@inproceedings{36319c78c3604f8db1565803e7ad2773,
title = "LiveDI: An Anti-theft Model Based on Driving Behavior",
abstract = "Anti-theft problem has been challenging since it mainly depends on the existence of external devices to defend from thefts. Recently, driver behavior analysis using supervised learning has been investigated with the goal to detect burglary by identifying drivers. In this paper, we propose a data-driven technique, LiveDI, which uses driving behavior removing the use of external devices in order to identify drivers. The built model utilizes Gated Recurrent Unit (GRU) and Fully Convolutional Networks (FCN) to learn long-short term patterns of the driving behaviors from drivers. Additionally, we improve the training time by utilizing the Segmented Feature Generation (SFG) algorithm to reduce the state space where the driving behaviors are split with a time window for analysis. Extensive experiments are conducted which show the impact of parameters on our technique and verify that our proposed approach outperforms the state-of-the-art baseline methods.",
keywords = "cybersecurity, driver classification, driver identification, driving behavior, machine learning, neural network, time series",
author = "Hashim Abu-Gellban and Long Nguyen and Mahdi Moghadasi and Zhenhe Pan and Fang Jin",
note = "Publisher Copyright: {\textcopyright} 2020 ACM.; null ; Conference date: 22-06-2020 Through 24-06-2020",
year = "2020",
month = jun,
day = "22",
doi = "10.1145/3369412.3395069",
language = "English",
series = "IH and MMSec 2020 - Proceedings of the 2020 ACM Workshop on Information Hiding and Multimedia Security",
publisher = "Association for Computing Machinery, Inc",
pages = "67--72",
booktitle = "IH and MMSec 2020 - Proceedings of the 2020 ACM Workshop on Information Hiding and Multimedia Security",
}