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Found 6 papers, 0 papers with code
BotSSCL: Social Bot Detection with Self-Supervised Contrastive Learning
To address these challenges, we propose a novel framework for social Bot detection with Self-Supervised Contrastive Learning (BotSSCL).
False Information, Bots and Malicious Campaigns: Demystifying Elements of Social Media Manipulations
While researchers from various disciplines have investigated different manipulation-triggering elements of OSNs (such as understanding information diffusion on OSNs or detecting automated behavior of accounts), these works have not been consolidated to present a comprehensive overview of the interconnections among these elements.
SPGNN-API: A Transferable Graph Neural Network for Attack Paths Identification and Autonomous Mitigation
Attack paths are the potential chain of malicious activities an attacker performs to compromise network assets and acquire privileges through exploiting network vulnerabilities.
Machine Learning-based Automatic Annotation and Detection of COVID-19 Fake News
Existing work neglects the presence of bots that act as a catalyst in the spread and focuses on fake news detection in 'articles shared in posts' rather than the post (textual) content.
Security and Privacy in IoT Using Machine Learning and Blockchain: Threats & Countermeasures
In this paper, we provide a summary of research efforts made in the past few years, starting from 2008 to 2019, addressing security and privacy issues using ML algorithms and BCtechniques in the IoT domain.
DaDiDroid: An Obfuscation Resilient Tool for Detecting Android Malware via Weighted Directed Call Graph Modelling
We examine the graph features of mobile apps code by building weighted directed graphs of the API calls, and verify that malicious apps often share structural similarities that can be used to differentiate them from benign apps, even under a heavily "polluted" training set where a large majority of the apps are obfuscated.
Cryptography and Security
