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Security is an increasing concern for real-time systems (RTS). Over the last decade or so, researchers have demonstrated attacks and defenses aimed at such systems. In this paper, we identify, classify and measure the effectiveness of the security research in this domain. We provide a high-level summary [identification] and a taxonomy [classification] of this existing body of work. Furthermore, we carry out an in-depth analysis [measurement] of scheduler-based security techniques — the most common class of real-time security mechanisms. For this purpose, we developed a common metric, “attacker’s burden”, used to measure the effectiveness of (existing as well as future) scheduler-based real-time security measures. 

Model-serving systems have become increasingly popular, especially in real-time web applications. In such systems, users send queries to the server and specify the desired performance metrics (e.g., desired accuracy, latency). The server maintains a set of models (model zoo) in the back-end and serves the queries based on the specified metrics. This paper examines the security, specifically robustness against model extraction attacks, of such systems. Existing black-box attacks assume a single model can be repeatedly selected for serving inference requests. Modern inference serving systems break this assumption. Thus, they cannot be directly applied to extract a victim model, as models are hidden behind a layer of abstraction exposed by the serving system. An attacker can no longer identify which model she is interacting with. To this end, we first propose a query-efficient fingerprinting algorithm to enable the attacker to trigger any desired model consistently. We show that by using our fingerprinting algorithm, model extraction can have fidelity and accuracy scores within 1% of the scores obtained when attacking a single, explicitly specified model, as well as up to 14.6% gain in accuracy and up to 7.7% gain in fidelity compared to the naive attack. Second, we counter the proposed attack with a noise-based defense mechanism that thwarts fingerprinting by adding noise to the specified performance metrics. The proposed defense strategy reduces the attack's accuracy and fidelity by up to 9.8% and 4.8%, respectively (on medium-sized model extraction). Third, we show that the proposed defense induces a fundamental trade-off between the level of protection and system goodput, achieving configurable and significant victim model extraction protection while maintaining acceptable goodput (>80%). We implement the proposed defense in a real system with plans to open source. 

Timing correctness is crucial in a multi-criticality real-time system, such as an autonomous driving system. It has been recently shown that these systems can be vulnerable to timing inference attacks, mainly due to their predictable behavioral patterns. Existing solutions like schedule randomization cannot protect against such attacks, often limited by the system’s real-time nature. This article presents “ SchedGuard++ ”: a temporal protection framework for Linux-based real-time systems that protects against posterior schedule-based attacks by preventing untrusted tasks from executing during specific time intervals. SchedGuard++ supports multi-core platforms and is implemented using Linux containers and a customized Linux kernel real-time scheduler. We provide schedulability analysis assuming the Logical Execution Time (LET) paradigm, which enforces I/O predictability. The proposed response time analysis takes into account the interference from trusted and untrusted tasks and the impact of the protection mechanism. We demonstrate the effectiveness of our system using a realistic radio-controlled rover platform. Not only is “ SchedGuard++ ” able to protect against the posterior schedule-based attacks, but it also ensures that the real-time tasks/containers meet their temporal requirements. 

Unmanned Aerial Vehicles (UAVs) find increasing use in mission critical tasks both in civilian and military operations. Most UAVs rely on Inertial Measurement Units (IMUs) to calculate vehicle attitude and track vehicle position. Therefore, an incorrect IMU reading can cause a vehicle to destabilize, and possibly even crash. In this paper, we describe how a strategic adversary might be able to introduce spurious IMU values that can deviate a vehicle from its mission-specified path while at the same time evade customary anomaly detection mechanisms, thereby effectively perpetuating a “stealthy attack” on the system. We explore the feasibility of a Deep Neural Network (DNN) that uses a vehicle's state information to calculate the applicable IMU values to perpetrate such an attack. The eventual goal is to cause a vehicle to perturb enough from its mission parameters to compromise mission reliability, while, from the operator's perspective, the vehicle still appears to be operating normally. 

System auditing is a powerful tool that provides insight into the nature of suspicious events in computing systems, allowing machine operators to detect and subsequently investigate security incidents. While auditing has proven invaluable to the security of traditional computers, existing audit frameworks are rarely designed with consideration for Real-Time Systems (RTS). The transparency provided by system auditing would be of tremendous benefit in a variety of security-critical RTS domains, (e.g., autonomous vehicles); however, if audit mechanisms are not carefully integrated into RTS, auditing can be rendered ineffectual and violate the real-world temporal requirements of the RTS. In this paper, we demonstrate how to adapt commodity audit frameworks to RTS. Using Linux Audit as a case study, we first demonstrate that the volume of audit events generated by commodity frameworks is unsustainable within the temporal and resource constraints of real-time (RT) applications. To address this, we present Ellipsis, a set of kernel-based reduction techniques that leverage the periodic repetitive nature of RT applications to aggressively reduce the costs of system-level auditing. Ellipsis generates succinct descriptions of RT applications’ expected activity while retaining a detailed record of unexpected activities, enabling analysis of suspicious activity while meeting temporal constraints. Our evaluation of Ellipsis, using ArduPilot (an open-source autopilot application suite) demonstrates up to 93% reduction in audit log generation.