Uaborative fltering systems help address information overload by using the opinions of users in a community to make personrd recommendations for douents to each user. h[any collaborative fltering systems have few user opinions relative to the large number of documents avtiabIe. W sparsity problem can reduce the utity of the~tering system by reducing the number of doments for v'hich the system can make recommendations and adversely~ecting the @ty of recommendations. This paper defines and implements a model for integrating content-based rutings into a coUabomtive fltering systm e fikerbot model~ows cohborative fikering systems to address sparsity by tapping the strength of content tering techniques. IVe iden@ and evrduate metrim for assessing the effectiveness of fikerbots specficfly, and Hterirtg system enhancements in gened.FWy, we exTerimen@y vfidate the fikerbot approach by showing that even simple flterbots such as speU checking can increase the UWW for users of sparsely poptiated co~aborative~tering systems.

We present and evaluate a large-scale malware detection system integrating machine learning with expert reviewers, treating reviewers as a limited labeling resource. We demonstrate that even in small numbers, reviewers can vastly improve the system's ability to keep pace with evolving threats. We conduct our evaluation on a sample of VirusTotal submissions spanning 2.5 years and containing 1.1 million binaries with 778GB of raw feature data. Without reviewer assistance, we achieve 72% detection at a 0.5% false positive rate, performing comparable to the best vendors on VirusTotal. Given a budget of 80 accurate reviews daily, we improve detection to 89% and are able to detect 42% of malicious binaries undetected upon initial submission to VirusTotal. Additionally, we identify a previously unnoticed temporal inconsistency in the labeling of training datasets. We compare the impact of training labels obtained at the same time training data is first seen with training labels obtained months later. We find that using training labels obtained well after samples appear, and thus unavailable in practice for current training data, inflates measured detection by almost 20 percentage points. We release our cluster-based implementation, as well as a list of all hashes in our evaluation and 3% of our entire dataset.

Abstract. Revelations of large scale electronic surveillance and data mining by governments and corporations have fueled increased adoption of HTTPS. We present a traffic analysis attack against over 6000 webpages spanning the HTTPS deployments of 10 widely used, industryleading websites in areas such as healthcare, finance, legal services and streaming video. Our attack identifies individual pages in the same website with 89% accuracy, exposing personal details including medical conditions, financial and legal affairs and sexual orientation. We examine evaluation methodology and reveal accuracy variations as large as 18% caused by assumptions affecting caching and cookies. We present a novel defense reducing attack accuracy to 27% with a 9% traffic increase, and demonstrate significantly increased effectiveness of prior defenses in our evaluation context, inclusive of enabled caching, user-specific cookies and pages within the same website.

In this position paper, we argue that to be of practical interest, a machine-learning based security system must engage with the human operators beyond feature engineering and instance labeling to address the challenge of drift in adversarial environments. We propose that designers of such systems broaden the classification goal into an explanatory goal, which would deepen the interaction with system's operators.To provide guidance, we advocate for an approach based on maintaining one classifier for each class of unwanted activity to be filtered. We also emphasize the necessity for the system to be responsive to the operators constant curation of the training set. We show how this paradigm provides a property we call isolation and how it relates to classical causative attacks.In order to demonstrate the effects of drift on a binary classification task, we also report on two experiments using a previously unpublished malware data set where each instance is timestamped according to when it was seen.