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- Creators: Huang, Dijiang
Description
Recent trends in big data storage systems show a shift from disk centric models to memory centric models. The primary challenges faced by these systems are speed, scalability, and fault tolerance. It is interesting to investigate the performance of these two models with respect to some big data applications. This thesis studies the performance of Ceph (a disk centric model) and Alluxio (a memory centric model) and evaluates whether a hybrid model provides any performance benefits with respect to big data applications. To this end, an application TechTalk is created that uses Ceph to store data and Alluxio to perform data analytics. The functionalities of the application include offline lecture storage, live recording of classes, content analysis and reference generation. The knowledge base of videos is constructed by analyzing the offline data using machine learning techniques. This training dataset provides knowledge to construct the index of an online stream. The indexed metadata enables the students to search, view and access the relevant content. The performance of the application is benchmarked in different use cases to demonstrate the benefits of the hybrid model.
ContributorsNAGENDRA, SHILPA (Author) / Huang, Dijiang (Thesis advisor) / Zhao, Ming (Committee member) / Maciejewski, Ross (Committee member) / Chung, Chun-Jen (Committee member) / Arizona State University (Publisher)
Created2017
Description
The telephone network is used by almost every person in the modern world. With the rise of Internet access to the PSTN, the telephone network today is rife with telephone spam and scams. Spam calls are significant annoyances for telephone users, unlike email spam, spam calls demand immediate attention. They are not only significant annoyances but also result in significant financial losses in the economy. According to complaint data from the FTC, complaints on illegal calls have made record numbers in recent years. Americans lose billions to fraud due to malicious telephone communication, despite various efforts to subdue telephone spam, scam, and robocalls.
In this dissertation, a study of what causes the users to fall victim to telephone scams is presented, and it demonstrates that impersonation is at the heart of the problem. Most solutions today primarily rely on gathering offending caller IDs, however, they do not work effectively when the caller ID has been spoofed. Due to a lack of authentication in the PSTN caller ID transmission scheme, fraudsters can manipulate the caller ID to impersonate a trusted entity and further a variety of scams. To provide a solution to this fundamental problem, a novel architecture and method to authenticate the transmission of the caller ID is proposed. The solution enables the possibility of a security indicator which can provide an early warning to help users stay vigilant against telephone impersonation scams, as well as provide a foundation for existing and future defenses to stop unwanted telephone communication based on the caller ID information.
In this dissertation, a study of what causes the users to fall victim to telephone scams is presented, and it demonstrates that impersonation is at the heart of the problem. Most solutions today primarily rely on gathering offending caller IDs, however, they do not work effectively when the caller ID has been spoofed. Due to a lack of authentication in the PSTN caller ID transmission scheme, fraudsters can manipulate the caller ID to impersonate a trusted entity and further a variety of scams. To provide a solution to this fundamental problem, a novel architecture and method to authenticate the transmission of the caller ID is proposed. The solution enables the possibility of a security indicator which can provide an early warning to help users stay vigilant against telephone impersonation scams, as well as provide a foundation for existing and future defenses to stop unwanted telephone communication based on the caller ID information.
ContributorsTu, Huahong (Author) / Doupe, Adam (Thesis advisor) / Ahn, Gail-Joon (Thesis advisor) / Huang, Dijiang (Committee member) / Zhang, Yanchao (Committee member) / Zhao, Ziming (Committee member) / Arizona State University (Publisher)
Created2017
Description
Today the information technology systems have addresses, software stacks and other configuration remaining unchanged for a long period of time. This paves way for malicious attacks in the system from unknown vulnerabilities. The attacker can take advantage of this situation and plan their attacks with sufficient time. To protect our system from this threat, Moving Target Defense is required where the attack surface is dynamically changed, making it difficult to strike.
In this thesis, I incorporate live migration of Docker container using CRIU (checkpoint restore) for moving target defense. There are 460K Dockerized applications, a 3100% growth over 2 years[1]. Over 4 billion containers have been pulled so far from Docker hub. Docker is supported by a large and fast growing community of contributors and users. As an example, there are 125K Docker Meetup members worldwide. As we see industry adapting to Docker rapidly, a moving target defense solution involving containers is beneficial for being robust and fast. A proof of concept implementation is included for studying performance attributes of Docker migration.
The detection of attack is using a scenario involving definitions of normal events on servers. By defining system activities, and extracting syslog in centralized server, attack can be detected via extracting abnormal activates and this detection can be a trigger for the Docker migration.
In this thesis, I incorporate live migration of Docker container using CRIU (checkpoint restore) for moving target defense. There are 460K Dockerized applications, a 3100% growth over 2 years[1]. Over 4 billion containers have been pulled so far from Docker hub. Docker is supported by a large and fast growing community of contributors and users. As an example, there are 125K Docker Meetup members worldwide. As we see industry adapting to Docker rapidly, a moving target defense solution involving containers is beneficial for being robust and fast. A proof of concept implementation is included for studying performance attributes of Docker migration.
The detection of attack is using a scenario involving definitions of normal events on servers. By defining system activities, and extracting syslog in centralized server, attack can be detected via extracting abnormal activates and this detection can be a trigger for the Docker migration.
ContributorsBohara, Bhakti (Author) / Huang, Dijiang (Thesis advisor) / Doupe, Adam (Committee member) / Zhao, Ziming (Committee member) / Arizona State University (Publisher)
Created2017
Description
Access control is necessary for information assurance in many of today's applications such as banking and electronic health record. Access control breaches are critical security problems that can result from unintended and improper implementation of security policies. Security testing can help identify security vulnerabilities early and avoid unexpected expensive cost in handling breaches for security architects and security engineers. The process of security testing which involves creating tests that effectively examine vulnerabilities is a challenging task. Role-Based Access Control (RBAC) has been widely adopted to support fine-grained access control. However, in practice, due to its complexity including role management, role hierarchy with hundreds of roles, and their associated privileges and users, systematically testing RBAC systems is crucial to ensure the security in various domains ranging from cyber-infrastructure to mission-critical applications. In this thesis, we introduce i) a security testing technique for RBAC systems considering the principle of maximum privileges, the structure of the role hierarchy, and a new security test coverage criterion; ii) a MTBDD (Multi-Terminal Binary Decision Diagram) based representation of RBAC security policy including RHMTBDD (Role Hierarchy MTBDD) to efficiently generate effective positive and negative security test cases; and iii) a security testing framework which takes an XACML-based RBAC security policy as an input, parses it into a RHMTBDD representation and then generates positive and negative test cases. We also demonstrate the efficacy of our approach through case studies.
ContributorsGupta, Poonam (Author) / Ahn, Gail-Joon (Thesis advisor) / Collofello, James (Committee member) / Huang, Dijiang (Committee member) / Arizona State University (Publisher)
Created2014
Description
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.
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.
ContributorsDong, Qiuxiang (Author) / Huang, Dijiang (Thesis advisor) / Sen, Arunabha (Committee member) / Doupe, Adam (Committee member) / Arizona State University (Publisher)
Created2020
Description
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.
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.
ContributorsChowdhary, Ankur (Author) / Huang, Dijiang (Thesis advisor) / Kambhampati, Subbarao (Committee member) / Doupe, Adam (Committee member) / Bao, Youzhi (Committee member) / Arizona State University (Publisher)
Created2020
Description
Many existing applications of machine learning (ML) to cybersecurity are focused on detecting malicious activity already present in an enterprise. However, recent high-profile cyberattacks proved that certain threats could have been avoided. The speed of contemporary attacks along with the high costs of remediation incentivizes avoidance over response. Yet, avoidance implies the ability to predict - a notoriously difficult task due to high rates of false positives, difficulty in finding data that is indicative of future events, and the unexplainable results from machine learning algorithms.
In this dissertation, these challenges are addressed by presenting three artificial intelligence (AI) approaches to support prioritizing defense measures. The first two approaches leverage ML on cyberthreat intelligence data to predict if exploits are going to be used in the wild. The first work focuses on what data feeds are generated after vulnerability disclosures. The developed ML models outperform the current industry-standard method with F1 score more than doubled. Then, an approach to derive features about who generated the said data feeds is developed. The addition of these features increase recall by over 19% while maintaining precision. Finally, frequent itemset mining is combined with a variant of a probabilistic temporal logic framework to predict when attacks are likely to occur. In this approach, rules correlating malicious activity in the hacking community platforms with real-world cyberattacks are mined. They are then used in a deductive reasoning approach to generate predictions. The developed approach predicted unseen real-world attacks with an average increase in the value of F1 score by over 45%, compared to a baseline approach.
In this dissertation, these challenges are addressed by presenting three artificial intelligence (AI) approaches to support prioritizing defense measures. The first two approaches leverage ML on cyberthreat intelligence data to predict if exploits are going to be used in the wild. The first work focuses on what data feeds are generated after vulnerability disclosures. The developed ML models outperform the current industry-standard method with F1 score more than doubled. Then, an approach to derive features about who generated the said data feeds is developed. The addition of these features increase recall by over 19% while maintaining precision. Finally, frequent itemset mining is combined with a variant of a probabilistic temporal logic framework to predict when attacks are likely to occur. In this approach, rules correlating malicious activity in the hacking community platforms with real-world cyberattacks are mined. They are then used in a deductive reasoning approach to generate predictions. The developed approach predicted unseen real-world attacks with an average increase in the value of F1 score by over 45%, compared to a baseline approach.
ContributorsAlmukaynizi, Mohammed (Author) / Shakarian, Paulo (Thesis advisor) / Huang, Dijiang (Committee member) / Maciejewski, Ross (Committee member) / Simari, Gerardo I. (Committee member) / Arizona State University (Publisher)
Created2019
Description
Computer science education is an increasingly vital area of study with various challenges that increase the difficulty level for new students resulting in higher attrition rates. As part of an effort to resolve this issue, a new visual programming language environment was developed for this research, the Visual IoT and Robotics Programming Language Environment (VIPLE). VIPLE is based on computational thinking and flowchart, which reduces the needs of memorization of detailed syntax in text-based programming languages. VIPLE has been used at Arizona State University (ASU) in multiple years and sections of FSE100 as well as in universities worldwide. Another major issue with teaching large programming classes is the potential lack of qualified teaching assistants to grade and offer insight to a student’s programs at a level beyond output analysis.
In this dissertation, I propose a novel framework for performing semantic autograding, which analyzes student programs at a semantic level to help students learn with additional and systematic help. A general autograder is not practical for general programming languages, due to the flexibility of semantics. A practical autograder is possible in VIPLE, because of its simplified syntax and restricted options of semantics. The design of this autograder is based on the concept of theorem provers. To achieve this goal, I employ a modified version of Pi-Calculus to represent VIPLE programs and Hoare Logic to formalize program requirements. By building on the inference rules of Pi-Calculus and Hoare Logic, I am able to construct a theorem prover that can perform automated semantic analysis. Furthermore, building on this theorem prover enables me to develop a self-learning algorithm that can learn the conditions for a program’s correctness according to a given solution program.
In this dissertation, I propose a novel framework for performing semantic autograding, which analyzes student programs at a semantic level to help students learn with additional and systematic help. A general autograder is not practical for general programming languages, due to the flexibility of semantics. A practical autograder is possible in VIPLE, because of its simplified syntax and restricted options of semantics. The design of this autograder is based on the concept of theorem provers. To achieve this goal, I employ a modified version of Pi-Calculus to represent VIPLE programs and Hoare Logic to formalize program requirements. By building on the inference rules of Pi-Calculus and Hoare Logic, I am able to construct a theorem prover that can perform automated semantic analysis. Furthermore, building on this theorem prover enables me to develop a self-learning algorithm that can learn the conditions for a program’s correctness according to a given solution program.
ContributorsDe Luca, Gennaro (Author) / Chen, Yinong (Thesis advisor) / Liu, Huan (Thesis advisor) / Hsiao, Sharon (Committee member) / Huang, Dijiang (Committee member) / Arizona State University (Publisher)
Created2020
Description
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.
ContributorsMarple, Sam (Author) / Yau, Sik-Sang (Thesis advisor) / Huang, Dijiang (Committee member) / Trieu, Ni (Committee member) / Arizona State University (Publisher)
Created2021
Description
Machine learning tutorials often employ an application and runtime specific solution for a given problem in which users are expected to have a broad understanding of data analysis and software programming. This thesis focuses on designing and implementing a new, hands-on approach to teaching machine learning by streamlining the process of generating Inertial Movement Unit (IMU) data from multirotor flight sessions, training a linear classifier, and applying said classifier to solve Multi-rotor Activity Recognition (MAR) problems in an online lab setting. MAR labs leverage cloud computing and data storage technologies to host a versatile environment capable of logging, orchestrating, and visualizing the solution for an MAR problem through a user interface. MAR labs extends Arizona State University’s Visual IoT/Robotics Programming Language Environment (VIPLE) as a control platform for multi-rotors used in data collection. VIPLE is a platform developed for teaching computational thinking, visual programming, Internet of Things (IoT) and robotics application development. As a part of this education platform, this work also develops a 3D simulator capable of simulating the programmable behaviors of a robot within a maze environment and builds a physical quadrotor for use in MAR lab experiments.
ContributorsDe La Rosa, Matthew Lee (Author) / Chen, Yinong (Thesis advisor) / Collofello, James (Committee member) / Huang, Dijiang (Committee member) / Arizona State University (Publisher)
Created2018