A SCADA system employing the distributed networks of sensors and actuators that interact with the physical environment is vulnerable to attacks that target the interface between the cyber and physical subsystems. An attack that hijacks the sensors in an attempt to provide false readings to the controller (for example, the Stuxnet worm that targeted Iran’s nuclear centrifuges) can be used to feign normal system operation for the control system, while the attacker can hijack the actuators to send the system beyond its safety range. This thesis extends the results of a previously proposed method. The original method proposed addition of a randomized “watermarking” signal and checking for the presence of this signal and its effects in the received sensor measurements. Since the control inputs traverse the cyberphysical boundary and make their effects apparent in the sensor measurements, they are employed to carry this watermarking signal through to the system and back to the SCADA controller. The sensor measurements are compared to the expected measurements (calculated using a suitably delayed model of the system within the controller). This methodology is based on using the statistics of the linear system and its controller. The inclusion of a randomized signal on the control inputs induces an increase in the performance cost of the physical system. This thesis proposes a method of optimization of the watermarking signal based on the trade-off between this performance cost and the attack detection rate, by leveraging the distribution the watermarking signal over multiple inputs and multiple outputs. It is further proved that regardless of the number of inputs and outputs in the system, only one watermarking signal needs to be generated. This optimization, and its necessity in improving the effectiveness of the detector without detriment to the performance cost, are demonstrated on a simulated chemical plant. The thesis also proposes another methodology that does not rely on these statistics, but is instead based on calculating the correlation between the received sensor measurements and the expected measurements accrued from the model inside the controller. Generalizing the form of attack even further to attacks that target the integrity of the data sent to the actuators and received from the sensors, this thesis demonstrates the effect of such integrity attack on electricity market operations, where the attacker successfully uses a vulnerability in the Global Position System to break synchronicity among dispersed phasor measurements to gain a competitive advantage over other bidders in the electricity market. In an effort to make state estimation robust against integrity attacks, the sensors and states are modeled as binary variables. Sensor networks use binary measurements and state estimations for several reasons, including communication and processing overheads. Such a state estimator is vulnerable to attackers that can hijack a subset of the sensors in an effort to change the state estimate. This thesis proposes a method for designing the estimators using the concept of invariant sets. This methodology relies on identifying the sets of measurement vectors for which no amount of manipulation by the attacker can change estimate, and maximizing the probability that the sensor measurement vector lies in this set. Although the problem of finding the best possible invariant sets for a general set of sensors has double-exponential complexity, by using some simplifications on the types of sensors, this can be reduced significantly. For the problem that employs all sensors of the same type, this method reduces to a linear search. For sensors that can be classified into two types, this complexity reduces to a search over a two-dimensional space, which is still tractable. Further increase in the confidence of the estimate can be achieved by considering the correlation between the sensor measurements.

Construction projects require large amounts of cyber-information, such as 3D models, in order to achieve success. Unfortunately, this information is typically difficult for construction field personnel to access and use on-site, due to the highly mobile nature of the job and hazardous work environments. Field personnel rely on carrying around large stacks of construction drawings, diagrams, and specifications, or traveling to a trailer to look up information electronically, reducing potential project efficiency. This thesis details my work on Hybrid 4-Dimensional Augmented Reality, known as HD4AR, a mobile augmented reality system for construction projects that provides high-precision visualization of semantically-rich 3D cyber-information over real-world imagery. The thesis examines the challenges related to augmenting reality on a construction site, describes how HD4AR overcomes these challenges, and empirically evaluates the capabilities of HD4AR. / Master of Science

<p> In computer vision, accurately detecting objects of varying sizes is essential for various applications, such as autonomous vehicle navigation and medical imaging diagnostics. Addressing the variance in object sizes presents a significant challenge requiring advanced computational solutions for reliable object recognition and processing. This research introduces a size-adaptive Convolutional Neural Network (CNN) framework to enhance detection performance across different object sizes. By dynamically adjusting the CNN’s configuration based on the observed distribution of object sizes, the framework employs statistical analysis and algorithmic decision-making to improve detection capabilities. Further innovation is presented through the Parameterized-Swish activation function. Distinguished by its dynamic parameters, this function is designed to better adapt to varying input patterns. It exceeds the performance of traditional activation functions by enabling faster model convergence and increasing detection accuracy, showcasing the effectiveness of adaptive activation functions in enhancing object detection systems. The implementation of this model has led to notable performance improvements: a 11.4% increase in mean Average Precision (mAP) and a 40.63% increase in frames per second (FPS) for small objects, demonstrating enhanced detection speed and accuracy. The model has achieved a 48.42% reduction in training time for medium-sized objects while still improving mAP, indicating significant efficiency gains without compromising precision. Large objects have seen a 16.9% reduction in training time and a 76.04% increase in inference speed, showcasing the model’s ability to expedite processing times substantially. Collectively, these advancements contribute to a more than 12% increase in detection efficiency and accuracy across various scenarios, highlighting the model’s robustness and adaptability in addressing the critical challenge of size variance in object detection. </p>

Sensors are widely deployed in our environment, and their number is increasing rapidly. In the near future, billions of devices will all be connected to each other, creating an Internet of Things. Furthermore, computational intelligence is needed to make applications involving these devices truly exciting. In IoT, however, the vast amounts of data will not be statically prepared for batch processing, but rather continually produced and streamed live to data consumers and intelligent algorithms. We refer to applications that perform live analysis on live data streams, bringing intelligence to IoT, as the Analysis of Things. However, the Analysis of Things also comes with a new set of challenges. The data sources are not collected in a single, centralized location, but rather distributed widely across the environment. AoT applications need to be able to access (consume, produce, and share with each other) this data in a way that is natural considering its live streaming nature. The data transport mechanism must also allow easy access to sensors, actuators, and analysis results. Furthermore, analysis applications require computational resources on which to run. We claim that system support for AoT can reduce the complexity of developing and executing such applications. To address this, we make the following contributions: - A framework for systems support of Live Streaming Analysis in the Internet of Things, which we refer to as the Analysis of Things (AoT), including a set of requirements for system design - A system implementation that validates the framework by supporting Analysis of Things applications at a local scale, and a design for a federated system that supports AoT on a wide geographical scale - An empirical system evaluation that validates the system design and implementation, including simulation experiments across a wide-area distributed system We present five broad requirements for the Analysis of Things and discuss one set of specific system support features that can satisfy these requirements. We have implemented a system, called \textsubscript{SS}IoTa, that implements these features and supports AoT applications running on local resources. The programming model for the system allows applications to be specified simply as operator graphs, by connecting operator inputs to operator outputs and sensor streams. Operators are code components that run arbitrary continuous analysis algorithms on streaming data. By conforming to a provided interface, operators may be developed that can be composed into operator graphs and executed by the system. The system consists of an Execution Environment, in which a Resource Manager manages the available computational resources and the applications running on them, a Stream Registry, in which available data streams can be registered so that they may be discovered and used by applications, and an Operator Store, which serves as a repository for operator code so that components can be shared and reused. Experimental results for the system implementation validate its performance. Many applications are also widely distributed across a geographic area. To support such applications, \textsubscript{SS}IoTa must be able to run them on infrastructure resources that are also distributed widely. We have designed a system that does so by federating each of the three system components: Operator Store, Stream Registry, and Resource Manager. The Operator Store is distributed using a distributed hast table (DHT), however since temporal locality can be expected and data churn is low, caching may be employed to further improve performance. Since sensors exist at particular locations in physical space, queries on the Stream Registry will be based on location. We also introduce the concept of geographical locality. Therefore, range queries in two dimensions must be supported by the federated Stream Registry, while taking advantage of geographical locality for improved average-case performance. To accomplish these goals, we present a design sketch for SkipCAN, a modification of the SkipNet and Content Addressable Network DHTs. Finally, the fundamental issue in the federated Resource Manager is how to distributed the operators of multiple applications across the geographically distributed sites where computational resources can execute them. To address this, we introduce DistAl, a fully distributed algorithm that assigns operators to sites. DistAl also respects the system resource constraints and application preferences for performance and quality of results (QoR), using application-specific utility functions to allow applications to express their preferences. DistAl is validated by simulation results.

In economical and sustainable operation of cyber-physical systems, a number of entities need to often cooperate over a communication network to solve optimization problems. A challenging aspect in the design of robust distributed solution algorithms to these optimization problems is that as technology advances and the networks grow larger, the communication bandwidth used to coordinate the solution is limited. Moreover, even though most research has focused distributed convex optimization, in cyberphysical systems nonconvex problems are often encountered, e.g., localization in wireless sensor networks and optimal power flow in smart grids, the solution of which poses major technical difficulties. Motivated by these challenges this thesis investigates distributed optimization with emphasis on limited communication for both convex and nonconvex structured problems. In particular, the thesis consists of four articles as summarized below. The first two papers investigate the convergence of distributed gradient solution methods for the resource allocation optimization problem, where gradient information is communicated at every iteration, using limited communication. In particular, the first paper investigates how distributed dual descent methods can perform demand-response in power networks by using one-way communication. To achieve the one-way communication, the power supplier first broadcasts a coordination signal to the users and then updates the coordination signal by using physical measurements related to the aggregated power usage. Since the users do not communicate back to the supplier, but instead they only take a measurable action, it is essential that the algorithm remains primal feasible at every iteration to avoid blackouts. The paper demonstrates how such blackouts can be avoided by appropriately choosing the algorithm parameters. Moreover, the convergence rate of the algorithm is investigated. The second paper builds on the work of the first paper and considers more general resource allocation problem with multiple resources. In particular, a general class of quantized gradient methods are studied where the gradient direction is approximated by a finite quantization set. Necessary and sufficient conditions on the quantization set are provided to guarantee the ability of these methods to solve a large class of dual problems. A lower bound on the cardinality of the quantization set is provided, along with specific examples of minimal quantizations. Furthermore, convergence rate results are established that connect the fineness of the quantization and number of iterations needed to reach a predefined solution accuracy. The results provide a bound on the number of bits needed to achieve the desired accuracy of the optimal solution. The third paper investigates a particular nonconvex resource allocation problem, the Optimal Power Flow (OPF) problem, which is of central importance in the operation of power networks. An efficient novel method to address the general nonconvex OPF problem is investigated, which is based on the Alternating Direction Method of Multipliers (ADMM) combined with sequential convex approximations. The global OPF problem is decomposed into smaller problems associated to each bus of the network, the solutions of which are coordinated via a light communication protocol. Therefore, the proposed method is highly scalable. The convergence properties of the proposed algorithm are mathematically and numerically substantiated. The fourth paper builds on the third paper and investigates the convergence of distributed algorithms as in the third paper but for more general nonconvex optimization problems. In particular, two distributed solution methods, including ADMM, that combine the fast convergence properties of augmented Lagrangian-based methods with the separability properties of alternating optimization are investigated. The convergence properties of these methods are investigated and sufficient conditions under which the algorithms asymptotically reache the first order necessary conditions for optimality are established. Finally, the results are numerically illustrated on a nonconvex localization problem in wireless sensor networks. The results of this thesis advocate the promising convergence behaviour of some distributed optimization algorithms on nonconvex problems. Moreover, the results demonstrate the potential of solving convex distributed resource allocation problems using very limited communication bandwidth. Future work will consider how even more general convex and nonconvex problems can be solved using limited communication bandwidth and also study lower bounds on the bandwidth needed to solve general resource allocation optimization problems. / <p>QC 20160203</p>

No description available.

The emerging technology of Cyberphysical systems consists of networked computing, sensing, and actuator devices used to monitor, connect, and control physical phenomena. In order to economically and sustainably operate Cyberphysical systems, their devices need to cooperate over a communication network to solve optimization problems. For example, in smart power grids, smart meters cooperatively optimize the grid performance, and in wireless sensor networks a number of sensors cooperate to find optimal estimators of real-world parameters. A challenging aspect in the design of distributed solution algorithms to these optimization problems is that while the technology advances and the networks grow larger, the communication bandwidth available to coordinate the solution remains limited. Motivated by this challenge, this thesis investigates the convergence of distributed solution methods for resource allocation optimization problems, where gradient information is communicated at every iteration, using limited communication. This problem is approached from three different perspectives, each presented in a separate paper.  The investigation of the three papers demonstrate promises and limits of solving distributed resource allocation problems using limited communication bandwidth. Future work will consider how even more general problems can be solved using limited communication bandwidth and also study different communication constraints. / <p>QC 20170424</p>

<p>The progressive integration of microcontrollers into various domains has transformed traditional mechanical systems into modern cyber-physical systems. However, the beginning of this transformation predated the era of hyper-interconnectedness that characterizes our contemporary world. As such, the principles and visions guiding the design choices of this transformation had not accounted for many of today's security challenges. Many designers had envisioned their systems to operate in an air-gapped-like fashion where few security threats loom. However, with the hyper-connectivity of today's world, many CPS find themselves in uncharted territory for which they are unprepared.</p> <p><br></p> <p>An example of this evolution is the Controller Area Network (CAN). CAN emerged during the transformation of many mechanical systems into cyber-physical systems as a pivotal communication standard, reducing vehicle wiring and enabling efficient data exchange. CAN's features, including noise resistance, decentralization, error handling, and fault confinement mechanisms, made it a widely adopted communication medium not only in transportation but also in diverse applications such as factories, elevators, medical equipment, avionic systems, and naval applications.</p> <p><br></p> <p>The increasing connectivity of modern vehicles through CD players, USB sticks, Bluetooth, and WiFi access has exposed CAN systems to unprecedented security challenges and highlighted the need to bolster their security posture. This dissertation addresses the urgent need to enhance the security of modern cyber-physical systems in the face of emerging threats by proposing a proactive vulnerability identification and defense construction approach and applying it to CAN as a lucid case study. By adopting this proactive approach, vulnerabilities can be systematically identified, and robust defense mechanisms can be constructed to safeguard the resilience of CAN systems.</p> <p><br></p> <p>We focus on developing vulnerability scanning techniques and innovative defense system designs tailored for CAN systems. By systematically identifying vulnerabilities before they are discovered and exploited by external actors, we minimize the risks associated with cyber-attacks, ensuring the longevity and reliability of CAN systems. Furthermore, the defense mechanisms proposed in this research overcome the limitations of existing solutions, providing holistic protection against CAN threats while considering its performance requirements and operational conditions.</p> <p><br></p> <p>It is important to emphasize that while this dissertation focuses on CAN, the techniques and rationale used here could be replicated to secure other cyber-physical systems. Specifically, due to CAN's presence in many cyber-physical systems, it shares many performance and security challenges with those systems, which makes most of the techniques and approaches used here easily transferrable to them. By accentuating the importance of proactive security, this research endeavors to establish a foundational approach to cyber-physical systems security and resiliency. It recognizes the evolving nature of cyber-physical systems and the specific security challenges facing each system in today's hyper-connected world and hence focuses on a single case study. </p>

<p dir="ltr">In the contemporary digital era, Advanced Persistent Threat (APT) attacks are evolving, becoming increasingly sophisticated, and now perilously targeting critical cyber-physical systems, notably Industrial Control Systems (ICS). The intersection of digital and physical realms in these systems enables APT attacks on ICSs to potentially inflict physical damage, disrupt critical infrastructure, and jeopardize human safety, thereby posing severe consequences for our interconnected world. Provenance tracing techniques are essential for investigating these attacks, yet existing APT attack forensics approaches grapple with scalability and maintainability issues. These approaches often hinge on system- or application-level logging, incurring high space and run-time overheads and potentially encountering difficulties in accessing source code. Their dependency on heuristics and manual rules necessitates perpetual updates by domain-knowledge experts to counteract newly developed attacks. Additionally, while there have been efforts to verify the safety of Programming Logic Controller (PLC) code as adversaries increasingly target industrial environments, these works either exclusively consider PLC program code without connecting to the underlying physical process or only address time-related physical safety issues neglecting other vital physical features.</p><p dir="ltr">This dissertation introduces two novel frameworks, ATLAS and ARCHPLC, to address the aforementioned challenges, offering a synergistic approach to fortifying cybersecurity in the face of evolving APT and ICS threats. ATLAS, an effective and efficient multi-host attack investigation framework, constructs end-to-end APT attack stories from audit logs by combining causality analysis, Natural Language Processing (NLP), and machine learning. Identifying key attack patterns, ATLAS proficiently analyzes and pinpoints attack events, minimizing alert fatigue for cyber analysts. During evaluations involving ten real-world APT attacks executed in a realistic virtual environment, ATLAS demonstrated an ability to recover attack steps and construct attack stories with an average precision of 91.06%, a recall of 97.29%, and an F1-score of 93.76%, providing a robust framework for understanding and mitigating cyber threats.</p><p dir="ltr">Concurrently, ARCHPLC, an advanced approach for enhancing ICS security, combines static analysis of PLC code and data mining from ICS data traces to derive accurate invariants, providing a comprehensive understanding of ICS behavior. ARCHPLC employs physical causality graph analysis techniques to identify cause-effect relationships among plant components (e.g., sensors and actuators), enabling efficient and quantitative discovery of physical causality invariants. Supporting patching and run-time monitoring modes, ARCHPLC inserts derived invariants into PLC code using program synthesis in patching mode and inserts invariants into a dedicated monitoring program for continuous safety checks in run-time monitoring mode. ARCHPLC adeptly detects and mitigates run-time anomalies, providing exceptional protection against cyber-physical attacks with minimal overhead. In evaluations against 11 cyber-physical attacks on a Fischertechnik manufacturing plant and a chemical plant simulator, ARCHPLC protected the plants without any false positives or negatives, with an average run-time overhead of 14.31% in patching mode and 0.4% in run-time monitoring mode.</p><p dir="ltr">In summary, this dissertation provides invaluable solutions that equip cybersecurity professionals to enhance APT attack investigation, enabling them to identify and comprehend complex attacks with heightened accuracy. Moreover, these solutions significantly bolster the safety and security of ICS infrastructure, effectively protecting critical systems and strengthening defenses against cyber-physical attacks, thereby contributing substantially to the field of cybersecurity.</p>

<p>Can a critical industrial system, such as a nuclear reactor, be made self-aware and cognizant of its operational history? Can it alert authorities covertly to malicious intrusion without exposing its  defense  mechanisms?  What  if  the  intruders  are  highly  knowledgeable  adversaries,  or  even  insiders that may have designed the system? This thesis addresses these research questions through a novel physical process defense called Covert Cognizance (C2). </p> <p>C2  serves  as  a  last  line  of  defense  to  industrial  systems  when  existing  information  and  operational technology defenses have been breached by advanced persistent threat (APT) actors or insiders. It is an active form of defense that may be embedded in an existing system to induce intelligence,  i.e.,  self-awareness,  and  make  various subsystems  aware  of  each  other.  It  interacts with the system at the process level and provides an additional layer of security to the process data therein without the need of a human in the loop. </p> <p>The C2 paradigm is  founded on two core requirements – zero-impact and zero-observability. Departing from contemporary active defenses, zero-impact requires a successful implementationto leave no footprint on the system ensuring identical operation while zero-observability requires that the embedding is immune to pattern-discovery algorithms.  In other words, a third-party such as  a  malicious  intruder  must  be  unable  to  detect  the  presence  of  the  C2  defense  based  on  observation of the process data, even when augmented by machine learning tools that are adept at pattern discovery. </p> <p>In the present work, nuclear reactor simulations are embedded with the C2 defense to induce awareness across subsystems and defend them against highly knowledgeable adversaries that have bypassed existing safeguards such as model-based defenses.  Specifically, the subsystems are made aware  of  each  other  by  embedding  critical information from  the  process  variables  of  one sub-module  along  the  noise of  the  process  variables  of  another,  thus  rendering  the  implementation  covert and  immune  to  pattern  discovery.   The  implementation  is  validated  using  generative adversarial  nets,  representing  a  state-of-the-art  machine  learning  tool,  and  statistical  analysis  of  the  reactor  states,  control  inputs,  outputs  etc. The  work  is  also  extended  to  data  masking  applications  via  the  deceptive  infusion  of  data  (DIOD)  paradigm.  Future  work  focuses  on  the  development of automated C2 modules for “plug ‘n’ play” deployment onto critical infrastructure and/or their digital twins.</p>