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1. Time-Print: Authenticating USB Flash Drives with Novel Timing Fingerprints

   https://doi.org/10.1109/SP46214.2022.9833595  

   Cronin, Patrick ; Gao, Xing ; Wang, Haining ; Cotton, Chase ( May 2022 , 2022 IEEE Symposium on Security and Privacy (SP))

   Universal Serial Bus (USB) ports are a ubiquitous feature in computer systems and offer a cheap and efficient way to provide power and data connectivity between a host and peripheral devices. Even with the rise of cloud and off-site computing, USB has played a major role in enabling data transfer between devices. Its usage is especially prevalent in high-security environments where systems are ‘air-gapped’ and not connected to the Internet. However, recent research has demonstrated that USB is not nearly as secure as once thought, with different attacks showing that modified firmware on USB mass storage devices can compromise a host system. While many defenses have been proposed, they require user interaction, advanced hardware support (incompatible with legacy devices), or utilize device identifiers that can be subverted by an attacker. In this paper, we present Time-Print, a novel timing-based fingerprinting method, for identifying USB mass storage devices. We create a fingerprint by timing a series of read operations from different locations on a drive, as the timing variations are unique enough to identify individual USB devices. Time-Print is low overhead, completely software-based, and does not require any extra or specialized hardware. To validate the efficacy of Time-Print, we examine more than 40 USB flash drives and conduct experiments in multiple authentication scenarios. The experimental results show that Time-Print can (1) identify known/unknown brand/model USB devices with greater than 99.5% accuracy, (2) identify seen/unseen devices of the same brand/model with 95% accuracy, and (3) classify USB devices from the same brand/model with an average accuracy of 98.7%. 

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2. SoK: Hardware Defenses Against Speculative Execution Attacks

   https://doi.org/10.1109/SEED51797.2021.00023  

   Hu, Guangyuan ; He, Zecheng ; Lee, Ruby B. ( September 2021 , 2021 International Symposium on Secure and Private Execution Environment Design (SEED))

   Speculative execution attacks leverage the speculative and out-of-order execution features in modern computer processors to access secret data or execute code that should not be executed. Secret information can then be leaked through a covert channel. While software patches can be installed for mitigation on existing hardware, these solutions can incur big performance overhead. Hardware mitigation is being studied extensively by the computer architecture community. It has the benefit of preserving software compatibility and the potential for much smaller performance overhead than software solutions. This paper presents a systematization of the hardware defenses against speculative execution attacks that have been proposed. We show that speculative execution attacks consist of 6 critical attack steps. We propose defense strategies, each of which prevents a critical attack step from happening, thus preventing the attack from succeeding. We then summarize 20 hardware defenses and overhead-reducing features that have been proposed. We show that each defense proposed can be classified under one of our defense strategies, which also explains why it can thwart the attack from succeeding. We discuss the scope of the defenses, their performance overhead, and the security-performance trade-offs that can be made. 

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3. DRIVETRUTH: Automated Autonomous Driving Dataset Generation for Security Applications

   Muller, Raymond ( January 2022 , Workshop on Automotive and Autonomous Vehicle Security (AutoSec))

   With emerging vision-based autonomous driving (AD) systems, it becomes increasingly important to have datasets to evaluate their correct operation and identify potential security flaws. However, when collecting a large amount of data, either human experts manually label potentially hundreds of thousands of image frames or systems use machine learning algorithms to label the data, with the hope that the accuracy is good enough for the application. This can become especially problematic when tracking the context information, such as the location and velocity of surrounding objects, useful to evaluate the correctness and improve stability and robustness of the AD systems. In this paper, we introduce DRIVETRUTH, a data collection framework built on CARLA, an open-source simulator for AD research, which constructs datasets with automatically generated accurate object labels, bounding boxes of objects and their contextual information through accessing simulation state and using semantic LiDAR raycasts. By leveraging the actual state of the simulation and the agents within it, we guarantee complete accuracy in all labels and gathered contextual information. Further, the use of the simulator provides easily collecting data in diverse environmental conditions and agent behaviors, with lighting, weather, and traffic behavior being configurable within the simulation. Through this effort, we provide users a means to extracting actionable simulated data from CARLA to test and explore attacks and defenses for AD systems. 

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4. Employing attack graphs for intrusion detection

   https://doi.org/10.1145/3368860.3368862  

   Capobianco, Frank ; George, Rahul ; Huang, Kaiming ; Jaeger, Trent ; Krishnamurthy, Srikanth ; Qian, Zhiyun ; Payer, Mathias ; Yu, Paul ( September 2019 , New Security Paradigms Workshop)

   Intrusion detection systems are a commonly deployed defense that examines network traffic, host operations, or both to detect attacks. However, more attacks bypass IDS defenses each year, and with the sophistication of attacks increasing as well, we must examine new perspectives for intrusion detection. Current intrusion detection systems focus on known attacks and/or vulnerabilities, limiting their ability to identify new attacks, and lack the visibility into all system components necessary to confirm attacks accurately, particularly programs. To change the landscape of intrusion detection, we propose that future IDSs track how attacks evolve across system layers by adapting the concept of attack graphs. Attack graphs were proposed to study how multi-stage attacks could be launched by exploiting known vulnerabilities. Instead of constructing attacks reactively, we propose to apply attack graphs proactively to detect sequences of events that fulfill the requirements for vulnerability exploitation. Using this insight, we examine how to generate modular attack graphs automatically that relate adversary accessibility for each component, called its attack surface, to flaws that provide adversaries with permissions that create threats, called attack states, and exploit operations from those threats, called attack actions. We evaluate the proposed approach by applying it to two case studies: (1) attacks on file retrieval, such as TOCTTOU attacks, and (2) attacks propagated among processes, such as attacks on Shellshock vulnerabilities. In these case studies, we demonstrate how to leverage existing tools to compute attack graphs automatically and assess the effectiveness of these tools for building complete attack graphs. While we identify some research areas, we also find several reasons why attack graphs can provide a valuable foundation for improving future intrusion detection systems. 

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5. A Billion Open Interfaces for Eve and Mallory: MitM, DoS, and Tracking Attacks on iOS and macOS Through Apple Wireless Direct Link

   Mariotto, Alex ; Heinrich, Alexander ; Kreitschmann, David ; Noubir, Guevara ; Hollick, Matthias ( January 2019 , Proceedings of the 28th USENIX Security Symposium)

   Apple Wireless Direct Link (AWDL) is a key protocol in Apple’s ecosystem used by over one billion iOS and macOS devices for device-to-device communications. AWDL is a proprietary extension of the IEEE 802.11 (Wi-Fi) standard and integrates with Bluetooth Low Energy (BLE) for providing services such as Apple AirDrop. We conduct the first security and privacy analysis of AWDL and its integration with BLE. We uncover several security and privacy vulnerabilities ranging from design flaws to implementation bugs leading to a man-in-the-middle (MitM) attack enabling stealthy modification of files transmitted via AirDrop, denial-of-service (DoS) attacks preventing communication, privacy leaks that enable user identification and long-term tracking undermining MAC address randomization, and DoS attacks enabling targeted or simultaneous crashing of all neighboring devices. The flaws span across AirDrop’s BLE discovery mechanism, AWDL synchronization, UI design, and Wi-Fi driver implementation. Our analysis is based on a combination of reverse engineering of protocols and code supported by analyzing patents. We provide proof-of-concept implementations and demonstrate that the attacks can be mounted using a low-cost ($20) micro:bit device and an off-the-shelf Wi-Fi card. We propose practical and effective countermeasures. While Apple was able to issue a fix for a DoS attack vulnerability after our responsible disclosure, the other security and privacy vulnerabilities require the redesign of some of their services. 

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