Reviewer Integration and Performance Measurement for Malware Detection

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.Comment: 20 papers, 11 figures, accepted at the 13th Conference on Detection of Intrusions and Malware & Vulnerability Assessment (DIMVA 2016

LNCS

We present a novel approach for blockchain asset owners to reclaim their funds in case of accidental private-key loss or transfer to a mistyped address. Our solution can be deployed upon failure or absence of proactively implemented backup mechanisms, such as secret sharing and cold storage. The main advantages against previous proposals is it does not require any prior action from users and works with both single-key and multi-sig accounts. We achieve this by a 3-phase Commit()→Reveal()→Claim()−or−Challenge() smart contract that enables accessing funds of addresses for which the spending key is not available. We provide an analysis of the threat and incentive models and formalize the concept of reactive KEy-Loss Protection (KELP)