Hold the Door! Fingerprinting Your Car Key to Prevent Keyless Entry Car Theft
March 30, 2020 ยท Declared Dead ยท ๐ Network and Distributed System Security Symposium
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Kyungho Joo, Wonsuk Choi, Dong Hoon Lee
arXiv ID
2003.13251
Category
cs.CR: Cryptography & Security
Cross-listed
eess.SP
Citations
45
Venue
Network and Distributed System Security Symposium
Last Checked
3 months ago
Abstract
Recently, the traditional way to unlock car doors has been replaced with a keyless entry system which proves more convenient for automobile owners. When a driver with a key fob is in the vicinity of the vehicle, doors automatically unlock on user command. However, unfortunately, it has been shown that these keyless entry systems are vulnerable to signal relaying attacks. While it is evident that automobile manufacturers incorporate preventative methods to secure these keyless entry systems, they continue to be vulnerable to a range of attacks. Relayed signals result in valid packets that are verified as legitimate, and this makes it is difficult to distinguish a legitimate door unlock request from a malicious signal. In response to this vulnerability, this paper presents an RF fingerprinting method (coined HOld the DOoR, HODOR) to detect attacks on keyless entry systems the first attempt to exploit the RF fingerprint technique in the automotive domain. HODOR is designed as a sub authentication method that supports existing authentication systems for keyless entry systems and does not require any modification of the main system to perform. Through a series of experiments, the results demonstrate that HODOR competently and reliably detects attacks on keyless entry systems. HODOR achieves both an average false positive rate (FPR) of 0.27 percent with a false negative rate (FNR) of 0 percent for the detection of simulated attacks, corresponding to current research on keyless entry car theft.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
๐ Similar Papers
In the same crypt โ Cryptography & Security
R.I.P.
๐ป
Ghosted
R.I.P.
๐ป
Ghosted
Membership Inference Attacks against Machine Learning Models
R.I.P.
๐ป
Ghosted
The Limitations of Deep Learning in Adversarial Settings
R.I.P.
๐ป
Ghosted
Practical Black-Box Attacks against Machine Learning
R.I.P.
๐ป
Ghosted
Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks
R.I.P.
๐ป
Ghosted
Extracting Training Data from Large Language Models
Died the same way โ ๐ป Ghosted
R.I.P.
๐ป
Ghosted
Language Models are Few-Shot Learners
R.I.P.
๐ป
Ghosted
PyTorch: An Imperative Style, High-Performance Deep Learning Library
R.I.P.
๐ป
Ghosted
XGBoost: A Scalable Tree Boosting System
R.I.P.
๐ป
Ghosted