Title

COIDS: A Clock Offset based Intrusion Detection System for Controller Area Networks

Abstract

Controller Area Network (CAN) is an in-vehicle communication protocol which provides an efficient and reliable communication link between Electronic Control Units (ECUs) in real-time. Recent studies have shown that attackers can take remote control of the targeted car by exploiting the vulnerabilities of the CAN protocol. Motivated by this fact, we propose Clock Offset-based Intrusion Detection System (COIDS) to monitor in-vehicle network and detect any intrusion. Precisely, we first measure and then exploit the clock offset of transmitter ECU's clock for fingerprinting ECU. We next leverage the derived fingerprints to construct a baseline of ECU's normal clock behaviour using an active learning technique. Based on the baseline of normal behaviour, we use Cumulative Sum method to detect any abnormal deviation in clock offset. Particularly, if the deviation in clock offset exceeds an unexpected positive or negative value, COIDS declares this change as an intrusion. Further, we use sequential change-point detection technique to determine the exact time of intrusion. We perform exhaustive experiments on real-world publicly available datasets primarily to assess the effectiveness of COIDS against three most potential attacks on CAN, i.e., DoS, impersonation and fuzzy attacks. The results show that COIDS is highly effective in defending all these three attacks. Further, the results show that COIDS considerably faster in detecting intrusion compared to a state-of-the-art solution.

Meeting Name

ACM International Conference

Department(s)

Computer Science

Research Center/Lab(s)

Center for High Performance Computing Research

Comments

National Science Foundation, Grant HIRPO2018040400359

Keywords and Phrases

Clock Offset; Clock Skew; Controller Area Network; Cumulative Sum method; Intrusion Detection Systems

International Standard Book Number (ISBN)

978-145037751-5

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2020, All rights reserved.

Publication Date

01 Jan 2020

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