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Recent developments in online tracking make it harder for individuals to detect and block trackers. Some sites have deployed indirect tracking methods, which attempt to uniquely identify a device by asking the browser to perform a seemingly-unrelated task. One type of indirect tracking, Canvas fingerprinting, causes the browser to render a graphic recording rendering statistics as a unique identifier. In this work, we observe how indirect device fingerprinting methods are disclosed in privacy policies, and consider whether the disclosures are sufficient to enable website visitors to block the tracking methods. We compare these disclosures to the disclosure of direct fingerprinting methods on the same websites. Our case study analyzes one indirect fingerprinting technique, Canvas fingerprinting. We use an existing automated detector of this fingerprinting technique to conservatively detect its use on Alexa Top 500 websites that cater to United States consumers, and we examine the privacy policies of the resulting 28 websites. Disclosures of indirect fingerprinting vary in specificity. None described the specific methods with enough granularity to know the website used Canvas fingerprinting. Conversely, many sites did provide enough detail about usage of direct fingerprinting methods to allow a website visitor to reliably detect and block those techniques. We conclude that indirect fingerprinting methods are often difficult to detect and are not identified with specificity in privacy policies. This makes indirect fingerprinting more difficult to block, and therefore risks disturbing the tentative armistice between individuals and websites currently in place for direct fingerprinting. This paper illustrates differences in fingerprinting approaches, and explains why technologists, technology lawyers, and policymakers need to appreciate the challenges of indirect fingerprinting. 

While the growth of cloud-based technologies has benefited the society tremendously, it has also increased the surface area for cyber attacks. Given that cloud services are prevalent today, it is critical to devise systems that detect intrusions. One form of security breach in the cloud is when cybercriminals compromise Virtual Machines (VMs) of unwitting users and, then, utilize user resources to run time-consuming, malicious, or illegal applications for their own benefit. This work proposes a method to detect unusual resource usage trends and alert the user and the administrator in real time. We experiment with three categories of methods: simple statistical techniques, unsupervised classification, and regression. So far, our approach successfully detects anomalous resource usage when experimenting with typical trends synthesized from published real-world web server logs and cluster traces. We observe the best results with unsupervised classification, which gives an average F1-score of 0.83 for web server logs and 0.95 for the cluster traces.