A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Adversarial attacks hide cyber-physical attacks in ICS.
Algorithm mines environment assumptions for cyber-physical systems.
CyPhERS provides real-time event info for CPSs, avoiding downtime.
New adversarial training method improves robustness of power system controllers.
The paper detects adversarial examples in LECs for regression in CPS using variational autoencoder.
The paper presents a method to compute trusted confidence bounds for LECs in CPS.
The paper uses conformal prediction to monitor CPS with machine learning components.
This paper enhances privacy in statistical model checking of cyber-physical systems.
Survey of algorithms for testing AI-driven CPS safety.
NSIBF detects anomalies in CPS using neural system identification and Bayesian filtering.
New approach detects cyber-attacks in real-time.
This paper explores formal verification for autonomous systems, identifying limitations and proposing improvements.
A generalized gamification framework is introduced as a form of smart infrastructure with potential to improve sustainability and energy efficiency by leveraging humans-in-the-loop strategy. The proposed framework enables a Human-Centric Cyber-Physical System using an interface to allow building managers to interact wi…
Survey of RL methods for control systems with time delays.
Real-time detection of out-of-distribution data in CPS control systems.
Method predicts hardware resource usage by control software with guaranteed linear convergence.
This dissertation uses deep reinforcement learning to improve drone flight control.
Defense strategy improves controller robustness against adversarial attacks.
Cyber-Physical Systems (CPSs) have been pervasive including smart grid, autonomous automobile systems, medical monitoring, process control systems, robotics systems, and automatic pilot avionics. As usually implemented on embedded devices, CPS is typically constrained by computation capacity and energy consumption. In …
This paper surveys ML applications in SG for cyberattacks.
The world is witnessing an unprecedented growth of cyber-physical systems (CPS), which are foreseen to revolutionize our world {via} creating new services and applications in a variety of sectors such as environmental monitoring, mobile-health systems, intelligent transportation systems and so on. The {information and …
Machine learning algorithms increasingly influence our decisions and interact with us in all parts of our daily lives. Therefore, just as we consider the safety of power plants, highways, and a variety of other engineered socio-technical systems, we must also take into account the safety of systems involving machine le…
Paper tackles delays in multi-agent reinforcement learning, improving performance.
Paper robustifies reinforcement learning agents against action space perturbations.
Blockchain aims to improve trust in AI systems, but lacks systematic studies.
FSPT identifies training space to prevent ML model extrapolation.
The implementation of smart building technology in the form of smart infrastructure applications has great potential to improve sustainability and energy efficiency by leveraging humans-in-the-loop strategy. However, human preference in regard to living conditions is usually unknown and heterogeneous in its manifestati…
Proposes a game-theoretic framework to motivate energy-efficient behavior in smart buildings.
Paper studies autoencoder-based anomaly detectors' robustness to adversarial poisoning attacks.
Cyber-physical systems, such as mobile robots, must respond adaptively to dynamic operating conditions. Effective operation of these systems requires that sensing and actuation tasks are performed in a timely manner. Additionally, execution of mission specific tasks such as imaging a room must be balanced against the n…
The scale of Internet-connected systems has increased considerably, and these systems are being exposed to cyber attacks more than ever. The complexity and dynamics of cyber attacks require protecting mechanisms to be responsive, adaptive, and scalable. Machine learning, or more specifically deep reinforcement learning…
Models play an essential role in the design process of cyber-physical systems. They form the basis for simulation and analysis and help in identifying design problems as early as possible. However, the construction of models that comprise physical and digital behavior is challenging. Therefore, there is considerable in…
Study detects and mitigates stealthy DDoS attacks in IoT networks.
Generative model captures repetitive industrial processes with varying durations and dynamics.
Cyber-physical systems often consist of entities that interact with each other over time. Meanwhile, as part of the continued digitization of industrial processes, various sensor technologies are deployed that enable us to record time-varying attributes (a.k.a., time series) of such entities, thus producing correlated …
Fueled by massive amounts of data, models produced by machine-learning (ML) algorithms, especially deep neural networks, are being used in diverse domains where trustworthiness is a concern, including automotive systems, finance, health care, natural language processing, and malware detection. Of particular concern is …
Digital twin reduces costs in various fields.
A method for identifying NPWARX models with arbitrary domains using probabilistic mixture models.
The growing prospect of deep reinforcement learning (DRL) being used in cyber-physical systems has raised concerns around safety and robustness of autonomous agents. Recent work on generating adversarial attacks have shown that it is computationally feasible for a bad actor to fool a DRL policy into behaving sub optima…
Research evaluates data poisoning attacks on regression learning and introduces a new defense strategy.
Cyber-physical system applications such as autonomous vehicles, wearable devices, and avionic systems generate a large volume of time-series data. Designers often look for tools to help classify and categorize the data. Traditional machine learning techniques for time-series data offer several solutions to solve these …
The security of Deep Reinforcement Learning (Deep RL) algorithms deployed in real life applications are of a primary concern. In particular, the robustness of RL agents in cyber-physical systems against adversarial attacks are especially vital since the cost of a malevolent intrusions can be extremely high. Studies hav…
Paper tackles cybersecurity attack detection with an ensemble approach.
A new machine learning framework reduces IoT data transfer by two orders of magnitude.
Intelligent transportation systems (ITSs) will be a major component of tomorrow's smart cities. However, realizing the true potential of ITSs requires ultra-low latency and reliable data analytics solutions that can combine, in real-time, a heterogeneous mix of data stemming from the ITS network and its environment. Su…
Despite the tremendous advances that have been made in the last decade on developing useful machine-learning applications, their wider adoption has been hindered by the lack of strong assurance guarantees that can be made about their behavior. In this paper, we consider how formal verification techniques developed for …
Performance monitoring, anomaly detection, and root-cause analysis in complex cyber-physical systems (CPSs) are often highly intractable due to widely diverse operational modes, disparate data types, and complex fault propagation mechanisms. This paper presents a new data-driven framework for root-cause analysis, based…
Robustness of Deep Reinforcement Learning (DRL) algorithms towards adversarial attacks in real world applications such as those deployed in cyber-physical systems (CPS) are of increasing concern. Numerous studies have investigated the mechanisms of attacks on the RL agent's state space. Nonetheless, attacks on the RL a…