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As DDoS (Distributed Denial of Service) attacks constantly evolve and bombard businesses and organizations from time to time, DDoS mitigation cloud service is a popular solution to defend against DDoS attacks. Decision makers can select which services to deploy given the associated risk and the deployment cost. In this work, we establish a game-theoretic model to simulate the decision making of attackers and defenders under the context of DDoS attacks. We simulate the attacker/defender game under different scenarios and demonstrate that the efficacy of using external services is impacted by several factors including the resources of the organization, the potential damage and the attacker cost/reward. We find that under different scenarios, the Nash Equilibrium may vary drastically from no attack at all to definite attack. Our study can provide useful insights to decision makers and stakeholders on their DDoS defense strategy planning. To the best of our knowledge, this is the first game model to investigate the DDoS attack/defense strategy involving third-party services

This paper presents an open architecture testbed for smart cities, called OpenCity, which is hosted at Virginia Commonwealth University (VCU). The OpenCity platform consists of data collection and processing units, database management, distributed performance management algorithms, and real-time data visualization. This smart city testbed aims to support various educational and research activities related to smart city development. The testbed provides a near-real-life platform to allow students to learn about the unique features of smart cities and explore supporting technologies. In addition, it allows researchers to develop, deploy, and validate new techniques, tools, and technologies to support future smart city developments. The OpenCity platform will support various ongoing important research directions in smart cities, including smart homes and buildings, urban mobility, smart grid, and water management. In addition, it will be extendable to include other potential applications and components as needed. The testbed will be validated by developing and deploying a management system that focuses on users’ experience and resource efficiency. The management system incorporates learning techniques and model-based predictive control approaches to take into account the current and future information of uncertain parameters as well as the subjective data (e.g., user-related data) in the design. The OpenCity management structure enables real-time control and monitoring of complex components in the testbed.

The design of attacks for cyber physical systems is critical to assess CPS resilience at design time and run-time, and to generate rich datasets from testbeds for research. Attacks against cyber physical systems distinguish themselves from IT attacks in that the main objective is to harm the physical system. Therefore, both cyber and physical system knowledge are needed to design such attacks. The current practice to generate attacks either focuses on the cyber part of the system using IT cyber security existing body of knowledge, or uses heuristics to inject attacks that could potentially harm the physical process. In this paper, we present a systematic approach to automatically generate integrity attacks from the CPS safety and control specifications, without knowledge of the physical system or its dynamics. The generated attacks violate the system operational and safety requirements, hence present a genuine test for system resilience. We present an algorithm to automate the malware payload development. Several examples are given throughout the paper to illustrate the proposed approach.

In this paper, we investigate the process of detection of False Data Injection (FDI) in a Linear Parameter Varying (LPV) cyber-physical system (CPS). We design a model based FDI detector capable of detecting false data injections on output measurements and scheduling variables. To improve the detection accuracy of FDI attacks, the attack detector design uses the performance metric H- to maximize the detection capability of the detector module to effectively detect FDI attacks. On the other hand, it uses the H? metric to minimize the effect of disturbance on the detector module given an unreliable network. We assume that the network unreliability comes from packet dropout that we modeled as Bernoulli process. The FDI attack detector is designed such that H- and H? performance metrics are maintained despite packet dropout. Based on stochastic stability, we define a set of sufficient Linear Matrix Inequalities (LMI) that we solve as a multi-objective optimization problem to obtain the detector gain. The obtained detector gain is used for estimating the current system state and current output measurement using the system input, manipulated measurements and manipulated scheduling variables. Then, the output of the detector is compared with the actual sensor measurement. The resulting residual signal carries the information about the FDI attack. The proposed approach is tested and validated on a two-tank system. The evaluation results demonstrate that the proposed detector is able to detect FDI attacks. - 2019 IEEE. This work was made possible by NPRP grant # NPRP9-005-1-002 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors. Scopus

The Continuous Stirred Tank Reactor (CSTR) is an essential process component in a variety of industries. Accurate modeling and simulation of the CSTR is crucial for efficient control and safety system design. In this paper, we present a novel hybrid dynamical model for the CSTR that supports accurate model-based design for CSTR safety and security systems. We first derive the system model then show how both the safety and security systems could be formally designed from the hybrid automaton. Finally, we discuss the generalization of the approach to other cyber physical systems.

The design of wireless sensor networks for detection applications is a challenging task. On one hand, classical work on decentralized detection does not consider practical wireless sensor networks. On the other hand, practical sensor network design approaches that treat the signal processing and communication aspects of the sensor network separately result in sub optimal detection performance because network resources are not allocated efficiently. In this work, we attempt to cross the gap between theoretical decentralized detection work and practical sensor network implementations. We consider a cross-layer approach, where the quality of information, channel state information, and residual energy information are included in the design process of tree-topology sensor networks. The design objective is to specify which sensors should contribute to a given detection task, and to calculate the relevant communication parameters. We compare two design schemes: (1) direct transmission, where raw data are transmitted to the fusion center without compression, and (2) in-network processing, where data is quantized before transmission. For both schemes, we design the optimal transmission control policy that coordinates the communication between sensor nodes and the fusion center. We show the performance improvement for the proposed design schemes over the classical decoupled and maximum throughput design approaches.

A Wireless Sensor Network (WSN) deployed for detection applications has the distinguishing feature that sensors cooperate to perform the detection task. Therefore, the decoupled design approach typically used to design communication networks, where each network layer is designed independently, does not lead to the desired optimal detection performance. Recent work on decentralized detection has addressed the design of MAC and routing protocols for detection applications by considering independently the Quality of Information (QoI), Channel State Information (CSI), and Residual Energy Information (REI) for each sensor. However, little attention has been given to integrate the three quality measures (QoI, CSI, REI) in the complete system design. In this work, we pursue a cross-layer approach to design a QoI, CSI, and REI-aware Transmission Control Policy (TCP) that coordinates communication between local sensors and the fusion center, in order to maximize the detection performance. We formulate and solve a constrained nonlinear optimization problem to find the optimal TCP design variables. We compare our design with the decoupled approach, where each layer is designed separately, in terms of the delay for detection and WSN lifetime.

Passive wireless sensors have emerged as a new technology to measure a vast majority of phenomena in our daily life. Passive sensors require no power source, and therefore their application domains are numerous, including health care, infrastructure protection, and national security, among many others. The deployment of wireless passive sensors and their readers has changed how detection needs to be performed. Passive sensors cannot pre-process the measurements as they have limited computational power. Therefore, no local decision is taken. Also, the reader polls the information from multiple sensors at the same time, and this causes collisions and hence packet drops and delays. In this paper, we formulate the detection performance, with non-ideal channels, in a probabilistic way, and compare with classical detection performance. We design an optimal adaptive Neyman-Pearson detector, given the channel probabilistic model, by formulating and solving a constrained optimization problem.

The decentralized detection performance, using wireless passive sensor networks, is analyzed according to the minimum probability of error criterion. Passive sensors communicate their measurements to the reader using data network packets, and therefore, the two main phenomena affecting the detection performance are packet loss and packet delay. In this paper, we formulate the decentralized detection problem with passive sensors and show that the optimal decision rule with packet loss is the likelihood ratio test. We present a comparative analysis study between detection with ideal and non-ideal channels, for the problem of DC level detection in White Gaussian Noise. We validate the analytical results using Monte Carlo Simulation study. Finally, we present a simple scheme for adaptive detector design, to restore the original detection performance, with the cost of increasing the delay for detection.

Passive wireless sensors have emerged as a new technology to measure multiple phenomena in our daily life. Passive sensors require no power source, and therefore their application domains are numerous, including health care, infrastructure protection, and national security. The deployment of passive wireless sensors and their readers has changed how detection needs to be performed. Passive sensors cannot pre-process the measurements as they have limited computational power. Therefore, no local decision is taken. Also, the reader polls the information from multiple sensors at the same time, and this causes collisions and hence packet drops and delays. Detectors designed without considering the properties of the communication channel have degraded performance. Therefore, analysis is required to quantify the degradation and take the necessary remedy action. In this paper, we study the effect of sensor-reader channel imperfection on the local detection performance of the reader, assuming no data pre-processing at the passive sensor.We consider the case of a single sensor-reader communication over a Bernoulli communication channel. We formulate the detector performance and compare with the ideal case. We present the problem of DC level detection in White Gaussian Noise, as a case study. Finally, we propose a heuristic approach to restore the original detector performance working with non-ideal channel, with the cost of increasing the delay for detection.

Integrated drive generators (IDGs) are the main source of electrical power for a number of critical systems in aircraft. Fast and accurate fault detection and isolation is a necessary component for safe and reliable operation of the IDG and the aircraft. IDGs are complex systems, and a majority of the existing fault detection and isolation techniques are based on signal analysis and heuristic methods derived from experience. Model-based fault diagnosis techniques are hypothesized to be more general and powerful in designing detection and isolation schemes, but building sufficiently accurate models of complex IDGs is a difficult task. dq0 models have been developed for design and control of generators, but these models are not suitable for fault situations, where the generator may become unbalanced. In this paper, we present a hybrid phase-domain model for the aircraft generator that accurately represents both nominal and parametric faulty behaviors. We present the details of the hybrid modeling approach and simulation results of nominal operation and fault behaviors associated with parametric faults in the aircraft generator. The simulation results show that the developed model is capable of accurately capturing the generator dynamics under a variety of normal and faulty configurations.