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Cyber-physical systems (CPS) refer to a group of systems that combine the real physical world with cyber components. Traditionally, the applications of CPS in research and the real world mainly include smart power grid, autonomous automobile systems, and robotics systems. In recent years, due to the fast development of pervasive computing, sensor manufacturing, and artificial intelligence technologies, mobile cyber-physical systems that extend the application domains of traditional cyber-physical systems have become increasingly popular. In mobile cyber-physical systems, devices have rich features, such as significant computational resources, multiple communication radios, various sensor modules, and high-level programming languages. These features enable us to build more powerful and convenient applications and systems for mobile users. At the same time, such information can also be leveraged by attackers to design new types of attacks. The security and privacy issues can exist in any application of mobile CPS. In terms of defense systems, we focus on three important topics: voice liveness detection, face forgery detection, and securing PIN-based authentication. In terms of attack systems, we study the location privacy in augmented reality (AR) applications. We first investigate the voice replay attacks on smartphones. Voice input is becoming an important interface on smartphones since it can provide better user experience compared with traditional typing-based input methods. However, because the human voice is often exposed to the public, attackers can easily steal victims' voices and replay it to victims' devices to issue malicious commands. To defend the smartphone from voice replay attacks, we propose a novel liveness detection system, which can determine whether the incoming voice is from a live person or a loudspeaker. The key idea is that voices are produced and finalized at multiple positions in human vocal systems, while the audio signals from loudspeakers are from one position. By using two microphones on the smartphone to record the voice at two positions and measure their relationship, the proposed system can defend against voice replay attacks with a high success rate. Besides smartphones, voice replay attacks are also feasible on AR headsets. However, due to the special hardware positions, the current voice liveness detection system designed for smartphones cannot be deployed on AR headsets. To address this issue, we propose a novel voice liveness detection system for AR headsets. The key insight is that the human voice can propagate through the internal body. By attaching a contact microphone around the user's temple, we can collect the internal body voice. A voice is determined from a live person as long as the collected internal body voice has a strong relationship with the mouth voice. Since the contact microphone is cheap, tiny, and thin, it can be embedded in current AR headsets with minimal additional cost. Next, we propose a system to detect the fake face in real-time video chat. Recent developments in deep learning-based forgery techniques largely improved the ability of forgery attackers. With the help of face reenactment techniques, attackers can transfer their facial expressions to another person's face to create fake facial videos in real-time with very high quality. In our system, we find that the face of a live person can reflect the screen light, and this reflected light can be captured by the web camera. Moreover, current face forgery techniques cannot generate such light change with acceptable quality. Therefore, we can measure the correlation and similarity of the luminance changes between the screen light and the face-reflected light to detect the liveness of the face. We also study to leverage IoT devices to enhance the privacy of some daily operations. We find that the widely used personal identification number (PIN) is not secure and can be attacked in many ways. In some scenarios, it is hard to prevent attackers from obtaining the victim's PIN. Therefore, we propose a novel system to secure the PIN input procedure even if the victim's PIN has been leaked. The basic idea is that different people have different PIN input behavior even for the same PIN. Even though attackers can monitor the victim's PIN input behaviors and imitate it afterward, the biological differences among each person's hands still exist and can be used to differentiate them. To capture both PIN input behavior and the biological features, we install a tiny light sensor at the center of the PIN pad to transfer the information into a light signal. By extracting useful features from multiple domains, we can determine whether the PIN input is from the same person with high accuracy. Besides designing new defense systems, we also show that sensory data and side-channel information can be leveraged to launch new types of attacks. We conduct a study on the network traffic of location-based AR applications. We find that it is feasible to infer the real-time location of a user using the short-time network traffic if the downloading jobs are related to the current location. By carefully deploying fake AR contents at some locations, our attack system can infer the location of the user with high accuracy by processing noisy network traffic data. / Computer and Information Science

With the advancement of technology, there has been a push to transition from the conventional electric grid to a smart grid. A smart grid is an electric delivery system that uses technology such as electronic sensors and digital communication networks to improve the reliability, resilience, and efficiency of the system. The transition toward a smart grid has increased the importance of networked control systems (NCS), which are the infrastructure that allows sensors, actuators and controllers to exchange information via a digital communication network. The research presents the development of an islanded generator NCS, and a grid connected NCS, and the investigation of the effects of cyberattacks on the NCS. This research considers two types of cyberattacks, such as Denial-of-Service (DoS) attack, and false data injection in the generator control loop. DoS attacks greatly increase the rate of packet loss and the duration of packet delay in a network. A high degree of packet drop and delay degrade the performance of the controller, which causes problems in the synchronization of the generator with the rest of the grid. False data injection in the sensors alters the generator terminal voltage and power output, and can cause the generator to lose synchronism. A mathematical model of the generator NCS systems is developed which includes the data acquisition and network characteristics, as well as the generator dynamics. The stability analysis of each NCS is performed which provides a mathematical approach to understanding the severity of cyberattacks that the system can tolerate before becoming unstable. The performance of the controllers, with respect to voltage control, is experimentally evaluated. / Educational Psychology

Enrollment in cyber schools has increased steadily from their inception in 1996 through 2019. Despite this increase there is a limited understanding of how to train teachers to teach in virtual classrooms. Most virtual professional development is created and delivered by leaders of cyber schools. Therefore, to contribute to the literature on teacher training for online schools, this dissertation explores what school leaders of a cyber school perceive about the skills required to teach online and how they address these skills through the preparation and ongoing development of their new and veteran teachers. All 30 members of the focal school’s leadership team were invited to complete an anonymous questionnaire, and additionally, they were invited to participate in an interview. The data were analyzed via frequency calculations and coding. Conclusions were focused on the knowledge gaps of new teachers, what defines a successful professional development experience, the state of teacher training at cyber schools, and the extent school leaders are involved with the creation of professional development at their school. I found that the knowledge gaps of new teachers depend on their prior teaching experiences, with the teachers who have more experience in brick-and-mortar schools having the most gaps. The most effective professional development activities were characterized to be engaging, relevant, timely, and a good example of what teachers should do in their own classrooms. The focal school uses several forms of professional development to meet the needs of new teachers: an in-person onboarding, induction, and mentorship. At the focal school, veteran teachers are provided with grade-level weekly workshops and a content-level professional learning community. Involvement in the creation of professional development is dependent on an individual’s title and role. / Educational Leadership

Cyber-physical systems refer to a new generation of synergy systems with integrated computational and physical processes which interact with one other. The development and simulation of cyber-physical systems (CPSs) are obstructed by the complexity of the subsystems of which they are comprised, fundamental differences in the operation of cyber and physical elements, significant correlative dependencies among the elements, and operation in dynamic and open environments. The Multiple Belief-Desire-Intention (BDI) agent system (BDI multi-agent system) is a promising choice for overcoming these challenges, since it offers a natural way to decompose complex systems or large scale problems into decentralized, autonomous, interacting, more or less intelligent entities. In particular, BDI agents have the ability to interact with, and expand the capabilities of, the physical world through computation, communication, and control. A BDI agent has its philosophical grounds on intentionality and practical reasoning, and it is natural to combine a philosophical model of human practical reasoning with the physical operation and any cyber infrastructure. In this thesis, we introduce the BDI Model, discuss implementations of BDI agents from an ideal theoretical perspective as well as from a more practical perspective, and show how they can be used to bridge the cyber infrastructure and the physical operation using the framework. We then strengthen the framework's performance using the state-of-the-art parallel computing architecture and eventually propose a BDI agent based software framework to enable the efficient modeling and simulation of heterogeneous CPS systems in an integrated manner. / Electrical and Computer Engineering

Reaping the benefits of the Internet of Things (IoT) system is contingent upon developing IoT-specific security and privacy solutions. Conventional security and authentication solutions often fail to meet IoT requirements due to the computationally limited and portable nature of IoT objects. Privacy in IoT is a major issue especially in the light of current attacks on Facebook and Uber. Research efforts in both the academic and the industrial fields have been focused on providing security and privacy solutions that are specific to IoT systems. These solutions include systems to manage keys, systems to handle routing protocols, systems that handle data transmission, access control for devices, and authentication of devices. One of these solutions is Blockchain, a trust-less peer-to-peer network of devices with an immutable data storage that does not require a trusted party to maintain and validate data entries in it. This emerging technology solves the problem of centralization in systems and has the potential to end the corporations control over our personal information. This unique characteristic makes blockchain an excellent candidate to handle data communication and storage between IoT devices without the need of oracle nodes to monitor and validate each data transaction. The peer-to-peer network of IoT devices validates data entries before being added to the blockchain database. However, accurate authentication of each IoT device using simple methods is another challenging problem. In this dissertation, a complete novel system is proposed to authenticate, verify, and secure devices in IoT systems. The proposed system consists of a blockchain framework to collect, monitor, and analyze data in IoT systems. The blockchain based system exploits a method, called Sharding, in which devices are grouped into smaller subsets to provide a scalable system. In addition to solving the scalability problem in blockchain, the proposed system is secured against the 51% attack in which a malicious node tries to gain control over the majority of devices in a single shard in order to disrupt the validation process of data entries. The proposed system dynamically changes the assignment of devices to shards to significantly decrease the possibility of performing 51% attacks. The second part of the novel system presented in this work handles IoT device authentication. The authentication framework uses device-specific information, called fingerprints, along with a transfer learning tool to authenticate objects in the IoT. The framework tracks the effect of changes in the physical environment on fingerprints and uses unique IoT environmental effects features to detect both cyber and cyber-physical emulation attacks. The proposed environmental effects estimation framework showed an improvement in the detection rate of attackers without increasing the false positives rate. The proposed framework is also shown to be able to detect cyber-physical attackers that are capable of replicating the fingerprints of target objects which conventional methods are unable to detect. In addition, a transfer learning approach is proposed to allow the use of objects with different types and features in the environmental effects estimation process. The transfer learning approach was also implemented in cognitive radio networks to prevent primary users emulation attacks that exist in these networks. Lastly, this dissertation investigated the challenge of preserving privacy of data stored in the proposed blockchain-IoT system. The approach presented continuously analyzes the data collected anonymously from IoT devices to insure that a malicious entity will not be able to use these anonymous datasets to uniquely identify individual users. The dissertation led to the following key results. First, the proposed blockchain based framework that uses sharding was able to provide a decentralized, scalable, and secured platform to handle data exchange between IoT devices. The security of the system against 51% attacks was simulated and showed significant improvements compared to typical blockchain implementations. Second, the authentication framework of IoT devices is shown to yield to a 40% improvement in the detection of cyber emulation attacks and is able to detect cyber-physical emulation attacks that conventional methods cannot detect. The key results also show that the proposed framework improves the authentication accuracy while the transfer learning approach yields up to 70% additional performance gains. Third, the transfer learning approach to combine knowledge about features from multiple device types was also implemented in cognitive radio networks and showed performance gains with an average of 3.4% for only 10% relevant information between the past knowledge and the current environment signals. / Doctor of Philosophy / The Internet of things (IoT) system is anticipated to reach billions of devices by the year 2020. With this massive increase in the number of devices, conventional security and authentication solutions will face many challenges from computational limits to privacy and security challenges. Research on solving the challenges of IoT systems is focused on providing lightweight solutions to be implemented on these low energy IoT devices. However these solutions are often prone to different types of attacks. The goal of this dissertation is to present a complete custom solution to secure IoT devices and systems. The system presented to solve IoT challenges consists of three main components. The first component focuses on solving scalability and centralization challenges that current IoT systems suffer from. To accomplish this a combination of distributed system, called blocchain, and a method to increase scalability, called Sharding, were used to provide both scalability and decentralization while maintaining high levels of security. The second component of the proposed solution consists of a novel framework to authenticate the identity of each IoT device. To provide an authentication solution that is both simple and effective, the framework proposed used a combination of features that are easy to collect, called fingerprints. These features were used to model the environment surrounding each IoT device to validate its identity. The solution uses a method called transfer learning to allow the framework to run on different types of devices. The proposed frameworks were able to provide a solution that is scalable, simple, and secured to handle data exchange between IoT devices. The simulation presented showed significant improvements compared to typical blockchain implementations. In addition, the frameworks proposed were able to detect attackers that have the resources to replicate all the device specific features. The proposed authentication framework is the first framework to be able to detect such an advanced attacker. The transfer learning tool added to the authentication framework showed performance gains of up to 70%.

Additive manufacturing (AM) is a growing section of the advanced manufacturing field and is being used to fabricate an increasing number of critical components, from aerospace components to medical implants. At the same time, cyber-physical attacks targeting manufacturing systems have continued to rise. For this reason, there is a need to research new techniques and methods to ensure the integrity of parts fabricated on AM systems. This work seeks to address this need by first performing a detailed analysis of vulnerabilities in the AM process chain and how these attack vectors could be used to execute malicious part sabotage attacks. This work demonstrated the ability of an internal void attack on the .STL file to reduce the yield load of a tensile specimen by 14% while escaping detection by operators. To mitigate these vulnerabilities, a new impedance-based approach for in situ monitoring of AM systems was created. Two techniques for implementing this approach were investigated, direct embedding of sensors in AM parts, and the use of an instrumented fixture as a build plate. The ability to detect changes in material as small as 1.38% of the printed volume (53.8 mm3) on a material jetting system was demonstrated. For metal laser powder bed fusion systems, a new method was created for representing side-channel meltpool emissions. This method reduces the quantity of data while remaining sensitive enough to detect changes to the toolpath and process parameters caused by malicious attacks. To enable the SCMS to validate part quality during fabrication required a way to receive baseline part quality information across an air-gap. To accomplish this a new process noise tolerant method of cyber-physical hashing for continuous data sets was presented. This method was coupled with new techniques for the storage, transmission, and reconstructing of the baseline quality data was implemented using stacks of "ghost" QR codes stored in the toolpath to transmit information through the laser position. A technique for storing and transmitting quality information in the toolpath files of parts using acoustic emissions was investigated. The ATTACH (additive toolpath transmission of acoustic cyber-physical hash) method used speed modulation of infill roads in a material extrusion system to generate acoustic tones containing quality information about the part. These modulations were able to be inserted without affecting the build time or requiring additional material and did not affect the quality of the part that contained them. Finally, a framework for the design and implementation of a SCMS for protecting AM systems against malicious cyber-physical part sabotage attacks was created. The IDEAS (Identify, Define, Establish, Aggregate, Secure) framework provides a detailed reference for engineers to use to secure AM systems by leveraging the previous work in vulnerability assessment, creation of new side-channel monitoring techniques, concisely representing quality data, and securely transmitting information to air-gapped systems through physical emissions. / Doctor of Philosophy / Additive manufacturing (AM), more widely known as 3D printing, is a growing field of manufacturing where parts are fabricated by building layers of material on top of each other. This layer-based approach allows the production of parts with complex shapes that cannot be made using more traditional approaches such as machining. This capability allows for great freedom in designing parts, but also means that defects can be created inside of parts during fabrication. This work investigates ways that an adversary might seek to sabotage AM parts through a cyber-physical attack. To prevent attacks seeking to sabotage AM parts several new approaches for security are presented. The first approach uses tiny vibrations to detect changes to part shape or material by attaching a small sensor either directly to the parts or to the surface that they are built on. Because an attack that sabotages an AM system (3D printer) could also affect the systems used to detect part defects these systems should be digitally separated from each other. By using a series of QR codes fabricated by the AM system along with the parts, information can be sent from the AM system to the monitoring system through its sensors. This prevents a cyber-attack from jumping from the AM system to the monitoring system. By temporarily turning off the laser power and tracking the movements of the guiding mirrors the QR code information can be sent to the monitoring system without having to actually print the QR code. The information stored in the QR code is compared to the emission generated when fabricating the parts and is used to detect if an attack has occurred since that would change the emissions from the part, but not from the QR code. Another approach for sending information from the AM system using physical emissions is by using sounds generated during part fabrication. Using a desktop scale 3D printer, the speed of certain movements was increased or decreased. The change in speed causes the sound emitted from the printer to change, while not affecting the actual quality of the print. By using a series of tones, similar to Morse code, information can be sent from the printer. Research was performed on the best settings to use to transmit the information as well as how to automatically receive and decode the information using a microphone. The final step in this work is a framework that serves as a guide for designing and implementing monitoring systems that can detect sabotage attacks on AM parts. The framework covers how to evaluate a system for potential vulnerabilities and how to use this information to choose sensors and data processing techniques to reduce the risk of cyber-physical attacks.

As buildings evolve towards the envisioned smart building paradigm, smart buildings' cyber-security issues and physical security issues are mingling. Although research studies have been conducted to detect and prevent physical (or cyber) intrusions to smart building systems(SBS), it is still unknown (1) how one type of intrusion facilitates the other, and (2) how such synergic attacks compromise the security protection of whole systems. To investigate both research questions, the author proposes a graph-based testbed to simulate cyber-physical attacks on smart buildings. The testbed models both cyber and physical accesses of a smart building in an integrated graph, and simulates diverse cyber-physical attacks to assess their synergic impacts on the building and its systems. In this thesis, the author presents the testbed design and the developed prototype, SHSIM. An experiment is conducted to simulate attacks on multiple smart home designs and to demonstrate the functions and feasibility of the proposed simulation system. / Master of Science / A smart home/building is a residence containing multiple connected devices which enable remote monitoring, automation, and management of appliances and systems, such as lighting, heating, entertainment, etc. Since the early 2000s, this concept of a smart home has becomequite popular due to rapid technological improvement. However, it brings with it a lot of security issues. Typically, security issues related to smart homes can be classified into two types - (1) cybersecurity and (2) physical security. The cyberattack involves hacking into a network to gain remote access to a system. The physical attack deals with unauthorized access to spaces within a building by damaging or tampering with access control. So far the two kinds of attacks on smart homes have been studied independently. However, it is still unknown (1) how one type of attack facilitates the other, and (2) how the combination of two kinds of attacks compromises the security of the whole smart home system. Thus, to investigate both research questions, we propose a graph-based approach to simulate cyber-physical attacks on smart homes/buildings. During the process, we model the smart home layout into an integrated graph and apply various cyber-physical attacks to assess the security of the smart building. In this thesis, I present the design and implementation of our tool, SHSIM. Using SHSIM we perform various experiments to mimic attacks on multiple smart home designs. Our experiments suggest that some current smart home designs are vulnerable to cyber-physical attacks

Memory-corruption vulnerability is one of the most common attack vectors used to compromise computer systems. Such vulnerabilities could lead to serious security problems and would remain an unsolved problem for a long time. Existing memory corruption attacks can be broadly classified into two categories: i) control-flow attacks and ii) data-oriented attacks. Though data-oriented attacks are known for a long time, the threats have not been adequately addressed due to the fact that most previous defense mechanisms focus on preventing control-flow exploits. As launching a control-flow attack becomes increasingly difficult due to many deployed defenses against control-flow hijacking, data-oriented attacks are considered an appealing attack technique for system compromise, including the emerging embedded control systems. To counter data-oriented attacks, mitigation techniques such as memory safety enforcement and data randomization can be applied in different stages over the course of an attack. However, attacks are still possible because currently deployed defenses can be bypassed. This dissertation explores the possibility of defeating data-oriented attacks through external monitoring using program anomaly detection techniques. I start with a systematization of current knowledge about exploitation techniques of data-oriented attacks and the applicable defense mechanisms. Then, I address three research problems in program anomaly detection against data-oriented attacks. First, I address the problem of securing control programs in Cyber-Physical Systems (CPS) against data-oriented attacks. I describe a new security methodology that leverages the event-driven nature in characterizing CPS control program behaviors. By enforcing runtime cyber-physical execution semantics, our method detects data-oriented exploits when physical events are inconsistent with the runtime program behaviors. Second, I present a statistical program behavior modeling framework for frequency anomaly detection, where frequency anomaly is the direct consequence of many non-control-data attacks. Specifically, I describe two statistical program behavior models, sFSA and sCFT, at different granularities. Our method combines the local and long-range models to improve the robustness against data-oriented attacks and significantly increase the difficulties that an attack bypasses the anomaly detection system. Third, I focus on defending against data-oriented programming (DOP) attacks using Intel Processor Trace (PT). DOP is a recently proposed advanced technique to construct expressive non-control data exploits. I first demystify the DOP exploitation technique and show its complexity and rich expressiveness. Then, I design and implement the DeDOP anomaly detection system, and demonstrate its detection capability against the real-world ProFTPd DOP attack. / Ph. D. / Memory-corruption vulnerability is one of the most common attack vectors used to compromise computer systems. Such vulnerabilities could lead to serious security problems and would remain an unsolved problem for a long time. This is because low-level memory-unsafe languages (e.g., C/C++) are still in use today for interoperability and speed performance purposes, and remain common sources of security vulnerabilities. Existing memory corruption attacks can be broadly classified into two categories: i) control-flow attacks that corrupt control data (e.g., return address or code pointer) in the memory space to divert the program’s control-flow; and ii) data-oriented attacks that target at manipulating non-control data to alter a program’s benign behaviors without violating its control-flow integrity. Though data-oriented attacks are known for a long time, the threats have not been adequately addressed due to the fact that most previous defense mechanisms focus on preventing control-flow exploits. As launching a control-flow attack becomes increasingly difficult due to many deployed defenses against control-flow hijacking, data-oriented attacks are considered an appealing attack technique for system compromise, including the emerging embedded control systems. To counter data-oriented attacks, mitigation techniques such as memory safety enforcement and data randomization can be applied in different stages over the course of an attack. However, attacks are still possible because currently deployed defenses can be bypassed. This dissertation explores the possibility of defeating data-oriented attacks through external monitoring using program anomaly detection techniques. I start with a systematization of current knowledge about exploitation techniques of data-oriented attacks and the applicable defense mechanisms. Then, I address three research problems in program anomaly detection against data-oriented attacks. First, I address the problem of securing control programs in Cyber-Physical Systems (CPS) against data-oriented attacks. The key idea is to detect subtle data-oriented exploits in CPS when physical events are inconsistent with the runtime program behaviors. Second, I present a statistical program behavior modeling framework for frequency anomaly detection, where frequency anomaly is often consequences of many non-control-data attacks. Our method combines the local and long-range models to improve the robustness against data-oriented attacks and significantly increase the difficulties that an attack bypasses the anomaly detection system. Third, I focus on defending against data-oriented programming (DOP) attacks using Intel Processor Trace (PT). I design and implement the DEDOP anomaly detection system, and demonstrate its detection capability against the real-world DOP attack.