@inproceedings{0d7c34713712404a8771be67042120ab,
title = "SherLock vs moriarty: A smartphone dataset for cybersecurity research",
abstract = "In this paper we describe and share with the research community, a significant smartphone dataset obtained from an ongoing long-term data collection experiment. The dataset currently contains 10 billion data records from 30 users collected over a period of 1.6 years and an additional 20 users for 6 months (totaling 50 active users currently participating in the experiment). The experiment involves two smartphone agents: SherLock and Moriarty. SherLock collects a wide variety of software and sensor data at a high sample rate. Moriarty perpetrates various attacks on the user and logs its activities, thus providing labels for the SherLock dataset. The primary purpose of the dataset is to help security professionals and academic researchers in developing innovative methods of implicitly detecting malicious behavior in smartphones. Specifically, from data obtainable without superuser (root) privileges. To demonstrate possible uses of the dataset, we perform a basic malware analysis and evaluate a method of continuous user authentication.",
keywords = "Anomaly detection, Continuous authentication, Forensics, Machine learning, Malware, Smartphone dataset",
author = "Yisroel Mirsky and Asaf Shabtai and Lior Rokach and Bracha Shapira and Yuval Elovici",
year = "2016",
month = oct,
day = "28",
doi = "https://doi.org/10.1145/2996758.2996764",
language = "American English",
series = "AISec 2016 - Proceedings of the 2016 ACM Workshop on Artificial Intelligence and Security, co-located with CCS 2016",
pages = "1--12",
booktitle = "AISec 2016 - Proceedings of the 2016 ACM Workshop on Artificial Intelligence and Security, co-located with CCS 2016",
note = "9th ACM Workshop on Artificial Intelligence and Security, AISec 2016 ; Conference date: 28-10-2016",
}