Sybil Attack Identification for Crowdsourced Navigation: A Self-Supervised Deep Learning Approach

Yu，James J.Q.

2020

10.1109/TITS.2020.3036085

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS   影响因子和分区

1524-9050

1558-0016

Crowdsourced navigation is becoming the prevalent automobile navigation solution with the widespread adoption of smartphones over the past decade, which supports a plethora of intelligent transportation system services. However, it is subjected to Sybil attacks that inject carefully designed adversarial GPS trajectories to compromise the data aggregation system and cause false traffic jams. Successful Sybil attacks have been launched against real crowdsourced navigation systems, yet defending such critical threats has seldom been studied. In this work, a novel deep generative model based on Bayesian deep learning is devised for Sybil attack identification. The proposed model exploits time-series features to embed trajectories in a latent distribution space, which serves as a basis for identifying ones generated by Sybil attacks. Case studies on three real-world vehicular trajectory datasets reveal that the proposed model improves the performance of state-of-the-art baselines by at least 76.6%. Additionally, a hyper-parameter test develops guidelines for parameter selection, and a fast training scheme is proposed and assessed to boost the model training efficiency.

Bayes methods Bayesian deep learning crowdsourced navigation cybersecurity. deep generative model Deep learning Global Positioning System intelligent transportation systems Navigation Sybil attack detection Training Trajectory Vehicle dynamics

EI

英语

第一

20204909595361

Cybersecurity ; Computer crime ; Learning systems ; Intelligent systems ; Traffic congestion ; Crime ; Deep learning ; Navigation

Ergonomics and Human Factors Engineering:461.4 ; Data Processing and Image Processing:723.2 ; Artificial Intelligence:723.4 ; Social Sciences:971

ENGINEERING

2-s2.0-85097151789

Scopus

Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation, Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China (e-mail: yujq3@sustech.edu.cn)

Yu，James J.Q.. Sybil Attack Identification for Crowdsourced Navigation: A Self-Supervised Deep Learning Approach[J]. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,2020,22(7):4622-4634.

Yu，James J.Q..(2020).Sybil Attack Identification for Crowdsourced Navigation: A Self-Supervised Deep Learning Approach.IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,22(7),4622-4634.

Yu，James J.Q.."Sybil Attack Identification for Crowdsourced Navigation: A Self-Supervised Deep Learning Approach".IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 22.7(2020):4622-4634.