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We study the problem of automatically repairing infinite-state software programs w.r.t. temporal hyperproperties. As a first step, we present a repair approach for the temporal logic HyperLTL based on symbolic execution, constraint generation, and syntax-guided synthesis of repair expression (SyGuS). To improve the repair quality, we introduce the notation of a transparent repair that aims to find a patch that is as close as possible to the original program. As a practical realization, we develop an iterative repair approach. Here, we search for a sequence of repairs that are closer and closer to the original program's behavior. We implement our method in a prototype and report on encouraging experimental results using off-the-shelf SyGuS solvers., Comment: CAV 2024

In distributed systems with processes that do not share a global clock, \emph{partial synchrony} is achieved by clock synchronization that guarantees bounded clock skew among all applications. Existing solutions for distributed runtime verification under partial synchrony against temporal logic specifications are exact but suffer from significant computational overhead. In this paper, we propose an \emph{approximate} distributed monitoring algorithm for Signal Temporal Logic (STL) that mitigates this issue by abstracting away potential interleaving behaviors. This conservative abstraction enables a significant speedup of the distributed monitors, albeit with a tradeoff in accuracy. We address this tradeoff with a methodology that combines our approximate monitor with its exact counterpart, resulting in enhanced efficiency without sacrificing precision. We evaluate our approach with multiple experiments, showcasing its efficacy in both real-world applications and synthetic examples., Comment: Full version of the paper to appear in RV 2024

Causality is the relationship where one event contributes to the production of another, with the cause being partly responsible for the effect and the effect partly dependent on the cause. In this paper, we propose a novel and effective method to formally reason about the causal effect of events in engineered systems, with application for finding the root-cause of safety violations in embedded and cyber-physical systems. We are motivated by the notion of actual causality by Halpern and Pearl, which focuses on the causal effect of particular events rather than type-level causality, which attempts to make general statements about scientific and natural phenomena. Our first contribution is formulating discovery of actual causality in computing systems modeled by transition systems as an SMT solving problem. Since datasets for causality analysis tend to be large, in order to tackle the scalability problem of automated formal reasoning, our second contribution is a novel technique based on abstraction-refinement that allows identifying for actual causes within smaller abstract causal models. We demonstrate the effectiveness of our approach (by several orders of magnitude) using three case studies to find the actual cause of violations of safety in (1) a neural network controller for a Mountain Car, (2) a controller for a Lunar Lander obtained by reinforcement learning, and (3) an MPC controller for an F-16 autopilot simulator.

We present the first decentralized algorithm for detecting predicates over continuous-time signals under partial synchrony. A distributed cyber-physical system (CPS) consists of a network of agents, each of which measures (or computes) a continuous-time signal. Examples include distributed industrial controllers connected over wireless networks and connected vehicles in traffic. The safety requirements of such CPS, expressed as logical predicates, must be monitored at runtime. This monitoring faces three challenges: first, every agent only knows its own signal, whereas the safety requirement is global and carries over multiple signals. Second, the agents' local clocks drift from each other, so they do not even agree on the time. Thus, it is not clear which signal values are actually synchronous to evaluate the safety predicate. Third, CPS signals are continuous-time so there are potentially uncountably many safety violations to be reported. In this paper, we present the first decentralized algorithm for detecting conjunctive predicates in this setup. Our algorithm returns all possible violations of the predicate, which is important for eliminating bugs from distributed systems regardless of actual clock drift. We prove that this detection algorithm is in the same complexity class as the detector for discrete systems. We implement our detector and validate it experimentally., Comment: 23 pages, 6 figures, accepted to conference Runtime Verification 2023

Probabilistic hyperproperties express probabilistic relations between different executions of systems with uncertain behavior. HyperPCTL allows to formalize such properties, where quantification over probabilistic schedulers resolves potential non-determinism. In this paper we propose an extension named AHyperPCTL to additionally introduce asynchronicity between the observed executions by quantifying over stutter-schedulers, which may randomly decide to delay scheduler decisions by idling. To our knowledge, this is the first asynchronous extension of a probabilistic branching-time hyperlogic. We show that AHyperPCTL can express interesting information-flow security policies, and propose a model checking algorithm for a decidable fragment., Comment: to be published in the Proceedings of QEST 2023

Industrial Control Systems (ICS) are often built from geographically distributed components and often use programmable logic controllers for localized processes. Since verification of such systems is challenging because of both time sensitivity of the system specifications and the inherent asynchrony in distributed components, developing runtime assurance that verifies not just the correctness of different components, but also generates aggregated statistics of the systems is of interest. In this paper, we first present a general technique for runtime monitoring of distributed applications whose behavior can be modeled as input/output {\em streams} with an internal computation module in the partially synchronous semantics, where an imperfect clock synchronization algorithm is assumed. Second, we propose a generalized stream-based decentralized runtime verification technique. We also rigorously evaluate our algorithm on extensive synthetic experiments and several ICS and aircraft SBS message datasets.

Many types of attacks on confidentiality stem from the nondeterministic nature of the environment that computer programs operate in (e.g., schedulers and asynchronous communication channels). In this paper, we focus on verification of confidentiality in nondeterministic environments by reasoning about asynchronous hyperproperties. First, we generalize the temporal logic A-HLTL to allow nested trajectory quantification, where a trajectory determines how different execution traces may advance and stutter. We propose a bounded model checking algorithm for A-HLTL based on QBF-solving for a fragment of the generalized A-HLTL and evaluate it by various case studies on concurrent programs, scheduling attacks, compiler optimization, speculative execution, and cache timing attacks. We also rigorously analyze the complexity of model checking for different fragments of A-HLTL., Comment: 34 pages

Bounded model checking (BMC) is an effective technique for hunting bugs by incrementally exploring the state space of a system. To reason about infinite traces through a finite structure and to ultimately obtain completeness, BMC incorporates loop conditions that revisit previously observed states. This paper focuses on developing loop conditions for BMC of HyperLTL- a temporal logic for hyperproperties that allows expressing important policies for security and consistency in concurrent systems, etc. Loop conditions for HyperLTL are more complicated than for LTL, as different traces may loop inconsistently in unrelated moments. Existing BMC approaches for HyperLTL only considered linear unrollings without any looping capability, which precludes both finding small infinite traces and obtaining a complete technique. We investigate loop conditions for HyperLTL BMC, where the HyperLTL formula can contain up to one quantifier alternation. We first present a general complete automata-based technique which is based on bounds of maximum unrollings. Then, we introduce alternative simulation-based algorithms that allow exploiting short loops effectively, generating SAT queries whose satisfiability guarantees the outcome of the original model checking problem. We also report empirical evaluation of the prototype implementation of our BMC techniques using Z3py., Comment: 20 pages

Objective: We develop a deep learning framework based on the pre-trained Bidirectional Encoder Representations from Transformers (BERT) model using unstructured clinical notes from electronic health records (EHRs) to predict the risk of disease progression from Mild Cognitive Impairment (MCI) to Alzheimer's Disease (AD). Materials and Methods: We identified 3657 patients diagnosed with MCI together with their progress notes from Northwestern Medicine Enterprise Data Warehouse (NMEDW) between 2000-2020. The progress notes no later than the first MCI diagnosis were used for the prediction. We first preprocessed the notes by deidentification, cleaning and splitting, and then pretrained a BERT model for AD (AD-BERT) based on the publicly available Bio+Clinical BERT on the preprocessed notes. The embeddings of all the sections of a patient's notes processed by AD-BERT were combined by MaxPooling to compute the probability of MCI-to-AD progression. For replication, we conducted a similar set of experiments on 2563 MCI patients identified at Weill Cornell Medicine (WCM) during the same timeframe. Results: Compared with the 7 baseline models, the AD-BERT model achieved the best performance on both datasets, with Area Under receiver operating characteristic Curve (AUC) of 0.8170 and F1 score of 0.4178 on NMEDW dataset and AUC of 0.8830 and F1 score of 0.6836 on WCM dataset. Conclusion: We developed a deep learning framework using BERT models which provide an effective solution for prediction of MCI-to-AD progression using clinical note analysis.

Transactions involving multiple blockchains are implemented by cross-chain protocols. These protocols are based on smart contracts, programs that run on blockchains, executed by a network of computers. Because smart contracts can automatically transfer ownership of cryptocurrencies, electronic securities, and other valuable assets among untrusting parties, verifying the runtime correctness of smart contracts is a problem of compelling practical interest. Such verification is challenging since smart contract execution is time-sensitive, and the clocks on different blockchains may not be perfectly synchronized. This paper describes a method for runtime monitoring of blockchain executions. First, we propose a generalized runtime verification technique for verifying partially synchronous distributed computations for the metric temporal logic (MTL) by exploiting bounded-skew clock synchronization. Second, we introduce a progression-based formula rewriting scheme for monitoring \MTL specifications which employ SMT solving techniques and report experimental results., Comment: 2022 IEEE 42nd International Conference on Distributed Computing Systems (ICDCS)