Abdulrahman Fahim, E. Papalexakis, S. Krishnamurthy, Amit K. Roy Chowdhury, L. Kaplan, T. Abdelzaher
Steerable cameras that can be controlled via a network, to retrieve telemetries of interest have become popular. In this paper, we develop a framework called AcTrak, to automate a camera’s motion to appropriately switch between (a) zoom ins on existing targets in a scene to track their activities, and (b) zoom out to search for new targets arriving to the area of interest. Specifically, we seek to achieve a good trade-off between the two tasks, i.e., we want to ensure that new targets are observed by the camera before they leave the scene, while also zooming in on existing targets frequently enough to monitor their activities. There exist prior control algorithms for steering cameras to optimize certain objectives; however, to the best of our knowledge, none have considered this problem, and do not perform well when target activity tracking is required. AcTrak automatically controls the camera’s PTZ configurations using reinforcement learning (RL), to select the best camera position given the current state. Via simulations using real datasets, we show that AcTrak detects newly arriving targets 30% faster than a non-adaptive baseline and rarely misses targets, unlike the baseline which can miss up to 5% of the targets. We also implement AcTrak to control a real camera and demonstrate that in comparison with the baseline, it acquires about 2× more high resolution images of targets.
{"title":"AcTrak: Controlling a Steerable Surveillance Camera using Reinforcement Learning","authors":"Abdulrahman Fahim, E. Papalexakis, S. Krishnamurthy, Amit K. Roy Chowdhury, L. Kaplan, T. Abdelzaher","doi":"10.1145/3585316","DOIUrl":"https://doi.org/10.1145/3585316","url":null,"abstract":"Steerable cameras that can be controlled via a network, to retrieve telemetries of interest have become popular. In this paper, we develop a framework called AcTrak, to automate a camera’s motion to appropriately switch between (a) zoom ins on existing targets in a scene to track their activities, and (b) zoom out to search for new targets arriving to the area of interest. Specifically, we seek to achieve a good trade-off between the two tasks, i.e., we want to ensure that new targets are observed by the camera before they leave the scene, while also zooming in on existing targets frequently enough to monitor their activities. There exist prior control algorithms for steering cameras to optimize certain objectives; however, to the best of our knowledge, none have considered this problem, and do not perform well when target activity tracking is required. AcTrak automatically controls the camera’s PTZ configurations using reinforcement learning (RL), to select the best camera position given the current state. Via simulations using real datasets, we show that AcTrak detects newly arriving targets 30% faster than a non-adaptive baseline and rarely misses targets, unlike the baseline which can miss up to 5% of the targets. We also implement AcTrak to control a real camera and demonstrate that in comparison with the baseline, it acquires about 2× more high resolution images of targets.","PeriodicalId":7055,"journal":{"name":"ACM Transactions on Cyber-Physical Systems","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2023-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48191774","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
S. Chakraborty, S. Jha, Soheil Samii, Philipp Mundhenk
One might argue that automotive and allied domains like robotics serve as the best possible examples of what “cyber-physical systems” (CPS) are. Here, the correctness of the underlying electronics and software (or cyber) components are defined by the dynamics of the vehicle or the robot, viz., the physical components of the system. This shift in perspective on how electronics and software should be modeled and synthesized, and how their correctness should be defined, has led to a tremendous volume of research on CPS in recent times [7, 8, 43, 56]. At the same time, the volume of electronics and software in modern cars has also grown tremendously. Today, high-end cars have more than 100 control computers or electronic control units (ECUs) embedded in them, that run hundreds of millions of lines of software code implementing a range of diverse functions. These functions span across engine and brake control, to the body and entertainment domains. Cars are also equipped with a variety of cameras, radars, and lidar sensors that are used to perceive the external world and take the appropriate control actions as a part of driver assistance features that are common today. As such features continue to accelerate the evolution and adoption of fully autonomous vehicles, the role of electronics and software in the automotive domain is increasing at an unprecedented pace, and modern automobiles are now aptly referred
{"title":"Introduction to the Special Issue on Automotive CPS Safety & Security: Part 1","authors":"S. Chakraborty, S. Jha, Soheil Samii, Philipp Mundhenk","doi":"10.1145/3579986","DOIUrl":"https://doi.org/10.1145/3579986","url":null,"abstract":"One might argue that automotive and allied domains like robotics serve as the best possible examples of what “cyber-physical systems” (CPS) are. Here, the correctness of the underlying electronics and software (or cyber) components are defined by the dynamics of the vehicle or the robot, viz., the physical components of the system. This shift in perspective on how electronics and software should be modeled and synthesized, and how their correctness should be defined, has led to a tremendous volume of research on CPS in recent times [7, 8, 43, 56]. At the same time, the volume of electronics and software in modern cars has also grown tremendously. Today, high-end cars have more than 100 control computers or electronic control units (ECUs) embedded in them, that run hundreds of millions of lines of software code implementing a range of diverse functions. These functions span across engine and brake control, to the body and entertainment domains. Cars are also equipped with a variety of cameras, radars, and lidar sensors that are used to perceive the external world and take the appropriate control actions as a part of driver assistance features that are common today. As such features continue to accelerate the evolution and adoption of fully autonomous vehicles, the role of electronics and software in the automotive domain is increasing at an unprecedented pace, and modern automobiles are now aptly referred","PeriodicalId":7055,"journal":{"name":"ACM Transactions on Cyber-Physical Systems","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2023-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48339430","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Eugene Vinitsky, Nathan Lichtlé, Kanaad Parvate, A. Bayen
We study the ability of autonomous vehicles to improve the throughput of a bottleneck using a fully decentralized control scheme in a mixed autonomy setting. We consider the problem of improving the throughput of a scaled model of the San Francisco–Oakland Bay Bridge: a two-stage bottleneck where four lanes reduce to two and then reduce to one. Although there is extensive work examining variants of bottleneck control in a centralized setting, there is less study of the challenging multi-agent setting where the large number of interacting AVs leads to significant optimization difficulties for reinforcement learning methods. We apply multi-agent reinforcement algorithms to this problem and demonstrate that significant improvements in bottleneck throughput, from 20% at a 5% penetration rate to 33% at a 40% penetration rate, can be achieved. We compare our results to a hand-designed feedback controller and demonstrate that our results sharply outperform the feedback controller despite extensive tuning. Additionally, we demonstrate that the RL-based controllers adopt a robust strategy that works across penetration rates whereas the feedback controllers degrade immediately upon penetration rate variation. We investigate the feasibility of both action and observation decentralization and demonstrate that effective strategies are possible using purely local sensing. Finally, we open-source our code at https://github.com/eugenevinitsky/decentralized_bottlenecks.
{"title":"Optimizing Mixed Autonomy Traffic Flow with Decentralized Autonomous Vehicles and Multi-Agent Reinforcement Learning","authors":"Eugene Vinitsky, Nathan Lichtlé, Kanaad Parvate, A. Bayen","doi":"10.1145/3582576","DOIUrl":"https://doi.org/10.1145/3582576","url":null,"abstract":"We study the ability of autonomous vehicles to improve the throughput of a bottleneck using a fully decentralized control scheme in a mixed autonomy setting. We consider the problem of improving the throughput of a scaled model of the San Francisco–Oakland Bay Bridge: a two-stage bottleneck where four lanes reduce to two and then reduce to one. Although there is extensive work examining variants of bottleneck control in a centralized setting, there is less study of the challenging multi-agent setting where the large number of interacting AVs leads to significant optimization difficulties for reinforcement learning methods. We apply multi-agent reinforcement algorithms to this problem and demonstrate that significant improvements in bottleneck throughput, from 20% at a 5% penetration rate to 33% at a 40% penetration rate, can be achieved. We compare our results to a hand-designed feedback controller and demonstrate that our results sharply outperform the feedback controller despite extensive tuning. Additionally, we demonstrate that the RL-based controllers adopt a robust strategy that works across penetration rates whereas the feedback controllers degrade immediately upon penetration rate variation. We investigate the feasibility of both action and observation decentralization and demonstrate that effective strategies are possible using purely local sensing. Finally, we open-source our code at https://github.com/eugenevinitsky/decentralized_bottlenecks.","PeriodicalId":7055,"journal":{"name":"ACM Transactions on Cyber-Physical Systems","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2023-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48028055","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ruihang Wang, Zhi-Ying Cao, Xiaoxia Zhou, Yonggang Wen, Rui Tan
Deep reinforcement learning (DRL) has shown good performance in tackling Markov decision process (MDP) problems. As DRL optimizes a long-term reward, it is a promising approach to improving the energy efficiency of data center cooling. However, enforcement of thermal safety constraints during DRL’s state exploration is a main challenge. The widely adopted reward shaping approach adds negative reward when the exploratory action results in unsafety. Thus, it needs to experience sufficient unsafe states before it learns how to prevent unsafety. In this paper, we propose a safety-aware DRL framework for data center cooling control. It applies offline imitation learning and online post-hoc rectification to holistically prevent thermal unsafety during online DRL. In particular, the post-hoc rectification searches for the minimum modification to the DRL-recommended action such that the rectified action will not result in unsafety. The rectification is designed based on a thermal state transition model that is fitted using historical safe operation traces and able to extrapolate the transitions to unsafe states explored by DRL. Extensive evaluation for chilled water and direct expansion-cooled data centers in two climate conditions show that our approach saves 18% to 26.6% of total data center power compared with conventional control and reduces safety violations by 94.5% to 99% compared with reward shaping. We also extend the proposed framework to address data centers with non-uniform temperature distributions for detailed safety considerations. The evaluation shows that our approach saves 14% power usage compared with the PID control while addressing safety compliance during the training.
{"title":"Green Data Center Cooling Control via Physics-Guided Safe Reinforcement Learning","authors":"Ruihang Wang, Zhi-Ying Cao, Xiaoxia Zhou, Yonggang Wen, Rui Tan","doi":"10.1145/3582577","DOIUrl":"https://doi.org/10.1145/3582577","url":null,"abstract":"Deep reinforcement learning (DRL) has shown good performance in tackling Markov decision process (MDP) problems. As DRL optimizes a long-term reward, it is a promising approach to improving the energy efficiency of data center cooling. However, enforcement of thermal safety constraints during DRL’s state exploration is a main challenge. The widely adopted reward shaping approach adds negative reward when the exploratory action results in unsafety. Thus, it needs to experience sufficient unsafe states before it learns how to prevent unsafety. In this paper, we propose a safety-aware DRL framework for data center cooling control. It applies offline imitation learning and online post-hoc rectification to holistically prevent thermal unsafety during online DRL. In particular, the post-hoc rectification searches for the minimum modification to the DRL-recommended action such that the rectified action will not result in unsafety. The rectification is designed based on a thermal state transition model that is fitted using historical safe operation traces and able to extrapolate the transitions to unsafe states explored by DRL. Extensive evaluation for chilled water and direct expansion-cooled data centers in two climate conditions show that our approach saves 18% to 26.6% of total data center power compared with conventional control and reduces safety violations by 94.5% to 99% compared with reward shaping. We also extend the proposed framework to address data centers with non-uniform temperature distributions for detailed safety considerations. The evaluation shows that our approach saves 14% power usage compared with the PID control while addressing safety compliance during the training.","PeriodicalId":7055,"journal":{"name":"ACM Transactions on Cyber-Physical Systems","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47468330","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jiyang Chen, Tomasz Kloda, Rohan Tabish, Ayoosh Bansal, Chien-Ying Chen, Bo Liu, Sibin Mohan, Marco Caccamo, Lui Sha
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.
{"title":"SchedGuard++: Protecting against Schedule Leaks Using Linux Containers on Multi-Core Processors","authors":"Jiyang Chen, Tomasz Kloda, Rohan Tabish, Ayoosh Bansal, Chien-Ying Chen, Bo Liu, Sibin Mohan, Marco Caccamo, Lui Sha","doi":"10.1145/3565974","DOIUrl":"https://doi.org/10.1145/3565974","url":null,"abstract":"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.","PeriodicalId":7055,"journal":{"name":"ACM Transactions on Cyber-Physical Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135202259","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jarul Mehta, Guillaume Richard, Loren Lugosch, Derek Yu, B. Meyer
The controller area network (CAN) protocol, used in many modern vehicles for real-time inter-device communications, is known to have cybersecurity vulnerabilities, putting passengers at risk for data exfiltration and control system sabotage. To address this issue, researchers have proposed to utilize security measures based on cryptography and message authentication; unfortunately, such approaches are often too computationally expensive to be deployed in real time on CAN devices. Additionally, they have developed machine learning (ML) techniques to detect anomalies in CAN traffic and thereby prevent attacks. The main disadvantage of existing ML-based techniques is that they either depend on additional computational hardware or they heuristically assume that all communication anomalies are malicious. In this article, we show that tree-based learning ensembles outperform anomaly-based techniques like AutoRegressive Integrated Moving Average (ARIMA) and Z-Score when used to detect attacks that result in increased bus utilization. We evaluated the detection capacity of three tree-based ensembles, Adaboost, gradient boosting, and random forests, and collectively refer to these as DT-DS. We conclude that the decision tree ensemble with Adaboost performs best with an area under curve (AUC) score of 0.999, closely followed by gradient boosting and random forests with 0.997 and 0.991 AUC scores, respectively, when trained using message profiles. We observe that with an increase in the observation window, the DT-DS models present an average AUC score of 0.999, and offer a nearly perfect detection of attacks, at the cost of increased latency in detection of attacked messages. We evaluate the performance of the IDS for Aeronautical Radio, Incorporated– (ARINC) encoded CAN communication traffic in avionic systems, generated using an aerospace testbench, ARINC-825TBv2. The IDS has been evaluated against the active attacks of a state-of-the-art predictive attacker model. Additionally, we observed that the performance of IDS approaches such as ARIMA and Z-Score degrade considerably with a decrease in the size of the observation time window. In contrast, the performance of DT-DS models is consistent, with only an average drop of 0.005 in the AUC score.
{"title":"DT-DS: CAN Intrusion Detection with Decision Tree Ensembles","authors":"Jarul Mehta, Guillaume Richard, Loren Lugosch, Derek Yu, B. Meyer","doi":"10.1145/3566132","DOIUrl":"https://doi.org/10.1145/3566132","url":null,"abstract":"The controller area network (CAN) protocol, used in many modern vehicles for real-time inter-device communications, is known to have cybersecurity vulnerabilities, putting passengers at risk for data exfiltration and control system sabotage. To address this issue, researchers have proposed to utilize security measures based on cryptography and message authentication; unfortunately, such approaches are often too computationally expensive to be deployed in real time on CAN devices. Additionally, they have developed machine learning (ML) techniques to detect anomalies in CAN traffic and thereby prevent attacks. The main disadvantage of existing ML-based techniques is that they either depend on additional computational hardware or they heuristically assume that all communication anomalies are malicious. In this article, we show that tree-based learning ensembles outperform anomaly-based techniques like AutoRegressive Integrated Moving Average (ARIMA) and Z-Score when used to detect attacks that result in increased bus utilization. We evaluated the detection capacity of three tree-based ensembles, Adaboost, gradient boosting, and random forests, and collectively refer to these as DT-DS. We conclude that the decision tree ensemble with Adaboost performs best with an area under curve (AUC) score of 0.999, closely followed by gradient boosting and random forests with 0.997 and 0.991 AUC scores, respectively, when trained using message profiles. We observe that with an increase in the observation window, the DT-DS models present an average AUC score of 0.999, and offer a nearly perfect detection of attacks, at the cost of increased latency in detection of attacked messages. We evaluate the performance of the IDS for Aeronautical Radio, Incorporated– (ARINC) encoded CAN communication traffic in avionic systems, generated using an aerospace testbench, ARINC-825TBv2. The IDS has been evaluated against the active attacks of a state-of-the-art predictive attacker model. Additionally, we observed that the performance of IDS approaches such as ARIMA and Z-Score degrade considerably with a decrease in the size of the observation time window. In contrast, the performance of DT-DS models is consistent, with only an average drop of 0.005 in the AUC score.","PeriodicalId":7055,"journal":{"name":"ACM Transactions on Cyber-Physical Systems","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2023-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47361185","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Today's automotive cyber-physical systems for autonomous driving aim to enhance driving safety by replacing the uncertainties posed by human drivers with standard procedures of automated systems. However, the accuracy of in-vehicle perception systems may significantly vary under different operational conditions (e.g., fog density, light condition, etc.) and consequently degrade the reliability of autonomous driving. A perception system for autonomous driving must be carefully validated with an extremely large dataset collected under all possible operational conditions in order to ensure its robustness. The aforementioned dataset required for validation, however, is expensive or even impossible to acquire in practice, since most operational corners rarely occur in a real-world environment. In this paper, we propose to generate synthetic datasets at a variety of operational corners by using a parameterized cycle-consistent generative adversarial network (PCGAN). The proposed PCGAN is able to learn from an image dataset recorded at real-world operational conditions with only a few samples at corners and synthesize a large dataset at a given operational corner. By taking STOP sign detection as an example, our numerical experiments demonstrate that the proposed approach is able to generate high-quality synthetic datasets to facilitate accurate validation.
{"title":"Data-Driven Parameterized Corner Synthesis for Efficient Validation of Perception Systems for Autonomous Driving","authors":"Handi Yu, Xin Li","doi":"10.1145/3571286","DOIUrl":"https://doi.org/10.1145/3571286","url":null,"abstract":"Today's automotive cyber-physical systems for autonomous driving aim to enhance driving safety by replacing the uncertainties posed by human drivers with standard procedures of automated systems. However, the accuracy of in-vehicle perception systems may significantly vary under different operational conditions (e.g., fog density, light condition, etc.) and consequently degrade the reliability of autonomous driving. A perception system for autonomous driving must be carefully validated with an extremely large dataset collected under all possible operational conditions in order to ensure its robustness. The aforementioned dataset required for validation, however, is expensive or even impossible to acquire in practice, since most operational corners rarely occur in a real-world environment. In this paper, we propose to generate synthetic datasets at a variety of operational corners by using a parameterized cycle-consistent generative adversarial network (PCGAN). The proposed PCGAN is able to learn from an image dataset recorded at real-world operational conditions with only a few samples at corners and synthesize a large dataset at a given operational corner. By taking STOP sign detection as an example, our numerical experiments demonstrate that the proposed approach is able to generate high-quality synthetic datasets to facilitate accurate validation.","PeriodicalId":7055,"journal":{"name":"ACM Transactions on Cyber-Physical Systems","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2023-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42881393","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Anas Alsoliman, Giulio Rigoni, Davide Callegaro, M. Levorato, C. Pinotti, M. Conti
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.
{"title":"Intrusion Detection Framework for Invasive FPV Drones Using Video Streaming Characteristics","authors":"Anas Alsoliman, Giulio Rigoni, Davide Callegaro, M. Levorato, C. Pinotti, M. Conti","doi":"10.1145/3579999","DOIUrl":"https://doi.org/10.1145/3579999","url":null,"abstract":"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.","PeriodicalId":7055,"journal":{"name":"ACM Transactions on Cyber-Physical Systems","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2023-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49235077","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Nitasha Sahani, Ruoxi Zhu, Jinny Cho, Chen-Ching Liu
Machine learning (ML)-based intrusion detection system (IDS) approaches have been significantly applied and advanced the state-of-the-art system security and defense mechanisms. In smart grid computing environments, security threats have been significantly increased as shared networks are commonly used, along with the associated vulnerabilities. However, compared to other network environments, ML-based IDS research in a smart grid is relatively unexplored, although the smart grid environment is facing serious security threats due to its unique environmental vulnerabilities. In this article, we conducted an extensive survey on ML-based IDS in smart grids based on the following key aspects: (1) The applications of the ML-based IDS in transmission and distribution side power components of a smart power grid by addressing its security vulnerabilities; (2) dataset generation process and its usage in applying ML-based IDSs in the smart grid; (3) a wide range of ML-based IDSs used by the surveyed papers in the smart grid environment; (4) metrics, complexity analysis, and evaluation testbeds of the IDSs applied in the smart grid; and (5) lessons learned, insights, and future research directions.
{"title":"Machine Learning-based Intrusion Detection for Smart Grid Computing: A Survey","authors":"Nitasha Sahani, Ruoxi Zhu, Jinny Cho, Chen-Ching Liu","doi":"10.1145/3578366","DOIUrl":"https://doi.org/10.1145/3578366","url":null,"abstract":"Machine learning (ML)-based intrusion detection system (IDS) approaches have been significantly applied and advanced the state-of-the-art system security and defense mechanisms. In smart grid computing environments, security threats have been significantly increased as shared networks are commonly used, along with the associated vulnerabilities. However, compared to other network environments, ML-based IDS research in a smart grid is relatively unexplored, although the smart grid environment is facing serious security threats due to its unique environmental vulnerabilities. In this article, we conducted an extensive survey on ML-based IDS in smart grids based on the following key aspects: (1) The applications of the ML-based IDS in transmission and distribution side power components of a smart power grid by addressing its security vulnerabilities; (2) dataset generation process and its usage in applying ML-based IDSs in the smart grid; (3) a wide range of ML-based IDSs used by the surveyed papers in the smart grid environment; (4) metrics, complexity analysis, and evaluation testbeds of the IDSs applied in the smart grid; and (5) lessons learned, insights, and future research directions.","PeriodicalId":7055,"journal":{"name":"ACM Transactions on Cyber-Physical Systems","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2023-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46531489","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Automotive cyber-physical systems consist of multiple control subsystems working under resource limitations, and the trend is to run the corresponding control tasks on a shared platform. The resource requirements of the tasks are usually variable at runtime due to the uncertainties in the environment, necessitating some kinds of adaptation to deal with the resource limitations. Such adaptations may positively or negatively affect the control performance of several subsystems. Since there might be some thresholds on the control performances as quality constraints, this matter should be considered carefully to avoid any quality attribute constraint violation. This paper proposes a scalable control performance constraint verification method for such a system that works based on a feedback scheduler. The scalability is the result of a control-aware pruning method. In case of a constraint violation, the designer may change the system configuration and perform re-verification. Our evaluations show that the proposed method scales well while preserving the verification soundness.
{"title":"Control Performance Analysis of Automotive Cyber-Physical Systems: A Study on Efficient Formal Verification","authors":"V. Panahi, M. Kargahi, Fathiyeh Faghih","doi":"10.1145/3576046","DOIUrl":"https://doi.org/10.1145/3576046","url":null,"abstract":"Automotive cyber-physical systems consist of multiple control subsystems working under resource limitations, and the trend is to run the corresponding control tasks on a shared platform. The resource requirements of the tasks are usually variable at runtime due to the uncertainties in the environment, necessitating some kinds of adaptation to deal with the resource limitations. Such adaptations may positively or negatively affect the control performance of several subsystems. Since there might be some thresholds on the control performances as quality constraints, this matter should be considered carefully to avoid any quality attribute constraint violation. This paper proposes a scalable control performance constraint verification method for such a system that works based on a feedback scheduler. The scalability is the result of a control-aware pruning method. In case of a constraint violation, the designer may change the system configuration and perform re-verification. Our evaluations show that the proposed method scales well while preserving the verification soundness.","PeriodicalId":7055,"journal":{"name":"ACM Transactions on Cyber-Physical Systems","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2022-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42137204","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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