Various defense schemes --- which determine the presence of an attack on the in-vehicle network --- have recently been proposed. However, they fail to identify which Electronic Control Unit (ECU) actually mounted the attack. Clearly, pinpointing the attacker ECU is essential for fast efficient forensic, isolation, security patch, etc. To meet this need, we propose a novel scheme, called Viden (Voltage-based attacker identification), which can identify the attacker ECU by measuring and utilizing voltages on the in-vehicle network. The first phase of Viden, called ACK learning, determines whether or not the measured voltage signals really originate from the genuine message transmitter. Viden then exploits the voltage measurements to construct and update the transmitter ECUs' voltage profiles as their fingerprints. It finally uses the voltage profiles to identify the attacker ECU. Since Viden adapts its profiles to changes inside outside of the vehicle, it can pinpoint the attacker ECU under various conditions. Moreover, its efficiency and design-compliance with modern in-vehicle network implementations make Viden practical and easily deployable. Our extensive experimental evaluations on both a CAN bus prototype and two real vehicles have shown that Viden can accurately fingerprint ECUs based solely on voltage measurements and thus identify the attacker ECU with a low false identification rate of 0.2 .
To overcome these difficulties, researchers proposed to extract other time and frequency domain features of voltage measurements (e.g., RMS amplitude) and use them as inputs for classification; more specifically, supervised (batch) learning algorithms (e.g., SVM) @cite_16 . This way, they were able to fingerprint ECUs with enhanced accuracy and was successful on high-speed CAN buses. However, this solution was neither practical nor attractive for attacker identification for the following reasons. First, it required not only a high sampling rate (2.5 GSamples sec), but also the use of the extended CAN frame format with 29-bit IDs, which is seldom used (due to its bandwidth waste) in contemporary vehicles; most vehicles use the standard format with 11-bit IDs. Moreover, since the modeling was done via batch learning, unpredictable changes in the CAN bus (e.g., temperature, battery level) and adversary's behaviors can lead to false identifications. These will be detailed later when we discuss the details of .
@cite_16
2464037380 2756382106 2113322447 2465629572
As more software modules and external interfaces are getting added on vehicles, new attacks and vulnerabilities are emerging. Researchers have demonstrated how to compromise in-vehicle Electronic Control Units (ECUs) and control the vehicle maneuver. To counter these vulnerabilities, various types of defense mechanisms have been proposed, but they have not been able to meet the need of strong protection for safety-critical ECUs against in-vehicle network attacks. To mitigate this deficiency, we propose an anomaly-based intrusion detection system (IDS), called Clock-based IDS (CIDS). It measures and then exploits the intervals of periodic in-vehicle messages for fingerprinting ECUs. The thus-derived fingerprints are then used for constructing a baseline of ECUs' clock behaviors with the Recursive Least Squares (RLS) algorithm. Based on this baseline, CIDS uses Cumulative Sum (CUSUM) to detect any abnormal shifts in the identification errors - a clear sign of intrusion. This allows quick identification of in-vehicle network intrusions with a low false-positive rate of 0.055 . Unlike state-of-the-art IDSs, if an attack is detected, CIDS's fingerprinting of ECUs also facilitates a rootcause analysis; identifying which ECU mounted the attack. Our experiments on a CAN bus prototype and on real vehicles have shown CIDS to be able to detect a wide range of in-vehicle network attacks. The Controller Area Network (CAN) was specified with no regards to security mechanisms at all. This fact in combination with the widespread adoption of the CAN standard for connecting more than a hundred Electrical Control Units (ECUs), which control almost every aspect of modern cars, makes the CAN bus a valuable target for adversaries. As vehicles are safety-critical systems and the physical integrity of the driver has the highest priority, it is necessary to invent suitable countermeasures to limit CAN’s security risks. As a matter of fact, the close resemblances of in-vehicle networks to traditional computer networks, enables the use of conventional countermeasures, e.g. Intrusion Detection Systems (IDS). We propose a software-based light-weight IDS relying on properties extracted from the signal database of a CAN domain. Further, we suggest two anomaly-based algorithms based on message cycle time analysis and plausibility analysis of messages (e.g. speed messages). We evaluate our IDS on a simulated setup, as well as a real in-vehicle network, by performing attacks on different parts of the network. Our evaluation shows that the proposed IDS successfully detects malicious events such as injection of malformed CAN frames, unauthorized CAN frames, speedometer plausibility detection and Denial of Service (DoS) attacks. Based on our experience of implementing an in-vehicle IDS, we discuss potential challenges and constraints that engineers might face during the process of implementing an IDS system for in-vehicle networks. We believe that the results of this work can contribute to more advanced research in the field of intrusion detection systems for in-vehicle networks and thereby add to a safer driving experience. Intermittently powered applications create a need for low-cost security and privacy in potentially hostile environments, supported by primitives including identification and random number generation. Our measurements show that power-up of SRAM produces a physical fingerprint. We propose a system of fingerprint extraction and random numbers in SRAM (FERNS) that harvests static identity and randomness from existing volatile CMOS memory without requiring any dedicated circuitry. The identity results from manufacture-time physically random device threshold voltage mismatch, and the random numbers result from runtime physically random noise. We use experimental data from high-performance SRAM chips and the embedded SRAM of the WISP UHF RFID tag to validate the principles behind FERNS. For the SRAM chip, we demonstrate that 8-byte fingerprints can uniquely identify circuits among a population of 5,120 instances and extrapolate that 24-byte fingerprints would uniquely identify all instances ever produced. Using a smaller population, we demonstrate similar identifying ability from the embedded SRAM. In addition to identification, we show that SRAM fingerprints capture noise, enabling true random number generation. We demonstrate that a 512-byte SRAM fingerprint contains sufficient entropy to generate 128-bit true random numbers and that the generated numbers pass the NIST tests for runs, approximate entropy, and block frequency. 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. This paper is also the first to identify potential attack models that systems should be able to detect.