#userbehaviour — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #userbehaviour, aggregated by home.social.
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#Discovery and #Virality 🚀 #Algorithms powered by #AI identify viral trends and surface news faster than humans, amplifying the speed at which stories spread globally.
#Misinformation 🛡️ #AItools help platforms detect #fakenews, flag content, and promote credible sources, but challenges remain in balancing accuracy and freedom of speech.
#Engagement 💬 AI analyses #userbehaviour to optimise headlines, timing, and content types, keeping audiences engaged.
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📢 Join our #DataScience talk by Yllka Velaj to explore the power of understanding #SocialNetworks and #UserBehaviour on 📅 22 May @ 14:00 CEST on-site @univienna or online #Zoom https://datascience.univie.ac.at/dsunivie-talks/about/news/yllka-velaj-social-network-analysis-from-clustering-to-link-recommendation/
#DSHQDiscover #SpectralMix, an #Algorithm uncovering patterns in multi-relational #Networks, linking graph structure and attributes. Plus, learn about a #DynamicStudy using a link recommendation algorithm to maximise #InformationDiffusion
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📢 Join our #DataScience talk by Yllka Velaj to explore the power of understanding #SocialNetworks and #UserBehaviour on 📅 22 May @ 14:00 CEST on-site @univienna or online #Zoom https://datascience.univie.ac.at/dsunivie-talks/about/news/yllka-velaj-social-network-analysis-from-clustering-to-link-recommendation/
#DSHQDiscover #SpectralMix, an #Algorithm uncovering patterns in multi-relational #Networks, linking graph structure and attributes. Plus, learn about a #DynamicStudy using a link recommendation algorithm to maximise #InformationDiffusion
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📢 Join our #DataScience talk by Yllka Velaj to explore the power of understanding #SocialNetworks and #UserBehaviour on 📅 22 May @ 14:00 CEST on-site @univienna or online #Zoom https://datascience.univie.ac.at/dsunivie-talks/about/news/yllka-velaj-social-network-analysis-from-clustering-to-link-recommendation/
#DSHQDiscover #SpectralMix, an #Algorithm uncovering patterns in multi-relational #Networks, linking graph structure and attributes. Plus, learn about a #DynamicStudy using a link recommendation algorithm to maximise #InformationDiffusion
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📢 Join our #DataScience talk by Yllka Velaj to explore the power of understanding #SocialNetworks and #UserBehaviour on 📅 22 May @ 14:00 CEST on-site @univienna or online #Zoom https://datascience.univie.ac.at/dsunivie-talks/about/news/yllka-velaj-social-network-analysis-from-clustering-to-link-recommendation/
#DSHQDiscover #SpectralMix, an #Algorithm uncovering patterns in multi-relational #Networks, linking graph structure and attributes. Plus, learn about a #DynamicStudy using a link recommendation algorithm to maximise #InformationDiffusion
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Sounds good but MSFT Edge and Bing gather quite some amount of user data i guess. Just tapped on Edge browser / Bing Discover / analytics. #webbrowser #userdata #userbehaviour https://fosstodon.org/@infoidevice/110031530384160295
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CW: research review
Q. Wang et al., "EavesDroid: Eavesdropping User Behaviors via OS Side-Channels on Smartphones"¹
As the Internet of Things (IoT) continues to grow, smartphones have become an integral part of IoT systems. However, with the increasing amount of personal information stored on smartphones, users' privacy is at risk of being compromised by malicious attackers. Malware detection engines are commonly installed on smartphones to defend against these attacks, but new attacks that can evade these defenses may still emerge. In this paper, we present EavesDroid, a new side-channel attack on Android smartphones that allows an unprivileged attacker to accurately infer fine-grained user behaviors (e.g. viewing messages, playing videos) through the on-screen operations. Our attack relies on the correlation between user behaviors and the return values of system calls. The fact that these return values are affected by many factors, resulting in fluctuation and misalignment, makes the attack more challenging. Therefore, we build a CNN-GRU classification model, apply min-max normalization to the raw data and combine multiple features to identify the fine-grained user behaviors. A series of experiments on different models and systems of Android smartphones show that, EavesDroid can achieve an accuracy of 98% and 86% for already considered user behaviors in test set and real-world settings. To prevent this attack, we recommend malware detection, obfuscating return values or restricting applications from reading vulnerable return values.
#ResearchPapers #arXiv #IoT #OSSideChannels #Smartphone #UserBehaviour
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¹ https://arxiv.org/abs/2303.03700 -
Curious: are you using #featureflags ? Is it “only” for #userbehaviour / #performance reasons? Anyone using them for #security use cases?