth Investigation ScioMind: Cognitively Grounded Multi-Agent Social Simulation with Anchoring-Based Belief Dynamics and Dynamic Profiles Humanwashing -- It Should Leave You Feeling Dirty When Do LLMs Generate Realistic Social Networks? A Multi-Dimensional Study of Culture, Language, Scale, and Method Moltbook Moderation: Uncovering Hidden Intent Through Multi-Turn Dialogue Linking Extreme Discourse to Structural Polarization in Signed Interaction Networks Predicting Channel Closures in the Lightning Network with Machine Learning Latent Causal Void: Explicit Missing-Context Reconstruction for Misinformation Detection Predictive Maps of Multi-Agent Reasoning: A Successor-Representation Spectrum for LLM Communication Topologies Large Language Models for Causal Relations Extraction in Social Media: A Validation Framework for Disaster Intelligence When Can Digital Personas Reliably Approximate Human Survey Findings? RAwR: Role-Aware Rewiring via Approximate Equitable Partition GravityGraphSAGE: Link Prediction in Directed Attributed Graphs Structure-Centric Graph Foundation Model via Geometric Bases Attention-based graph neural networks: a survey When AI Meets Science: Research Diversity, Interdisciplinarity, Visibility, and Retractions across Disciplines in a Global Surge Scalable inference of spatial regions and temporal signatures from time series Can LLMs Emulate Human Belief Dynamics? Predicting Post Virality with Temporal Cross-Attention over Trend Signals H3: A Healthcare Three-Hop Index for Physician Referral Network Prediction Dynamic Graph with Similarity-Aware Attention Graph Neural Network for Recommender Systems Spectral Graph Sparsification Preserves Representation Geometry in Graph Neural Networks
DarkGram: A Large-Scale Analysis of Cybercriminal Activit...
Sayak Saha Roy, Elham Pourabbas Vafa, Kobra Khanmohammadi, Shiri · 2024-09-23 · via cs.SI updates on arXiv.org

We present the first large-scale analysis of 339 cybercriminal activity channels (CACs). Followed by over 23.8 million users, these channels share a wide array of malicious and unethical content with their subscribers, including compromised credentials, pirated software and media, social media manipulation tools, and blackhat hacking resources such as malware, exploit kits, and social engineering scams. To evaluate these channels, we developed DarkGram, a BERT-based framework that automatically identifies malicious posts from the CACs with an accuracy of 96%. Using DarkGram, we conducted a quantitative analysis of 53,605 posts shared on these channels between February and May 2024, revealing key characteristics of the content. While much of this content is distributed for free, channel administrators frequently employ strategies such as promotions and giveaways to engage users and boost the sales of premium cybercriminal content. Interestingly, these channels sometimes pose significant risks to their own subscribers. Notably, 28.1% of the links shared in these channels contained phishing attacks, and 38% of executable files were bundled with malware. Analyzing how subscribers consume and positively react to the shared content paints a dangerous picture of the perpetuation of cybercriminal content at scale. We also found that the CACs can evade scrutiny or platform takedowns by quickly migrating to new channels with minimal subscriber loss, highlighting the resilience of this ecosystem. To counteract this, we utilized DarkGram to detect emerging channels and reported malicious content to Telegram and affected organizations. This resulted in the takedown of 196 channels over three months. Our findings underscore the urgent need for coordinated efforts to combat the growing threats posed by these channels. To aid this effort, we open-source our dataset and the DarkGram framework.