Hardware supply-chain attacks are raising significant security threats to the boot process of multiprocessor systems. This paper identifies a new, prevalent hardware supply-chain attack surface that can bypass multiprocessor secure boot due to the absence of processor-authentication mechanisms. To defend against such attacks, we present PA-Boot, the first formally verified processor-authentication protocol for secure boot in multiprocessor systems. PA-Boot is proved functionally correct and is guaranteed to detect multiple adversarial behaviors, e.g., processor replacements, man-in-the-middle attacks, and tampering with certificates. The fine-grained formalization of PA-Boot and its fully mechanized security proofs are carried out in the Isabelle/HOL theorem prover with 306 lemmas/theorems and ~7,100 LoC. Experiments on a proof-of-concept implementation indicate that PA-Boot can effectively identify boot-process attacks with a considerably minor overhead and thereby improve the security of multiprocessor systems.
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We propose a covariance stationarity test for an otherwise dependent and possibly globally non-stationary time series. We work in the new setting of Jin, Wang and Wang (2015) who exploit Walsh (1923) functions (global square waves) in order to compare sub-sample covariances with the full sample counterpart. They impose strict stationarity under the null, only consider linear processes under either hypothesis, and exploit linearity in order to achieve a parametric estimator for an inverted high dimensional asymptotic covariance matrix. Conversely, we allow for linear or linear processes with possibly non-iid innovations. This is important in macroeconomics and finance where nonlinear feedback and random volatility occur in many settings. We completely sidestep asymptotic covariance matrix estimation and inversion by bootstrapping a max-correlation difference statistic, where the maximum is taken over the correlation lag h and Walsh function generated sub-sample counter k (the number of systematic samples). We achieve a higher feasible rate of increase for the maximum lag and counter H and K, and in the supplemental material we present a data driven method for selecting H and K. Of particular note, our test is capable of detecting breaks in variance, and distant, or very mild, deviations from stationarity.
Recent advances in camera designs and imaging pipelines allow us to capture high-quality images using smartphones. However, due to the small size and lens limitations of the smartphone cameras, we commonly find artifacts or degradation in the processed images. The most common unpleasant effects are noise artifacts, diffraction artifacts, blur, and HDR overexposure. Deep learning methods for image restoration can successfully remove these artifacts. However, most approaches are not suitable for real-time applications on mobile devices due to their heavy computation and memory requirements. In this paper, we propose LPIENet, a lightweight network for perceptual image enhancement, with the focus on deploying it on smartphones. Our experiments show that, with much fewer parameters and operations, our model can deal with the mentioned artifacts and achieve competitive performance compared with state-of-the-art methods on standard benchmarks. Moreover, to prove the efficiency and reliability of our approach, we deployed the model directly on commercial smartphones and evaluated its performance. Our model can process 2K resolution images under 1 second in mid-level commercial smartphones.
Privacy preservation in Ride-Hailing Services (RHS) is intended to protect privacy of drivers and riders. pRide, published in IEEE Trans. Vehicular Technology 2021, is a prediction based privacy-preserving RHS protocol to match riders with an optimum driver. In the protocol, the Service Provider (SP) homomorphically computes Euclidean distances between encrypted locations of drivers and rider. Rider selects an optimum driver using decrypted distances augmented by a new-ride-emergence prediction. To improve the effectiveness of driver selection, the paper proposes an enhanced version where each driver gives encrypted distances to each corner of her grid. To thwart a rider from using these distances to launch an inference attack, the SP blinds these distances before sharing them with the rider. In this work, we propose a passive attack where an honest-but-curious adversary rider who makes a single ride request and receives the blinded distances from SP can recover the constants used to blind the distances. Using the unblinded distances, rider to driver distance and Google Nearest Road API, the adversary can obtain the precise locations of responding drivers. We conduct experiments with random on-road driver locations for four different cities. Our experiments show that we can determine the precise locations of at least 80% of the drivers participating in the enhanced pRide protocol.
In today's computing environment, where Artificial Intelligence (AI) and data processing are moving toward the Internet of Things (IoT) and Edge computing paradigms, benchmarking resource-constrained devices is a critical task to evaluate their suitability and performance. Between the employed devices, Single-Board Computers arise as multi-purpose and affordable systems. The literature has explored Single-Board Computers performance when running high-level benchmarks specialized in particular application scenarios, such as AI or medical applications. However, lower-level benchmarking applications and datasets are needed to enable new Edge-based AI solutions for network, system and service management based on device and component performance, such as individual device identification. Thus, this paper presents LwHBench, a low-level hardware benchmarking application for Single-Board Computers that measures the performance of CPU, GPU, Memory and Storage taking into account the component constraints in these types of devices. LwHBench has been implemented for Raspberry Pi devices and run for 100 days on a set of 45 devices to generate an extensive dataset that allows the usage of AI techniques in scenarios where performance data can help in the device management process. Besides, to demonstrate the inter-scenario capability of the dataset, a series of AI-enabled use cases about device identification and context impact on performance are presented as exploration of the published data. Finally, the benchmark application has been adapted and applied to an agriculture-focused scenario where three RockPro64 devices are present.
We explore Markov-modulated marked Poisson processes (MMMPPs) as a natural framework for modelling patients' disease dynamics over time based on medical claims data. In claims data, observations do not only occur at random points in time but are also informative, i.e. driven by unobserved disease levels, as poor health conditions usually lead to more frequent interactions with the healthcare system. Therefore, we model the observation process as a Markov-modulated Poisson process, where the rate of healthcare interactions is governed by a continuous-time Markov chain. Its states serve as proxies for the patients' latent disease levels and further determine the distribution of additional data collected at each observation time, the so-called marks. Overall, MMMPPs jointly model observations and their informative time points by comprising two state-dependent processes: the observation process (corresponding to the event times) and the mark process (corresponding to event-specific information), which both depend on the underlying states. The approach is illustrated using claims data from patients diagnosed with chronic obstructive pulmonary disease (COPD) by modelling their drug use and the interval lengths between consecutive physician consultations. The results indicate that MMMPPs are able to detect distinct patterns of healthcare utilisation related to disease processes and reveal inter-individual differences in the state-switching dynamics.
Software weaknesses that create attack surfaces for adversarial exploits, such as lateral SQL injection (LSQLi) attacks, are usually introduced during the design phase of software development. Security design patterns are sometimes applied to tackle these weaknesses. However, due to the stealthy nature of lateral-based attacks, employing traditional security patterns to address these threats is insufficient. Hence, we present SEAL, a secure design that extrapolates architectural, design, and implementation abstraction levels to delegate security strategies toward tackling LSQLi attacks. We evaluated SEAL using case study software, where we assumed the role of an adversary and injected several attack vectors tasked with compromising the confidentiality and integrity of its database. Our evaluation of SEAL demonstrated its capacity to address LSQLi attacks.
Zero Trust is a novel cybersecurity model that focuses on continually evaluating trust to prevent the initiation and horizontal spreading of attacks. A cloud-native Service Mesh is an example of Zero Trust Architecture that can filter out external threats. However, the Service Mesh does not shield the Application Owner from internal threats, such as a rogue administrator of the cluster where their application is deployed. In this work, we are enhancing the Service Mesh to allow the definition and reinforcement of a Verifiable Configuration that is defined and signed off by the Application Owner. Backed by automated digital signing solutions and confidential computing technologies, the Verifiable Configuration allows changing the trust model of the Service Mesh, from the data plane fully trusting the control plane to partially trusting it. This lets the application benefit from all the functions provided by the Service Mesh (resource discovery, traffic management, mutual authentication, access control, observability), while ensuring that the Cluster Administrator cannot change the state of the application in a way that was not intended by the Application Owner.
Sim-to-real is a mainstream method to cope with the large number of trials needed by typical deep reinforcement learning. However, transferring a policy trained in simulation to actual hardware remains challenging due to the reality gap. In particular, the characteristics of actuators in legged robots have a considerable influence on sim-to-real transfer. High reduction ratio gears are widely used in actuators, and the reality gap issue becomes especially pronounced when even the utilization of backdrivability is considered to control joints compliantly. We propose a new simulation model of gears to address this gap. Additionally, the difficulty in achieving stable bipedal locomotion causes typical methods to fail to tune physical parameters in simulation with the behavior of transferred policy. Thus, we propose a method for system identification that can utilize failed attempts. The method's effectiveness is verified using a biped robot, the ROBOTIS-OP3, and the sim-to-real transferred policy can stabilize the robot under severe disturbances and walk on uneven surfaces without force and torque sensors.
Discovering new intents is of great significance to establishing Bootstrapped Task-Oriented Dialogue System. Most existing methods either lack the ability to transfer prior knowledge in the known intent data or fall into the dilemma of forgetting prior knowledge in the follow-up. More importantly, these methods do not deeply explore the intrinsic structure of unlabeled data, so they can not seek out the characteristics that make an intent in general. In this paper, starting from the intuition that discovering intents could be beneficial to the identification of the known intents, we propose a probabilistic framework for discovering intents where intent assignments are treated as latent variables. We adopt Expectation Maximization framework for optimization. Specifically, In E-step, we conduct discovering intents and explore the intrinsic structure of unlabeled data by the posterior of intent assignments. In M-step, we alleviate the forgetting of prior knowledge transferred from known intents by optimizing the discrimination of labeled data. Extensive experiments conducted in three challenging real-world datasets demonstrate our method can achieve substantial improvements.
Currently there exist many blockchains with weak trust guarantees, limiting applications and participation. Existing solutions to boost the trust using a stronger blockchain, e.g., via checkpointing, requires the weaker blockchain to give up sovereignty. In this paper we propose a family of protocols in which multiple blockchains interact to create a combined ledger with boosted trust. We show that even if several of the interacting blockchains cease to provide security guarantees, the combined ledger continues to be secure - our TrustBoost protocols achieve the optimal threshold of tolerating the insecure blockchains. Furthermore, the protocol simply operates via smart contracts and require no change to the underlying consensus protocols of the participating blockchains, a form of "consensus on top of consensus". The protocols are lightweight and can be used on specific (e.g., high value) transactions; we demonstrate the practicality by implementing and deploying TrustBoost as cross-chain smart contracts in the Cosmos ecosystem using approximately 3,000 lines of Rust code, made available as open source. Our evaluation shows that using 10 Cosmos chains in a local testnet, TrustBoost has a gas cost of roughly $2 with a latency of 2 minutes per request, which is in line with the cost on a high security chain such as Bitcoin or Ethereum.
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