Article
Automation & Control Systems
Weitao Yao, Yu Wang, Yan Xu, Chao Deng
Summary: This article investigates the stability issue of microgrid systems with distributed secondary control under latency attacks and random denial-of-service attacks. It proposes a cyber-resilient control strategy with two control modes to sustain the stability and control functions of the systems under different attack scenarios. Experimental tests on a modified IEEE 13-bus microgrid system verify the effectiveness of the proposed controller.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Automation & Control Systems
Qing Geng, Hongjiu Yang, Li Li, Changchun Hua
Summary: This article focuses on the hybrid dynamic event-triggered security tracking control design for wheeled mobile robots (WMRs) against round-trip stochastic denial of service (DoS) attacks. The DoS attacks are modeled as Markov switched processes to capture their random properties. A hybrid dynamic model is established and a hybrid dynamic event-triggering mechanism (ETM) is proposed for the WMR, which achieves larger triggering intervals compared to static triggering. To actively defend against DoS attacks and achieve trajectory tracking control, a hybrid dynamic controller with a dynamic parameter reflecting the real-time DoS attacks is designed. The uniformly globally asymptotically stability (UGAS) condition of the addressed hybrid dynamic WMR system is obtained. Practical experiments are conducted to validate the provided theoretical results.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Computer Science, Information Systems
Mahmood A. A. Al-Shareeda, Selvakumar Manickam
Summary: Traffic safety and efficiency are crucial in both private and public transportation. The use of 5G-enabled vehicular networks allows vehicles to share information wirelessly, aiding drivers and passengers. However, privacy and security are challenging issues in these networks. This paper proposes the MSR-DoS scheme, which effectively resists DoS attacks and meets various privacy and security requirements in 5G-enabled vehicular networks. Performance analysis shows that the scheme has lower communication and computational costs compared to existing works while reducing computation overhead significantly.
Article
Automation & Control Systems
Zhen Han, Jiang Long, Wei Wang, Lei Wang
Summary: This paper addresses the tracking control problem for a two-wheeled mobile robot with unknown parameters in a networked positioning system subjected to Denial-of-Service attacks. The proposed method utilizes parameter estimators and an adaptive tracking controller to handle uncertainties and mitigate the impact of attacks on system performance. Experimental results demonstrate the effectiveness of the proposed control scheme.
Article
Automation & Control Systems
Rui Kato, Ahmet Cetinkaya, Hideaki Ishii
Summary: This article studies the stabilization problem of networked control systems under DoS attacks and proposes a dynamic quantizer for achieving asymptotic stabilization. The estimation of the region of attraction is also investigated.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2022)
Article
Automation & Control Systems
Xiaobin Gao, Feiqi Deng, Pengyu Zeng, Xiaohua Liu
Summary: This article investigates the stability issue of stochastic hybrid systems against energy-constrained denial-of-service (DoS) attacks. It introduces a sampled-data-based Round-Robin protocol to avoid communication network congestion. A new switched time-delay stochastic closed-loop system with an unstable subsystem is established by discussing the effect of DoS attacks. The stability of the closed-loop system is then analyzed using the piecewise Lyapunov-Krasovskii functional method and stochastic analysis technique. The proposed method is validated through two examples, demonstrating its correctness and applicability.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2022)
Article
Engineering, Electrical & Electronic
Yifang Zhang, Zheng-Guang Wu
Summary: This brief discusses the secure asynchronous control problem of Markov jump systems (MJSs) against aperiodic discrete denial-of-service (DoS) attacks. DoS attacks can cut off communication networks, resulting in the loss of state information. The impact of DoS attacks can cause mismatched behavior between controlled plant modes and controller modes, which can be described by a hidden Markov model (HMM). An asynchronous controller is used to overcome this mismatched phenomenon. Sufficient conditions are derived to guarantee mean square stability of MJSs under aperiodic DoS attacks by employing multiple Lyapunov functions and iterative methods, and analyzing the conditions on malicious DoS attacks. An asynchronous controller is designed by solving a set of linear matrix inequalities (LMIs). The effectiveness of the proposed theoretical method is validated through numerical simulation.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS
(2023)
Article
Automation & Control Systems
Lili Zhang, Wei-Wei Che, Chao Deng, Zheng-Guang Wu
Summary: This article investigates the prescribed performance security control problem for nonlinear systems subject to denial-of-service attacks. A security tracking control method is proposed using an attack compensator and a fuzzy estimator to steer the tracking errors to a predetermined neighborhood within a predefined settling time.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Automation & Control Systems
Fang Fang, Jiayu Li, Yajuan Liu, Ju H. Park
Summary: In this article, a resilient distributed sampled-data control scheme is proposed for multiagent systems. The scheme introduces novel logic processors to obtain information on DoS attacks and develops resilient distributed controllers using derived criteria. Two examples are provided to demonstrate the efficiency of the proposed scheme.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Computer Science, Information Systems
Jing-Jing Yan, Guang-Hong Yang
Summary: This paper addresses the fault estimation problem for cyber-physical systems modeled by interconnected systems. A novel switching-type FE scheme is proposed to improve estimation accuracy, with a quantitative description of estimation error divergence rate under attack status. The interconnections among subsystems are fully considered to enhance estimation accuracy, and the mean sequence of estimate errors converges to zero in the disturbance-free case.
INFORMATION SCIENCES
(2021)
Article
Engineering, Aerospace
Ying Zhang, Lei Ma, Chunyu Yang, Linna Zhou, Guoqing Wang, Wei Dai
Summary: This article investigates distributed formation control for quadrotor groups vulnerable to denial-of-service (DoS) attacks. The DoS attacks disrupt the formation mission by preventing information interaction between adjacent quadrotors on individual communication channels. A decomposition-combination control framework is proposed, partitioning the high-order dynamic system into slow and fast subsystems to reduce computing load and resolve under-driven issues. The composition formation control scheme includes a resilient slow-scale controller to address DoS attacks and an optimal fast-scale controller to ensure flying performance and stability. The proposed method effectively utilizes position and velocity information, acquired and processed entirely by subcontrollers, for more efficient system execution. A slow-fast composition mechanism is provided and simulation results validate the suggested methods using a system of four quadrotors.
IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS
(2023)
Article
Automation & Control Systems
Songlin Hu, Fuyi Yang, Sergey Gorbachev, Dong Yue, Victor Kuzin, Chao Deng
Summary: This paper investigates the design of a resilient current controller for a networked DC microgrid system with multiple constant power loads (CPLs) in the presence of a new type of time-constrained denial-of-service (DoS) attack. Unlike existing DoS attack models, which consider DoS frequency and duration, this paper focuses on the duration characteristics of sporadic/aperiodic DoS attacks and proposes a new time-constrained DoS attack model. Under the impact of such attacks, a switching state feedback control law is formulated and a switching-like DC microgrid system model is established. Moreover, leveraging an attack-parameter-dependent time-varying Lyapunov function (TVLF) method, the exponential stability criterion of the resulting DC microgrid system under aperiodic DoS attacks is derived, and a new resilient controller design method is introduced. Simulation studies are conducted to validate the effectiveness and advantages of the proposed resilient control design scheme in terms of achieving desired control performance and attack resilience.
Article
Automation & Control Systems
Xiaodan Zhang, Feng Xiao, Bo Wei, Mei Yu, Kaien Liu
Summary: This article investigates the resilient control for networked control systems in the presence of denial-of-service (DoS) attacks using a sampled-data and dynamic quantization scheme. A novel dynamic quantization strategy is designed for signal transmissions in the presence of DoS attacks. An estimator is introduced to design control laws and sufficient conditions are provided for the asymptotic stability of the system. An event-triggered communication scheme is also designed to reduce network resource consumption.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2023)
Article
Automation & Control Systems
Yang Tang, Dandan Zhang, Peng Shi, Wenbing Zhang, Feng Qian
Summary: This article discusses the formation control problem of nonlinear multiagent systems under denial-of-service attacks, proposing a distributed hybrid event-triggering strategy to preserve formation control. Theoretical results are verified using a benchmark problem of six miniature quadrotor prototypes.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2021)
Article
Computer Science, Information Systems
Shengli Du, Yuee Wang, Lijing Dong, Xiaoli Li
Summary: This paper investigates the secure consensus problem of multiagent systems under switching topologies, affected by both DoS attacks and external disturbances. By developing graph-based Lyapunov functions and stabilization controllers based on solutions to Lyapunov equations and an algebraic Riccati equation, the expected system performance is achieved. Simulation results validate the feasibility of the proposed scheme.
INFORMATION SCIENCES
(2021)
Article
Chemistry, Analytical
Ivan Vaccari, Vanessa Orani, Alessia Paglialonga, Enrico Cambiaso, Maurizio Mongelli
Summary: The application of machine learning and artificial intelligence in the medical field is expanding, with a focus on remote monitoring and data augmentation for accurate algorithms. By using generative adversarial networks (GANs) to create synthetic datasets, and validating them through machine learning approaches, the accuracy of the synthetic data can be demonstrated.
Article
Chemistry, Analytical
Syed Ghazanfar Abbas, Ivan Vaccari, Faisal Hussain, Shahzaib Zahid, Ubaid Ullah Fayyaz, Ghalib A. Shah, Taimur Bakhshi, Enrico Cambiaso
Summary: The Internet of Things (IoT) allows objects to connect to the Internet for meaningful purposes, but also faces increasing security threats, particularly phishing attacks targeting IoT devices. This paper proposes a threat modelling approach to identify and mitigate cyber-threats that may cause phishing attacks, focusing on smart autonomous vehicular systems and smart homes as significant IoT use cases. The proposed approach aims to support IoT researchers, engineers, and policymakers in securing IoT devices and systems during the early design stages for secure deployment in critical infrastructures.
Article
Chemistry, Analytical
Muhammad Husnain, Khizar Hayat, Enrico Cambiaso, Ubaid U. Fayyaz, Maurizio Mongelli, Habiba Akram, Syed Ghazanfar Abbas, Ghalib A. Shah
Summary: In this paper, a MQTT parsing engine is designed and developed to serve as an initial layer in network-based IDS for extensive checking of IoT protocol vulnerabilities and improper usage. By rigorously validating packet fields, the proposed solution effectively detects and prevents the exploitation of vulnerabilities on IoT protocols.
Article
Biochemical Research Methods
Ilaria Pulsoni, Markus Lubda, Maurizio Aiello, Arianna Fedi, Monica Marzagalli, Joerg von Hagen, Silvia Scaglione
Summary: In vitro diffusive models are important for evaluating the penetration ability of active ingredients in different formulations. This study compared the Franz Diffusion Cell with a novel fluid-dynamic platform and assessed the penetration ability of caffeine and LIP1. The results showed similar penetration kinetics in both diffusive systems and the fluid-dynamic platform showed better prediction for lipophilic molecules.
Article
Biotechnology & Applied Microbiology
Monica Marzagalli, Giorgia Pelizzoni, Arianna Fedi, Chiara Vitale, Fabrizio Fontana, Silvia Bruno, Alessandro Poggi, Alessandra Dondero, Maurizio Aiello, Roberta Castriconi, Cristina Bottino, Silvia Scaglione
Summary: The success of immunotherapeutic approaches depends on the interaction between immune cells and cancer cells. Conventional cell cultures and animal models cannot fully represent the complexity of the tumor microenvironment and the human immune system. Therefore, it is crucial to develop reliable and predictive preclinical models for screening immunotherapeutic approaches. This study presents an organ-on-chip (OOC)-based approach that can mimic the migration of natural killer (NK) cells, infiltration into a 3D tumor matrix, and activation against neuroblastoma cancer cells in a fluid-dynamic environment. The proposed immune-tumor OOC-based model shows promise in faithfully replicating human pathology and effectively testing immunotherapies, potentially in a personalized perspective.
FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY
(2022)
Article
Computer Science, Artificial Intelligence
Sara Narteni, Vanessa Orani, Ivan Vaccari, Enrico Cambiaso, Maurizio Mongelli
Summary: Nowadays, artificial intelligence is rapidly developing in many fields, leading to the emergence of reliable AI that ensures the safety of autonomous decisions. Sensitivity analysis of explainable AI models can help design safety regions in the feature space with statistical zero error.
IEEE INTELLIGENT SYSTEMS
(2022)
Proceedings Paper
Computer Science, Information Systems
Ivan Vaccari, Alberto Carlevaro, Sara Narteni, Enrico Cambiaso, Maurizio Mongelli
Summary: Adversarial machine learning manipulates datasets to deceive machine learning algorithm decisions. In this study, a new approach based on eXplainable and Reliable AI is proposed to detect adversarial attacks. Experimental results demonstrate that canonical algorithms may struggle to identify attacks, while the proposed approach is capable of accurately identifying different adversarial settings.
IEEE INFOCOM 2022 - IEEE CONFERENCE ON COMPUTER COMMUNICATIONS WORKSHOPS (INFOCOM WKSHPS)
(2022)
Article
Computer Science, Software Engineering
Enrico Cambiaso, Maurizio Aiello
Summary: In this paper, the authors discuss the impact of legitimate data dumping activities, specifically scraping/storing data shown on browsers, in the web security field. They propose Cookidump as a tool to evaluate the dumping of all available recipes on the Cookidoo (c) website portal. Although the focus is on recipe dumping, the authors also discuss the potential impact of such activities for other web applications hosting sensitive information.
Article
Computer Science, Information Systems
Ivan Vaccari, Alberto Carlevaro, Sara Narteni, Enrico Cambiaso, Maurizio Mongelli
Summary: This article discusses the wide adoption of machine learning algorithms and the concept of adversarial machine learning attacks. The research proposes new approaches to detect and mitigate these attacks and compares their performance to traditional algorithms.
Article
Computer Science, Information Systems
Sara Narteni, Vanessa Orani, Enrico Cambiaso, Matteo Rucco, Maurizio Mongelli
Summary: In this study, the use of eXplainable AI (XAI) in detecting physical fatigue during manual material handling task simulation is explored. Global rule-based XAI models (LLM and DT) are compared to black-box models (NN, SVM, XGBoost) in terms of performance, and global models are also compared to local ones (LIME over XGBoost). Surprisingly, both global and local approaches yield similar conclusions in terms of feature importance.
Proceedings Paper
Computer Science, Information Systems
Umberto Morelli, Ivan Vaccari, Silvio Ranise, Enrico Cambiaso
Summary: The Internet of Things is a widely adopted and pervasive technology that is also vulnerable to attacks due to the volume of shared data and the availability of insecure products. This paper investigates two denial of service attacks targeting MQTT message queues, demonstrating their effectiveness and proposing mitigations in open-source MQTT implementations. The research results are integrated into the MQTTSA tool to improve security awareness in MQTT-based deployments.
ARES 2021: 16TH INTERNATIONAL CONFERENCE ON AVAILABILITY, RELIABILITY AND SECURITY
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
Sara Narteni, Melissa Ferretti, Vanessa Orani, Ivan Vaccari, Enrico Cambiaso, Maurizio Mongelli
Summary: This study proposes a solution based on eXplainable AI models to define safety regions in the feature space, aiming to reduce false negatives. The results show that the effectiveness of the algorithms strongly depends on the level of noise in the dataset.
MACHINE LEARNING AND KNOWLEDGE EXTRACTION (CD-MAKE 2021)
(2021)
Article
Computer Science, Information Systems
Ivan Vaccari, Sara Narteni, Maurizio Aiello, Maurizio Mongelli, Enrico Cambiaso
Summary: The Internet of Things is a widely adopted technology that is also highly relevant in cybersecurity due to the volume and sensitivity of data shared and the availability of affordable but insecure products. This paper proposes a novel cyber threat using the MQTT protocol for tunneling attacks in IoT networks, which could be used by malicious users to steal sensitive information. Experimental results show that using MQTT for tunneling purposes is effective, especially for payloads up to 3000 bytes. Additionally, the study presents a machine learning-based approach to detect the proposed MQTT tunneling attack, with some algorithms achieving over 95% accuracy in identifying the attack.
