In this chapter, you will see how the simplex method simplifies when it is applied to a class of optimization problems that are known as “network flow models.” You will also see that if a network flow model has “integer-valued data,” the simplex method finds an optimal solution that is integer-valued.

Flows in Networks

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Detection, Estimation, and Modulation Theory

This paper provides an overview and makes a deep investigation on sampled-data-based event-triggered control and filtering for networked systems. Compared with some existing event-triggered and self-triggered schemes, a sampled-data-based event-triggered scheme can ensure a positive minimum inter-event time and make it possible to jointly design suitable feedback controllers and event-triggered threshold parameters. Thus, more attention has been paid to the sampled-data-based event-triggered scheme. A deep investigation is first made on the sampled-data-based event-triggered scheme. Then, recent results on sampled-data-based event-triggered state feedback control, dynamic output feedback control, $H_\infty$ filtering for networked systems are surveyed and analyzed. An overview on sampled-data-based event-triggered consensus for distributed multiagent systems is given. Finally, some challenging issues are addressed to direct the future research.

An Overview and Deep Investigation on Sampled-Data-Based Event-Triggered Control and Filtering for Networked Systems

The security issue impacting the Internet-of-Things (IoT) paradigm has recently attracted significant attention from the research community. To this end, several surveys were put forward addressing various IoT-centric topics, including intrusion detection systems, threat modeling, and emerging technologies. In contrast, in this paper, we exclusively focus on the ever-evolving IoT vulnerabilities. In this context, we initially provide a comprehensive classification of state-of-the-art surveys, which address various dimensions of the IoT paradigm. This aims at facilitating IoT research endeavors by amalgamating, comparing, and contrasting dispersed research contributions. Subsequently, we provide a unique taxonomy, which sheds the light on IoT vulnerabilities, their attack vectors, impacts on numerous security objectives, attacks which exploit such vulnerabilities, corresponding remediation methodologies and currently offered operational cyber security capabilities to infer and monitor such weaknesses. This aims at providing the reader with a multidimensional research perspective related to IoT vulnerabilities, including their technical details and consequences, which is postulated to be leveraged for remediation objectives. Additionally, motivated by the lack of empirical (and malicious) data related to the IoT paradigm, this paper also presents a first look on Internet-scale IoT exploitations by drawing upon more than 1.2 GB of macroscopic, passive measurements’ data. This aims at practically highlighting the severity of the IoT problem, while providing operational situational awareness capabilities, which undoubtedly would aid in the mitigation task, at large. Insightful findings, inferences and outcomes in addition to open challenges and research problems are also disclosed in this paper, which we hope would pave the way for future research endeavors addressing theoretical and empirical aspects related to the imperative topic of IoT security.

Demystifying IoT Security: An Exhaustive Survey on IoT Vulnerabilities and a First Empirical Look on Internet-Scale IoT Exploitations

Monitoring the “physics” of cyber-physical systems to detect attacks is a growing area of research. In its basic form, a security monitor creates time-series models of sensor readings for an industrial control system and identifies anomalies in these measurements to identify potentially false control commands or false sensor readings. In this article, we review previous work on physics-based anomaly detection based on a unified taxonomy that allows us to identify limitations and unexplored challenges and to propose new solutions.

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A Survey of Physics-Based Attack Detection in Cyber-Physical Systems

Cyberphysical systems (CPSs) refer to the embedding of widespread sensing, networking, computation, and control into physical spaces with the goal of making them safer, more efficient, and reliable. Driven by the miniaturization and integration of sensing, communication, and computation in cost-efficient devices, CPSs are bound to transform industries such as aerospace, transportation, built environments, energy, health care, and manufacturing, to name a few. This great opportunity, unfortunately, is matched by even greater challenges. Taming the complexity of design and analysis of these systems poses a fundamental problem as a new paradigm is needed to bridge various scientific domains, which, through the years, have developed significantly different formalisms and methodologies. In addition, while the use of dedicated communication networks has so far sheltered systems from the outside world, use of off-the-shelf networking and computing, combined with the unattended operation of a plethora of devices, provides several opportunities for malicious entities to inject attacks on CPSs. A wide variety of motivations exists for launching an attack on CPSs, ranging from economic reasons, such as obtaining a financial gain, all the way to terrorism, for instance, threatening an entire population by manipulating life-critical resources. Any attack on safety-critical CPSs may significantly hamper the economy and lead to the loss of human lives. While the threat of attacks on CPSs tends to be underplayed at times, the Stuxnet worm provided a clear example of the possible future to come. This malware, targeting a uranium enriching facility in Iran, managed to reach the supervisory control and data acquisition (SCADA) system controlling the centrifuges used in the enrichment process. Stuxnet modified the control system, increasing pressure in the centrifuges in a first version of the worm and spinning centrifuges in an erratic fashion in a second version. As a result, Stuxnet caused significant damage to the plant [1]. For details, see "The Stuxnet Attack."

Physical Authentication of Control Systems: Designing Watermarked Control Inputs to Detect Counterfeit Sensor Outputs

We propose an open-loop and a closed-loop stochastic event-triggered sensor schedule for remote state estimation. Both schedules overcome the essential difficulties of existing schedules in recent literature works where, through introducing a deterministic event-triggering mechanism, the Gaussian property of the innovation process is destroyed which produces a challenging nonlinear filtering problem that cannot be solved unless approximation techniques are adopted. The proposed stochastic event-triggered sensor schedules eliminate such approximations. Under these two schedules, the minimum mean squared error (MMSE) estimator and its estimation error covariance matrix at the remote estimator are given in a closed-form. The stability in terms of the expected error covariance and the sample path of the error covariance for both schedules is studied. We also formulate and solve an optimization problem to obtain the minimum communication rate under some estimation quality constraint using the open-loop sensor schedule. A numerical comparison between the closed-loop MMSE estimator and a typical approximate MMSE estimator with deterministic event-triggered sensor schedule, in a problem setting of target tracking, shows the superiority of the proposed sensor schedule.

Stochastic event-triggered sensor scheduling for remote state estimation

Maintaining the security of control systems in the presence of integrity attacks is a significant challenge. In literature, several possible attacks against control systems have been formulated including replay, false data injection, and zero dynamics attacks. The detection and prevention of these attacks require the defender to possess a particular subset of trusted communication channels. Alternatively, these attacks can be prevented by keeping the system model secret from the adversary. In this paper, we consider an adversary who has the ability to modify and read all sensor and actuator channels. To thwart this adversary, we introduce external states dependent on the state of the control system, with linear time-varying dynamics unknown to the adversary. We also include sensors to measure these states. The presence of unknown time-varying dynamics is leveraged to detect an adversary who simultaneously aims to identify the system and inject stealthy outputs. Potential attack strategies and bounds on the attacker's performance are provided.

Detecting integrity attacks on control systems using a moving target approach

We propose an open-loop and a closed-loop stochastic event-triggered sensor schedule for remote state estimation. Both schedules overcome the essential difficulties of existing schedules in recent literature works where, through introducing a deterministic event-triggering mechanism, the Gaussian property of the innovation process is destroyed which produces a challenging nonlinear filtering problem that cannot be solved unless approximation techniques are adopted. The proposed stochastic event-triggered sensor schedules eliminate such approximations. Under these two schedules, the MMSE estimator and its estimation error covariance matrix at the remote estimator are given in a closed-form. Simulation studies demonstrate that the proposed schedules have better performance than periodic ones with the same sensor-to-estimator communication rate.

Stochastic Event-triggered Sensor Schedule for Remote State Estimation

Ensuring the security of control systems against integrity attacks is a major challenge. Due to the events of Stuxnet, replay attacks in particular have been considered by the research community. Replaying previous measurements of a system in steady state allows an adversary to generate statistically correct virtual outputs which can bypass traditional detectors. The adversary can then inject destabilizing inputs to cause damage to the plant. The method of injecting secret noisy control inputs, or physical watermarking, has recently been proposed to detect replay attacks. However, the proposed watermarking design methods assume that the adversary does not use his potential access to real time communication channels to create stealthy virtual outputs to send to the defender. In this paper, we formulate an attack model for an adversary who uses knowledge of the system as well as access to a subset of real time control inputs and sensor outputs to construct stealthy virtual outputs. A robust physical watermark and detector to counter such an adversary is proposed.

Sean Weerakkody

Papers

Physical Authentication of Control Systems: Designing Watermarked Control Inputs to Detect Counterfeit Sensor Outputs

Stochastic event-triggered sensor scheduling for remote state estimation

Detecting integrity attacks on control systems using a moving target approach

Stochastic Event-triggered Sensor Schedule for Remote State Estimation

Detecting integrity attacks on control systems using robust physical watermarking