ACM Transactions on Cyber-Physical Systems

Lu Feng

Abhishek Dubey

Introduction to the Special Issue on ICCPS 2023

《ICCPS 2023 特刊导言》

To realize the grand vision of automated driving in smart vehicle cyber-physical systems (CPS), one important task is to support the merging of connected automated vehicles (CAVs) from a metered-ramp to highway. Certain safety rules must be guaranteed. However, this demand is complicated by the inherently unreliable wireless communications. In this article, we focus on the well adopted constant-time-headway (CTH) safety rule. We propose a highway and metered-ramp CAV collaborative merging protocol, and formally prove its guarantee of the CTH safety and liveness under arbitrary wireless data packet losses . These theoretical claims are further validated by our simulations. Furthermore, the simulation results also show significant improvements in the merging efficiency over other solution alternatives. Particularly, the merging success rates are more than 99% better in 11 out of 18 comparison pairs, and 0% (i.e., tied) ∼ 71% better in the remaining 7 comparison pairs.

Xueli Fan

Qixin Wang

Jie Liu

A Reliable Wireless Protocol for Highway and Metered-Ramp CAV Collaborative Merging with Constant-Time-Headway Safety Guarantee

高速公路和定量匝道车联网协作汇入的可靠无线协议，具备恒定时间车头时距安全保障

Many control applications in real-time cyber-physical systems are represented as Directed Acyclic Graphs ( DAGs ) due to complex interactions among their functional components, and executed on distributed heterogeneous platforms. Data communication between dependent task nodes running on different processing elements are often realized through message transmission over a public network, and are hence susceptible to multiple security threats such as snooping , alteration and spoofing . Several alternative security protocols having varying security strengths and associated implementation overheads are available in the market, for incorporating confidentiality , integrity and authentication on the transmitted messages. While message size and conceptually its associated transmission overheads may be marginally increased due to the assignment of security protocols, significant computation overheads must be incurred for securing the message at the location of its source task node and for unlocking security/message extraction at the destination. Obtained security strengths and associated computation overheads vary depending on the set of protocols chosen for a given message from an available pool of protocols. Given lower bounds on the security demands of an application's messages, selecting the appropriate protocols for each message such that a system's overall security is maximized while satisfying constraints related to the resource, task precedence and deadline, is a challenging and computationally hard problem. In this paper, we propose an efficient heuristic strategy called SHIELD for security-aware real-time scheduling of DAG-structured applications to be executed on distributed heterogeneous systems. The efficacy of the proposed scheduler is exhibited through extensive simulation-based experiments using two DAG-structured application benchmarks. Our performance evaluation results demonstrate that SHIELD significantly outperforms two greedy baseline strategies SHIELDb in terms of solution generation times (i.e., run-times) and SHIELDf in terms of achieved security utility. Additionally, a case study on the Traction Control application in automotive systems has been included to exhibit the applicability of SHIELD in real-world settings.

Debabrata Senapati

Pooja Bhagat

Chandan Karfa

Arnab Sarkar

SHIELD: Security-aware Schreduling for Real-Time DAGs on Heterogeneous Systems

SHIELD：异构系统中实时DAG的安全感知调度

Cheap commercial off-the-shelf (COTS) First-Person View (FPV) drones have become widely available for consumers in recent years. Unfortunately, they also provide low-cost attack opportunities to malicious users. Thus, effective methods to detect the presence of unknown and non-cooperating drones within a restricted area are highly demanded. Approaches based on detection of drones based on emitted video stream have been proposed, but were not yet shown to work against other similar benign traffic, such as that generated by wireless security cameras. Most importantly, these approaches were not studied in the context of detecting new unprofiled drone types. In this work, we propose a novel drone detection framework, which leverages specific patterns in video traffic transmitted by drones. The patterns consist of repetitive synchronization packets (we call pivots), which we use as features for a machine learning classifier. We show that our framework can achieve up to 99% in detection accuracy over an encrypted WiFi channel using only 170 packets originated from the drone within 820ms time period. Our framework is able to identify drone transmissions even among very similar WiFi transmissions (such as video streams originated from security cameras) as well as in noisy scenarios with background traffic. Furthermore, the design of our pivot features enables the classifier to detect unprofiled drones in which the classifier has never trained on and is refined using a novel feature selection strategy that selects the features that have the discriminative power of detecting new unprofiled drones.

Anas Alsoliman

Giulio Rigoni

Davide Callegaro

Marco Levorato

Cristina M. Pinotti

Mauro Conti

Intrusion Detection Framework for Invasive FPV Drones Using Video Streaming Characteristics

基于视频流特征的侵入式FPV无人机入侵检测框架

The adoption rate of EVs has witnessed a significant increase in recent years driven by multiple factors, chief among which is the increased flexibility and ease of access to charging infrastructure. To improve user experience and increase system flexibility, mobile applications have been incorporated into the EV charging ecosystem. EV charging mobile applications allow consumers to remotely trigger actions on charging stations and use functionalities such as start/stop charging sessions, pay for usage, and locate charging stations, to name a few. In this article, we study the security posture of the EV charging ecosystem against a new type of remote that exploits vulnerabilities in the EV charging mobile applications as an attack surface. We leverage a combination of static and dynamic analysis techniques to analyze the security of widely used EV charging mobile applications. Our analysis was performed on 31 of the most widely used mobile applications including their interactions with various components such as cloud management systems. The attack scenarios that exploit these vulnerabilities were verified on a real-time co-simulation test bed. Our discoveries indicate the lack of user/vehicle verification and improper authorization for critical functions, which allow adversaries to remotely hijack charging sessions and launch attacks against the connected critical infrastructure. The attacks were demonstrated using the EVCS mobile applications showing the feasibility and the applicability of our attacks. Indeed, we discuss specific remote attack scenarios and their impact on EV users. More importantly, our analysis results demonstrate the feasibility of leveraging existing vulnerabilities across various EV charging mobile applications to perform wide-scale coordinated remote charging/discharging attacks against the connected critical infrastructure (e.g., power grid), with significant economical and operational implications. Finally, we propose countermeasures to secure the infrastructure and impede adversaries from performing reconnaissance and launching remote attacks using compromised accounts.

Khaled Sarieddine

Mohammad Ali Sayed

Sadegh Torabi

Ribal Atallah

Chadi Assi

Investigating the Security of EV Charging Mobile Applications as an Attack Surface

探究电动汽车充电移动应用作为攻击面的安全性

This paper introduces a model-based approach for training feedback controllers for an autonomous agent operating in a highly nonlinear (albeit deterministic) environment. We desire the trained policy to ensure that the agent satisfies specific task objectives and safety constraints, both expressed in Discrete-Time Signal Temporal Logic (DT-STL). One advantage for reformulation of a task via formal frameworks, like DT-STL, is that it permits quantitative satisfaction semantics. In other words, given a trajectory and a DT-STL formula, we can compute the robustness , which can be interpreted as an approximate signed distance between the trajectory and the set of trajectories satisfying the formula. We utilize feedback control, and we assume a feed forward neural network for learning the feedback controller. We show how this learning problem is similar to training recurrent neural networks (RNNs), where the number of recurrent units is proportional to the temporal horizon of the agent's task objectives. This poses a challenge: RNNs are susceptible to vanishing and exploding gradients, and naïve gradient descent-based strategies to solve long-horizon task objectives thus suffer from the same problems. To tackle this challenge, we introduce a novel gradient approximation algorithm based on the idea of dropout or gradient sampling. One of the main contributions is the notion of controller network dropout , where we approximate the NN controller in several time-steps in the task horizon by the control input obtained using the controller in a previous training step. We show that our control synthesis methodology, can be quite helpful for stochastic gradient descent to converge with less numerical issues, enabling scalable backpropagation over long time horizons and trajectories over high dimensional state spaces. We demonstrate the efficacy of our approach on various motion planning applications requiring complex spatio-temporal and sequential tasks ranging over thousands of time-steps.

Navid Hashemi

Bardh Hoxha

Danil Prokhorov

Georgios Fainekos

Jyotirmoy V. Deshmukh

Scaling Learning based Policy Optimization for Temporal Logic Tasks by Controller Network Dropout

基于控制器网络退出的时序逻辑任务策略优化学习扩展

Although wireless control is one of the key technologies for future industries, most wireless networks are only used for monitoring. When wireless networks are applied to transmit control commands, the uncertain link qualities and limited network resources may destroy the performance of multi-loop control systems. Hence, it is critical to allocate these resources to optimize the control performance as the network condition changes and plants evolve. This paper presents comprehensive optimal scheduling strategies for wireless control systems based on adaptive dynamic programming. Firstly, we propose an effective adaptive dynamic programming scheduling (ADPS) strategy to solve the optimal scheduling problem based on the single-step control performance at run-time while significantly reducing computational complexity. Moreover, to overcome the ”short-sightedness” of single-step performance prediction, we extend ADPS to ADPS-m ( m ulti-step prediction), which optimizes multi-step performance by incorporating a longer-horizon evolution of the plants. Furthermore, we propose ADPS-H ( H eterogeneous flow scheduling) to support heterogeneous flows with different data rates and sizes, and ADPS-H-m ( m ulti-step prediction for H eterogeneous flow scheduling), which schedules heterogeneous flows in a longer prediction horizon. We prove that all these scheduling strategies can achieve optimality and stability under mild assumptions. Extensive experiments integrating TOSSIM and MATLAB/Simulink are performed to evaluate all of the proposed methods in case studies of four- and ten-loop control systems. The simulation results demonstrate that these strategies can effectively improve the control performance at lower computing costs under both cyber and physical disturbances. Under the noise level of -76 dBm, for the four-loop case, ADPS achieves the same control performance as the linear programming while saving 99.5% of the execution time. ADPS-m further improves the control performance by up to 27.0% compared with ADPS at the prediction horizon of 3, and ADPS-H-m improves the performance by up to 32.3% and 8.4% compared with round-robin and ADPS-H, respectively. And the ten-loop case indicates the effectiveness and scalability of the proposed approaches.

Ruijie Fu

Lancong Guo

An Zou

Cailian Chen

Xinping Guan

Yehan Ma

Comprehensive Optimal Network Scheduling Strategies for Wireless Control Systems

无线控制系统综合优化网络调度策略

Extreme outside temperatures resulting from heat waves, winter storms, and similar weather-related events trigger the Heating Ventilation and Air Conditioning (HVAC) systems, resulting in challenging, and potentially catastrophic, peak loads. As a consequence, such extreme outside temperatures put a strain on power grids and may thus lead to blackouts. To avoid the financial and personal repercussions of peak loads, demand response and power conservation represent promising solutions. Despite numerous efforts, it has been shown that the current state-of-the-art fails to consider (1) the complexity of human behavior when interacting with power conservation systems and (2) realistic home-level power dynamics. As a consequence, this leads to approaches that are (1) ineffective due to poor long-term user engagement and (2) too abstract to be used in real-world settings. In this article, we propose an auction theory-based power conservation framework for HVAC designed to address such individual human component through a three-fold approach: personalized preferences of power conservation, models of realistic user behavior , and realistic home-level power dynamics . In our framework, the System Operator sends Load Serving Entities (LSEs) the required power saving to tackle peak loads at the residential distribution feeder. Each LSE then prompts its users to provide bids , i.e., personalized preferences of thermostat temperature adjustments, along with corresponding financial compensations. We employ models of realistic user behavior by means of online surveys to gather user bids and evaluate user interaction with such system. Realistic home-level power dynamics are implemented by our machine learning-based Power Saving Predictions (PSP) algorithm, calculating the individual power savings in each user’s home resulting from such bids. A machine learning-based PSPs algorithm is executed by the users’ Smart Energy Management System (SEMS). PSP translates temperature adjustments into the corresponding power savings. Then, the SEMS sends bids back to the LSE, which selects the auction winners through an optimization problem called POwer Conservation Optimization (POCO). We prove that POCO is NP-hard, and thus provide two approaches to solve this problem. One approach is an optimal pseudo-polynomial algorithm called DYnamic programming Power Saving (DYPS), while the second is a heuristic polynomial time algorithm called Greedy Ranking AllocatioN (GRAN). EnergyPlus, the high-fidelity and gold-standard energy simulator funded by the U.S. Department of Energy, was used to validate our experiments, as well as to collect data to train PSP. We further evaluate the results of the auctions across several scenarios, showing that, as expected, DYPS finds the optimal solution, while GRAN outperforms recent state-of-the-art approaches.

Enrico Casella

Simone Silvestri

D. A. Baker

Sajal K. Das

A Human-Centered Power Conservation Framework based on Reverse Auction Theory and Machine Learning

基于逆向拍卖理论和机器学习的人本节能框架

短名	ACM Trans. Cyber-Phys. Syst.
Journal Impact	2.12
国际分区	COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS(Q3)

ISSN	2378-962X, 2378-9638
h-index	23

涉及主题	计算机科学工程类操作系统数学物理人工智能计算机安全程序设计语言生物量子力学经济计算机网络热力学地理电信信息物理系统分布式计算统计电气工程法学政治学管理实时计算生态学
出版信息	出版商: Association for Computing Machinery (ACM)，出版周期: ，期刊类型: journal，开源期刊: 否
基本数据	创刊年份: 2017，原创研究文献占比: 97.06%，自引率:5.00%， Gold OA占比: 3.85%

ACM Transactions on Cyber-Physical Systems

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Introduction to the Special Issue on ICCPS 2023

A Reliable Wireless Protocol for Highway and Metered-Ramp CAV Collaborative Merging with Constant-Time-Headway Safety Guarantee

SHIELD: Security-aware Schreduling for Real-Time DAGs on Heterogeneous Systems

Intrusion Detection Framework for Invasive FPV Drones Using Video Streaming Characteristics

Investigating the Security of EV Charging Mobile Applications as an Attack Surface

Introduction to the Special Issue on ICCPS 2023

SHIELD: Security-aware Schreduling for Real-Time DAGs on Heterogeneous Systems

Scaling Learning based Policy Optimization for Temporal Logic Tasks by Controller Network Dropout

Comprehensive Optimal Network Scheduling Strategies for Wireless Control Systems

A Human-Centered Power Conservation Framework based on Reverse Auction Theory and Machine Learning