Tweeting your Destiny: Profiling Users in the Twitter Landscape around an Online Game

Social media has become a major communication channel for communities centered around video games. Consequently, social media offers a rich data source to study online communities and the discussions evolving around games. Towards this end, we explore a large-scale dataset consisting of over 1 million tweets related to the online multiplayer shooter Destiny and spanning a time period of about 14 months using unsupervised clustering and topic modelling. Furthermore, we correlate Twitter activity of over 3,000 players with their playtime. Our results contribute to the understanding of online player communities by identifying distinct player groups with respect to their Twitter characteristics, describing subgroups within the Destiny community, and uncovering broad topics of community interest.Comment: Accepted at IEEE Conference on Games 201

A Survey of Machine Learning Techniques for Video Quality Prediction from Quality of Delivery Metrics

A growing number of video streaming networks are incorporating machine learning (ML) applications. The growth of video streaming services places enormous pressure on network and video content providers who need to proactively maintain high levels of video quality. ML has been applied to predict the quality of video streams. Quality of delivery (QoD) measurements, which capture the end-to-end performances of network services, have been leveraged in video quality prediction. The drive for end-to-end encryption, for privacy and digital rights management, has brought about a lack of visibility for operators who desire insights from video quality metrics. In response, numerous solutions have been proposed to tackle the challenge of video quality prediction from QoD-derived metrics. This survey provides a review of studies that focus on ML techniques for predicting the QoD metrics in video streaming services. In the context of video quality measurements, we focus on QoD metrics, which are not tied to a particular type of video streaming service. Unlike previous reviews in the area, this contribution considers papers published between 2016 and 2021. Approaches for predicting QoD for video are grouped under the following headings: (1) video quality prediction under QoD impairments, (2) prediction of video quality from encrypted video streaming traffic, (3) predicting the video quality in HAS applications, (4) predicting the video quality in SDN applications, (5) predicting the video quality in wireless settings, and (6) predicting the video quality in WebRTC applications. Throughout the survey, some research challenges and directions in this area are discussed, including (1) machine learning over deep learning; (2) adaptive deep learning for improved video delivery; (3) computational cost and interpretability; (4) self-healing networks and failure recovery. The survey findings reveal that traditional ML algorithms are the most widely adopted models for solving video quality prediction problems. This family of algorithms has a lot of potential because they are well understood, easy to deploy, and have lower computational requirements than deep learning techniques

PREDICTING THE UNKNOWN: MACHINE LEARNING TECHNIQUES FOR VIDEO FINGERPRINTING ATTACKS OVER TOR

In recent years, anonymization services such as Tor have become a popular resource for terrorist organizations and violent extremist groups. These adversaries use Tor to access the Dark Web to distribute video media as a way to recruit, train, and incite violence and acts of terrorism worldwide. This research strives to address this issue by examining and analyzing the use and development of video fingerprinting attacks using deep learning models. These high-performing deep learning models are called Deep Fingerprinting, which is used to predict video patterns with high accuracy in a closed-world setting. We pose ourselves as the adversary by passively observing raw network traffic as a user downloads a short video from YouTube. Based on traffic patterns, we can deduce what video the user was streaming with higher accuracy than previously obtained. In addition, our results include identifying the genre of the video. Our results suggest that an adversary may predict the video a user downloads over Tor with up to 83% accuracy, even when the user applies additional defenses to protect online privacy. By comparing different Deep Fingerprinting models with one another, we can better understand which models perform better from both the attacker and user’s perspective.Lieutenant, United States NavyApproved for public release. Distribution is unlimited