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3. Identifying ECUs Using Inimitable Characteristics of Signals in Controller Area Networks

Author

Choi, Wonsuk, Jo, Hyo Jin, Woo, Samuel, Chun, Ji Young, Park, Jooyoung, and Lee, Dong Hoon

Subjects
Computer Science - Cryptography and Security
Abstract

In the last several decades, the automotive industry has come to incorporate the latest Information and Communications (ICT) technology, increasingly replacing mechanical components of vehicles with electronic components. These electronic control units (ECUs) communicate with each other in an in-vehicle network that makes the vehicle both safer and easier to drive. Controller Area Networks (CANs) are the current standard for such high quality in-vehicle communication. Unfortunately, however, CANs do not currently offer protection against security attacks. In particular, they do not allow for message authentication and hence are open to attacks that replay ECU messages for malicious purposes. Applying the classic cryptographic method of message authentication code (MAC) is not feasible since the CAN data frame is not long enough to include a sufficiently long MAC to provide effective authentication. In this paper, we propose a novel identification method, which works in the physical layer of an in-vehicle CAN network. Our method identifies ECUs using inimitable characteristics of signals enabling detection of a compromised or alien ECU being used in a replay attack. Unlike previous attempts to address security issues in the in-vehicle CAN network, our method works by simply adding a monitoring unit to the existing network, making it deployable in current systems and compliant with required CAN standards. Our experimental results show that the bit string and classification algorithm that we utilized yielded more accurate identification of compromised ECUs than any other method proposed to date. The false positive rate is more than 2 times lower than the method proposed by P.-S. Murvay et al. This paper is also the first to identify potential attack models that systems should be able to detect., Comment: 12 pages, 6 figures

Published
2016

4. In-Vehicle Network Intrusion Detection System Using CAN Frame-Aware Features

Author

Jeong, Yeonseon, Kim, Hyunghoon, Lee, Seyoung, Choi, Wonsuk, Lee, Dong Hoon, and Jo, Hyo Jin

Abstract

With the advancement of connected and automated vehicles (CAVs), drivers now have access to convenient features such as lane-keeping, cruise control, and more. The electronic control units (ECUs) equipped within vehicles communicate with each other through the controller area network (CAN). However, since the CAN does not possess any security mechanisms, it becomes a target for adversaries to attack. In light of this, a significant amount of research regarding intrusion detection systems (IDSs) has focused on detecting such maliciously injected CAN packets. Nevertheless, most existing machine learning-based IDSs neither calculate the exact time intervals of the CAN packets nor utilize the counter information. Precise timing intervals are a crucial feature for detecting spoofing, fuzzing, and replay attacks, and counter information is also a significant feature that can detect fuzzing and replay attacks. Therefore, in this paper, we propose a methodology for extracting two detection features that are aware of CAN frame characteristics: the interframe space (IFS) between two consecutive CAN packets, and the counter information of a CAN data payload (i.e., data field). Using these features, we introduce decision tree-based IDS. We evaluate the proposed features with popular decision tree-based models such as random forest and extreme gradient boosting (XGBoost). The results show that our proposed IDS can detect maliciously injected CAN packets with an F1 score of 99.54% in binary classification and 97.99% in multi-class classification, which are higher scores than what existing machine/deep learning-based IDSs achieve. Additionally, we measure the detection time of our proposed IDS in both online and offline testing environments.

Published
2024
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5. ErrIDS: An Enhanced Cumulative Timing Error-Based Automotive Intrusion Detection System

Author

Lee, Seyoung, Choi, Wonsuk, Jo, Hyo Jin, and Lee, Dong Hoon

Abstract

Contemporary vehicles have undergone numerous transformations to become fully computerized machines. This computerizing process is intended to provide safety and convenience for drivers; however, there have been many studies demonstrating how to remotely maneuver a vehicle by compromising its in-vehicle electronic control units (ECU). As a countermeasure, automotive intrusion detection systems (IDSs) have also been extensively explored as potential remedies. The clock-based IDS was one of the most promising methods for an automotive IDS, but researchers have recently determined it to be insufficient, as adversaries can emulate the clock skew. In this paper, we propose a novel automotive IDS that leverages the residuals—which have traditionally been considered an error that should be removed from analysis—of average and actual timestamp intervals of two consecutive controller area network (CAN) messages. Thus, we present a rationale as to why large residuals occur in a real in-vehicle CAN network. Our method analyzes transmission periodicity so closely that any minuscule change can be detected in the event of an intrusion. We show that our method detects a vehicle intrusion with a low false-alarm rate, and that it can detect a new sophisticated attack which emulates the clock skew of an original transmission. To the best of our knowledge, this is the first approach analyzing transmission time to detect the frequency masquerading attack with clock skew emulation. Finally, our method enables the sharing of parameters determined in a vehicle with other like models, which is meaningful for manufacturers in terms of scalability.

Published
2023
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29. BadVoice: Soundless voice-control replay attack on modern smartphones

Author

Jo Hyo Jin, Dong Hoon Lee, Samuel Woo, and Park Joon Young

Subjects
Authentication, business.product_category, Computer science, 020206 networking & telecommunications, 02 engineering and technology, Adversary, Speaker recognition, computer.software_genre, Computer security, Attack model, 0202 electrical engineering, electronic engineering, information engineering, Malware, 020201 artificial intelligence & image processing, business, Replay attack, computer, Mobile device, Headphones
Abstract

Speech recognition is a technology that enables the recognition and translation of spoken language into data that can be interpreted by computers. With the development of speech recognition technologies, voice assistants on mobile devices, such as Siri or Google Voice have been introduced. Particularly, mobile device users can easily perform several commands on their mobile devices using these systems. However, this convenience causes a vulnerability in that an adversary can access the mobile device data and functions easily. This vulnerability could be exploited by the adversary because there are no authentication procedures. Recently, a remote attack on a voice assistant was introduced, but the attack can be considered to be unrealistic because of many assumptions. In this paper, we analyze the vulnerabilities of mobile device speech recognition systems. We also introduce the Toilet-time attack as a new realistic attack model. Furthermore, we prove the practicality of our attack model and evaluate attack scenarios using a new attack tool called BadVoice.

Published
2016

32. Cognitive impairment differs between neuromyelitis optica spectrum disorder and multiple sclerosis

Author

Kim, Su-Hyun, primary, Kwak, Kichang, additional, Jeong, In Hye, additional, Hyun, Jae-Won, additional, Jo, Hyo-Jin, additional, Joung, AeRan, additional, Yu, Eun-Seung, additional, Kim, Ji-Hee, additional, Lee, Sang Hyun, additional, Yun, Sooin, additional, Joo, Jungnam, additional, Lee, Dong-kyun, additional, Lee, Jong-Min, additional, and Kim, Ho Jin, additional

Published
2016
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39. A Practical Wireless Attack on the Connected Car and Security Protocol for In-Vehicle CAN.

Author

Woo, Samuel, Jo, Hyo Jin, and Lee, Dong Hoon

Abstract

Vehicle-IT convergence technology is a rapidly rising paradigm of modern vehicles, in which an electronic control unit (ECU) is used to control the vehicle electrical systems, and the controller area network (CAN), an in-vehicle network, is commonly used to construct an efficient network of ECUs. Unfortunately, security issues have not been treated properly in CAN, although CAN control messages could be life-critical. With the appearance of the connected car environment, in-vehicle networks (e.g., CAN) are now connected to external networks (e.g., 3G/4G mobile networks), enabling an adversary to perform a long-range wireless attack using CAN vulnerabilities. In this paper we show that a long-range wireless attack is physically possible using a real vehicle and malicious smartphone application in a connected car environment. We also propose a security protocol for CAN as a countermeasure designed in accordance with current CAN specifications. We evaluate the feasibility of the proposed security protocol using CANoe software and a DSP-F28335 microcontroller. Our results show that the proposed security protocol is more efficient than existing security protocols with respect to authentication delay and communication load. [ABSTRACT FROM PUBLISHER]

Published
2015
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40. Cognitive impairment differs between neuromyelitis optica spectrum disorder and multiple sclerosis.

Author

Kim SH, Kwak K, Jeong IH, Hyun JW, Jo HJ, Joung A, Yu ES, Kim JH, Lee SH, Yun S, Joo J, Lee DK, Lee JM, and Kim HJ

Subjects
Adult, Cognitive Dysfunction diagnostic imaging, Cognitive Dysfunction epidemiology, Cognitive Dysfunction etiology, Female, Humans, Magnetic Resonance Imaging, Male, Middle Aged, Multiple Sclerosis complications, Multiple Sclerosis diagnostic imaging, Multiple Sclerosis epidemiology, Neuromyelitis Optica complications, Neuromyelitis Optica diagnostic imaging, Neuromyelitis Optica epidemiology, Cognitive Dysfunction physiopathology, Multiple Sclerosis physiopathology, Neuromyelitis Optica physiopathology
Abstract

Objective: To compare the frequency and pattern of cognitive impairment (CI) between patients with neuromyelitis optica spectrum disorder (NMOSD) and multiple sclerosis (MS)., Methods: A total of 82 NMOSD patients, 58 MS patients, and 45 healthy controls (HCs) underwent a neuropsychological assessment., Results: CI was observed in 29% of NMOSD and 50% of MS patients (p < 0.001); CI was considered present if a patient scored lower than the fifth percentile compared with HCs in at least three domains. A lower frequency of CI was consistently found when CI was indicated by at least two failed tests (p < 0.001). MS patients performed worse than did NMOSD patients on verbal learning and verbal and visual memory tests. Levels of education and depression and the interval from disease onset to treatment were associated with a negative influence on cognition in patients with NMOSD., Conclusion: CI in patients with NMOSD may be not as common as in patients with MS. MS patients exhibited severe impairment, particularly on learning and memory tests, compared with NMOSD patients. Differential prevalence and patterns of CI between NMOSD and MS patients suggest that the two diseases have different mechanisms of brain injury., (© The Author(s), 2016.)

Published
2016
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