Realizing the applications of Internet of Things (IoT) with the goal of achieving a more efficient and automated world requires billions of connected smart devices and the minimization of hardware cost in these devices. As a result, many IoT devices do not have sufficient resources to support various protocols required in many IoT applications. Because of this, new protocols have been introduced to support the integration of these devices. One of these protocols is the increasingly popular routing protocol for low-power and lossy networks (RPL). However, this protocol is well known to attract blackhole and sinkhole attacks and cause serious difficulties when using more computationally intensive techniques to protect against these attacks, such as intrusion detection systems and rank authentication schemes. In this paper, an effective approach is presented to protect RPL networks against blackhole attacks. The approach does not address sinkhole attacks because they cause low damage and are often used along blackhole attacks and can be detected when blackhole attaches are detected. This approach uses the feature of multiple parents per node and a parent evaluation system enabling nodes to select more reliable routes. Simulations have been conducted, compared to existing approaches this approach would provide better protection against blackhole attacks with much lower overheads for small RPL networks.

Personalized learning is gaining popularity in online computer science education due to its characteristics of pacing the learning progress and adapting the instructional approach to each individual learner from a diverse background. Among various instructional methods in computer science education, hands-on labs have unique requirements of understanding learners' behavior and assessing learners' performance for personalization. Hands-on labs are a critical learning approach for cybersecurity education. It provides real-world complex problem scenarios and helps learners develop a deeper understanding of knowledge and concepts while solving real-world problems. But there are unique challenges when using hands-on labs for cybersecurity education. Existing hands-on lab exercises materials are usually managed in a problem-centric fashion, while it lacks a coherent way to manage existing labs and provide productive lab exercising plans for cybersecurity learners. To solve these challenges, a personalized learning platform called ThoTh Lab specifically designed for computer science hands-on labs in a cloud environment is established. ThoTh Lab can identify the learning style from student activities and adapt learning material accordingly. With the awareness of student learning styles, instructors are able to use techniques more suitable for the specific student, and hence, improve the speed and quality of the learning process. ThoTh Lab also provides student performance prediction, which allows the instructors to change the learning progress and take other measurements to help the students timely. A knowledge graph in the cybersecurity domain is also constructed using Natural language processing (NLP) technologies including word embedding and hyperlink-based concept mining. This knowledge graph is then utilized during the regular learning process to build a personalized lab recommendation system by suggesting relevant labs based on students' past learning history to maximize their learning outcomes. To evaluate ThoTh Lab, several in-class experiments were carried out in cybersecurity classes for both graduate and undergraduate students at Arizona State University and data was collected over several semesters. The case studies show that, by leveraging the personalized lab platform, students tend to be more absorbed in a lab project, show more interest in the cybersecurity area, spend more effort on the project and gain enhanced learning outcomes.

Demand for processing machine learning workloads has grown incredibly over the past few years. Kubernetes, an open-source container orchestrator, has been widely used by public and private cloud providers for building scalable systems for meeting this demand. The data used to train machine learning workloads can be sensitive in nature, and organizations may prefer to be responsible for their data security and governance by housing it on on-premises systems. Hybrid cloud gives organizations the flexibility to use both on-premises and cloud infrastructure together, leveraging the advantages of both. While there is a long list of benefits, Kubernetes has limitations by design that limit a user’s abilities in a hybrid cloud environment. The Kubernetes control plane does not allow for the management of worker nodes across cloud providers. This boundary puts new responsibilities on the end-user when deploying a hybrid cloud workload. The end-user must create their clusters and specify which cluster the workload will be scheduled to ahead of time. The Kubernetes scheduler will not take the capacity of another cluster into account. To address these limitations, this thesis presents a new hybrid cloud Kubernetes scheduler that can create new clusters on-demand and burst machine learning workloads to a public cloud when on-premises resources are insufficient. Workloads begin scheduling on an on-premises Kubernetes cluster. When the on-premises cluster’s capacity is exhausted, a new Kubernetes cluster is created on-demand in a public cloud provider, and machine learning tasks waiting in the Kubernetes scheduling queue are dynamically migrated to the public cloud provider’s Kubernetes cluster. The public Kubernetes cluster is dynamically sized and auto scaled based on the pending tasks’ demand. When migrating tasks, the data dependencies among tasks are considered, and a region is dynamically chosen to reduce migration time and cost. The scheduler is experimentally evaluated with real-world machine learning workloads, including predicting if a subscriber will stay with a subscription service, predicting the discount needed to retain a subscription customer, predicting if a credit card transaction is fraudulent, and simulated real-world job arrival behavior in a real hybrid cloud environment. Results show that the scheduler can substantially reduce the workload execution time by dynamically migrating tasks from on-premises to public cloud and minimizing the cost by dynamically sizing and scaling the public cluster.

Applications over a gesture-based human-computer interface (HCI) require a new user login method with gestures because it does not have traditional input devices. For example, a user may be asked to verify the identity to unlock a device in a mobile or wearable platform, or sign in to a virtual site over a Virtual Reality (VR) or Augmented Reality (AR) headset, where no physical keyboard or touchscreen is available. This dissertation presents a unified user login framework and an identity input method using 3D In-Air-Handwriting (IAHW), where a user can log in to a virtual site by writing a passcode in the air very fast like a signature. The presented research contains multiple tasks that span motion signal modeling, user authentication, user identification, template protection, and a thorough evaluation in both security and usability. The results of this research show around 0.1% to 3% Equal Error Rate (EER) in user authentication in different conditions as well as 93% accuracy in user identification, on a dataset with over 100 users and two types of gesture input devices. Besides, current research in this area is severely limited by the availability of the gesture input device, datasets, and software tools. This study provides an infrastructure for IAHW research with an open-source library and open datasets of more than 100K IAHW hand movement signals. Additionally, the proposed user identity input method can be extended to a general word input method for both English and Chinese using limited training data. Hence, this dissertation can help the research community in both cybersecurity and HCI to explore IAHW as a new direction, and potentially pave the way to practical adoption of such technologies in the future.

The purpose of an election is for the voice of the voters to be heard. All the participants in an election must be able to trust that the result of an election is actually the opinion of the people, unaltered by anything or anyone that may be trying to sway the vote. In the voting process, any "black boxes" or secrets can lead to mistrust in the system. In this thesis, an approach is developed for an electronic voting framework that is transparent, auditable, and scalable, making it trustworthy and usable for a wide-scale election. Based on my analysis, linkable ring signatures are utilized in order to preserve voter privacy while ensuring that a corrupt authenticating authority could not sway the vote. A hierarchical blockchain framework is presented to make ring signatures a viable signature scheme even when working with large populations. The solution is evaluated for compliance with secure voting requirements and scalability.

The use of reactive security mechanisms in enterprise networks can, at times, provide an asymmetric advantage to the attacker. Similarly, the use of a proactive security mechanism like Moving Target Defense (MTD), if performed without analyzing the effects of security countermeasures, can lead to security policy and service level agreement violations. In this thesis, I explore the research questions 1) how to model attacker-defender interactions for multi-stage attacks? 2) how to efficiently deploy proactive (MTD) security countermeasures in a software-defined environment for single and multi-stage attacks? 3) how to verify the effects of security and management policies on the network and take corrective actions?

I propose a Software-defined Situation-aware Cloud Security framework, that, 1) analyzes the attacker-defender interactions using an Software-defined Networking (SDN) based scalable attack graph. This research investigates Advanced Persistent Threat (APT) attacks using a scalable attack graph. The framework utilizes a parallel graph partitioning algorithm to generate an attack graph quickly and efficiently. 2) models single-stage and multi-stage attacks (APTs) using the game-theoretic model and provides SDN-based MTD countermeasures. I propose a Markov Game for modeling multi-stage attacks. 3) introduces a multi-stage policy conflict checking framework at the SDN network's application plane. I present INTPOL, a new intent-driven security policy enforcement solution. INTPOL provides a unified language and INTPOL grammar that abstracts the network administrator from the underlying network controller's lexical rules. INTPOL develops a bounded formal model for network service compliance checking, which significantly reduces the number of countermeasures that needs to be deployed. Once the application-layer policy conflicts are resolved, I utilize an Object-Oriented Policy Conflict checking (OOPC) framework that identifies and resolves rule-order dependencies and conflicts between security policies.

The field of cyber-defenses has played catch-up in the cat-and-mouse game of finding vulnerabilities followed by the invention of patches to defend against them. With the complexity and scale of modern-day software, it is difficult to ensure that all known vulnerabilities are patched; moreover, the attacker, with reconnaissance on their side, will eventually discover and leverage them. To take away the attacker's inherent advantage of reconnaissance, researchers have proposed the notion of proactive defenses such as Moving Target Defense (MTD) in cyber-security. In this thesis, I make three key contributions that help to improve the effectiveness of MTD.

First, I argue that naive movement strategies for MTD systems, designed based on intuition, are detrimental to both security and performance. To answer the question of how to move, I (1) model MTD as a leader-follower game and formally characterize the notion of optimal movement strategies, (2) leverage expert-curated public data and formal representation methods used in cyber-security to obtain parameters of the game, and (3) propose optimization methods to infer strategies at Strong Stackelberg Equilibrium, addressing issues pertaining to scalability and switching costs. Second, when one cannot readily obtain the parameters of the game-theoretic model but can interact with a system, I propose a novel multi-agent reinforcement learning approach that finds the optimal movement strategy. Third, I investigate the novel use of MTD in three domains-- cyber-deception, machine learning, and critical infrastructure networks. I show that the question of what to move poses non-trivial challenges in these domains. To address them, I propose methods for patch-set selection in the deployment of honey-patches, characterize the notion of differential immunity in deep neural networks, and develop optimization problems that guarantee differential immunity for dynamic sensor placement in power-networks.

As automation research into penetration testing has developed, several methods have been proposed as suitable control mechanisms for use in pentesting frameworks. These include Markov Decision Processes (MDPs), partially observable Markov Decision Processes (POMDPs), and POMDPs utilizing reinforcement learning. Since much work has been done automating other aspects of the pentesting process using exploit frameworks and scanning tools, this is the next focal point in this field. This paper shows a fully-integrated solution comprised of a POMDP-based planning algorithm, the Nessus scanning utility, and MITRE's CALDERA pentesting platform. These are linked in order to create an autonomous AI attack platform with scanning, planning, and attack capabilities.

Vulnerability testing/evaluation is a regular task for cyber-security groups. Conducting tasks like this can take up a great amount of time and may not be perfect. Automating these tasks helps speed up the rate at which experts can test systems. However, script based or static programs that run automatically often do not have the versatility required to properly replace human analysis. With the advances in Artificial Intelligence and Machine Learning, a utility can be developed that would allow for the creation of penetration testing plans rather than manually testing vulnerabilities. A variety of existing cyber-security programs and utilities provide an API layer that commonly interacts with the Python environment. With the commonality of AI/ML tools within the Python ecosystem, a plugin like interface can be developed to feed any AI/ML program real world data and receive a response/report in return. Using Python 2.7+, Python 3.6+, pymdptoolbox, and POMDPy, a program was developed that ingests real-world data from scanning tools and returned a suggested course of action to be used by analysts in order to perform a practical validation of the algorithms in a real world setting. This program was able to successfully navigate a test network and produce results that were expected to be found on the target machines without needing human analysis of the network. Using POMDP based systems for more cyber-security type tasks may be a valuable use case for future developments and help ease the burden faced in a rapid paced world.

The traditional access control system suffers from the problem of separation of data ownership and management. It poses data security issues in application scenarios such as cloud computing and blockchain where the data owners either do not trust the data storage provider or even do not know who would have access to their data once they are appended to the chain. In these scenarios, the data owner actually loses control of the data once they are uploaded to the outside storage. Encryption-before-uploading is the way to solve this issue, however traditional encryption schemes such as AES, RSA, ECC, bring about great overheads in key management on the data owner end and could not provide fine-grained access control as well.

Attribute-Based Encryption (ABE) is a cryptographic way to implement attribute-based access control, which is a fine-grained access control model, thus solving all aforementioned issues. With ABE, the data owner would encrypt the data by a self-defined access control policy before uploading the data. The access control policy is an AND-OR boolean formula over attributes. Only users with attributes that satisfy the access control policy could decrypt the ciphertext. However the existing ABE schemes do not provide some important features in practical applications, e.g., user revocation and attribute expiration. Furthermore, most existing work focus on how to use ABE to protect cloud stored data, while not the blockchain applications.

The main objective of this thesis is to provide solutions to add two important features of the ABE schemes, i.e., user revocation and attribute expiration, and also provide a practical trust framework for using ABE to protect blockchain data. To add the feature of user revocation, I propose to add user's hierarchical identity into the private attribute key. In this way, only users whose identity is not revoked and attributes satisfy the access control policy could decrypt the ciphertext. To add the feature of attribute expiration, I propose to add the attribute valid time period into the private attribute key. The data would be encrypted by access control policy where all attributes have a temporal value. In this way, only users whose attributes both satisfy the access policy and at the same time these attributes do not expire,

are allowed to decrypt the ciphertext. To use ABE in the blockchain applications, I propose an ABE-enabled trust framework in a very popular blockchain platform, Hyperledger Fabric. Based on the design, I implement a light-weight attribute certificate authority for attribute distribution and validation; I implement the proposed ABE schemes and provide a toolkit which supports system setup, key generation,

data encryption and data decryption. All these modules were integrated into a demo system for protecting sensitive les in a blockchain application.