LiveDI: An Anti-theft Model Based on Driving Behavior

Hashim Abu-Gellban, Long Nguyen, Mahdi Moghadasi, Zhenhe Pan, Fang Jin

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

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.

Original languageEnglish
Title of host publicationIH and MMSec 2020 - Proceedings of the 2020 ACM Workshop on Information Hiding and Multimedia Security
PublisherAssociation for Computing Machinery, Inc
Pages67-72
Number of pages6
ISBN (Electronic)9781450370509
DOIs
StatePublished - Jun 22 2020
Event8th ACM Workshop on Information Hiding and Multimedia Security, IH and MMSec 2020 - Denver, United States
Duration: Jun 22 2020Jun 24 2020

Publication series

NameIH and MMSec 2020 - Proceedings of the 2020 ACM Workshop on Information Hiding and Multimedia Security

Conference

Conference8th ACM Workshop on Information Hiding and Multimedia Security, IH and MMSec 2020
Country/TerritoryUnited States
CityDenver
Period06/22/2006/24/20

Keywords

  • cybersecurity
  • driver classification
  • driver identification
  • driving behavior
  • machine learning
  • neural network
  • time series

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