Matching Items (16)
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- All Subjects: Cloud Computing
- All Subjects: Computer Engineering
- Creators: Ahn, Gail-Joon
Description
With the growth of IT products and sophisticated software in various operating systems, I observe that security risks in systems are skyrocketing constantly. Consequently, Security Assessment is now considered as one of primary security mechanisms to measure assurance of systems since systems that are not compliant with security requirements may lead adversaries to access critical information by circumventing security practices. In order to ensure security, considerable efforts have been spent to develop security regulations by facilitating security best-practices. Applying shared security standards to the system is critical to understand vulnerabilities and prevent well-known threats from exploiting vulnerabilities. However, many end users tend to change configurations of their systems without paying attention to the security. Hence, it is not straightforward to protect systems from being changed by unconscious users in a timely manner. Detecting the installation of harmful applications is not sufficient since attackers may exploit risky software as well as commonly used software. In addition, checking the assurance of security configurations periodically is disadvantageous in terms of time and cost due to zero-day attacks and the timing attacks that can leverage the window between each security checks. Therefore, event-driven monitoring approach is critical to continuously assess security of a target system without ignoring a particular window between security checks and lessen the burden of exhausted task to inspect the entire configurations in the system. Furthermore, the system should be able to generate a vulnerability report for any change initiated by a user if such changes refer to the requirements in the standards and turn out to be vulnerable. Assessing various systems in distributed environments also requires to consistently applying standards to each environment. Such a uniformed consistent assessment is important because the way of assessment approach for detecting security vulnerabilities may vary across applications and operating systems. In this thesis, I introduce an automated event-driven security assessment framework to overcome and accommodate the aforementioned issues. I also discuss the implementation details that are based on the commercial-off-the-self technologies and testbed being established to evaluate approach. Besides, I describe evaluation results that demonstrate the effectiveness and practicality of the approaches.
ContributorsSeo, Jeong-Jin (Author) / Ahn, Gail-Joon (Thesis advisor) / Yau, Stephen S. (Committee member) / Lee, Joohyung (Committee member) / Arizona State University (Publisher)
Created2014
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
Attribute Based Access Control (ABAC) mechanisms have been attracting a lot of interest from the research community in recent times. This is especially because of the flexibility and extensibility it provides by using attributes assigned to subjects as the basis for access control. ABAC enables an administrator of a server to enforce access policies on the data, services and other such resources fairly easily. It also accommodates new policies and changes to existing policies gracefully, thereby making it a potentially good mechanism for implementing access control in large systems, particularly in today's age of Cloud Computing. However management of the attributes in ABAC environment is an area that has been little touched upon. Having a mechanism to allow multiple ABAC based systems to share data and resources can go a long way in making ABAC scalable. At the same time each system should be able to specify their own attribute sets independently. In the research presented in this document a new mechanism is proposed that would enable users to share resources and data in a cloud environment using ABAC techniques in a distributed manner. The focus is mainly on decentralizing the access policy specifications for the shared data so that each data owner can specify the access policy independent of others. The concept of ontologies and semantic web is introduced in the ABAC paradigm that would help in giving a scalable structure to the attributes and also allow systems having different sets of attributes to communicate and share resources.
ContributorsPrabhu Verleker, Ashwin Narayan (Author) / Huang, Dijiang (Thesis advisor) / Ahn, Gail-Joon (Committee member) / Dasgupta, Partha (Committee member) / Arizona State University (Publisher)
Created2014
Description
With the increasing user demand for low latency, elastic provisioning of computing resources coupled with ubiquitous and on-demand access to real-time data, cloud computing has emerged as a popular computing paradigm to meet growing user demands. However, with the introduction and rising use of wear- able technology and evolving uses of smart-phones, the concept of Internet of Things (IoT) has become a prevailing notion in the currently growing technology industry. Cisco Inc. has projected a data creation of approximately 403 Zetabytes (ZB) by 2018. The combination of bringing benign devices and connecting them to the web has resulted in exploding service and data aggregation requirements, thus requiring a new and innovative computing platform. This platform should have the capability to provide robust real-time data analytics and resource provisioning to clients, such as IoT users, on-demand. Such a computation model would need to function at the edge-of-the-network, forming a bridge between the large cloud data centers and the distributed connected devices.
This research expands on the notion of bringing computational power to the edge- of-the-network, and then integrating it with the cloud computing paradigm whilst providing services to diverse IoT-based applications. This expansion is achieved through the establishment of a new computing model that serves as a platform for IoT-based devices to communicate with services in real-time. We name this paradigm as Gateway-Oriented Reconfigurable Ecosystem (GORE) computing. Finally, this thesis proposes and discusses the development of a policy management framework for accommodating our proposed computational paradigm. The policy framework is designed to serve both the hosted applications and the GORE paradigm by enabling them to function more efficiently. The goal of the framework is to ensure uninterrupted communication and service delivery between users and their applications.
This research expands on the notion of bringing computational power to the edge- of-the-network, and then integrating it with the cloud computing paradigm whilst providing services to diverse IoT-based applications. This expansion is achieved through the establishment of a new computing model that serves as a platform for IoT-based devices to communicate with services in real-time. We name this paradigm as Gateway-Oriented Reconfigurable Ecosystem (GORE) computing. Finally, this thesis proposes and discusses the development of a policy management framework for accommodating our proposed computational paradigm. The policy framework is designed to serve both the hosted applications and the GORE paradigm by enabling them to function more efficiently. The goal of the framework is to ensure uninterrupted communication and service delivery between users and their applications.
ContributorsDsouza, Clinton (Author) / Ahn, Gail-Joon (Thesis advisor) / Doupe, Adam (Committee member) / Dasgupta, Partha (Committee member) / Arizona State University (Publisher)
Created2015
Description
Users often join an online social networking (OSN) site, like Facebook, to remain social, by either staying connected with friends or expanding social networks. On an OSN site, users generally share variety of personal information which is often expected to be visible to their friends, but sometimes vulnerable to unwarranted access from others. The recent study suggests that many personal attributes, including religious and political affiliations, sexual orientation, relationship status, age, and gender, are predictable using users' personal data from an OSN site. The majority of users want to remain socially active, and protect their personal data at the same time. This tension leads to a user's vulnerability, allowing privacy attacks which can cause physical and emotional distress to a user, sometimes with dire consequences. For example, stalkers can make use of personal information available on an OSN site to their personal gain. This dissertation aims to systematically study a user vulnerability against such privacy attacks.
A user vulnerability can be managed in three steps: (1) identifying, (2) measuring and (3) reducing a user vulnerability. Researchers have long been identifying vulnerabilities arising from user's personal data, including user names, demographic attributes, lists of friends, wall posts and associated interactions, multimedia data such as photos, audios and videos, and tagging of friends. Hence, this research first proposes a way to measure and reduce a user vulnerability to protect such personal data. This dissertation also proposes an algorithm to minimize a user's vulnerability while maximizing their social utility values.
To address these vulnerability concerns, social networking sites like Facebook usually let their users to adjust their profile settings so as to make some of their data invisible. However, users sometimes interact with others using unprotected posts (e.g., posts from a ``Facebook page\footnote{The term ''Facebook page`` refers to the page which are commonly dedicated for businesses, brands and organizations to share their stories and connect with people.}''). Such interactions help users to become more social and are publicly accessible to everyone. Thus, visibilities of these interactions are beyond the control of their profile settings. I explore such unprotected interactions so that users' are well aware of these new vulnerabilities and adopt measures to mitigate them further. In particular, {\em are users' personal attributes predictable using only the unprotected interactions}? To answer this question, I address a novel problem of predictability of users' personal attributes with unprotected interactions. The extreme sparsity patterns in users' unprotected interactions pose a serious challenge. Therefore, I approach to mitigating the data sparsity challenge by designing a novel attribute prediction framework using only the unprotected interactions. Experimental results on Facebook dataset demonstrates that the proposed framework can predict users' personal attributes.
A user vulnerability can be managed in three steps: (1) identifying, (2) measuring and (3) reducing a user vulnerability. Researchers have long been identifying vulnerabilities arising from user's personal data, including user names, demographic attributes, lists of friends, wall posts and associated interactions, multimedia data such as photos, audios and videos, and tagging of friends. Hence, this research first proposes a way to measure and reduce a user vulnerability to protect such personal data. This dissertation also proposes an algorithm to minimize a user's vulnerability while maximizing their social utility values.
To address these vulnerability concerns, social networking sites like Facebook usually let their users to adjust their profile settings so as to make some of their data invisible. However, users sometimes interact with others using unprotected posts (e.g., posts from a ``Facebook page\footnote{The term ''Facebook page`` refers to the page which are commonly dedicated for businesses, brands and organizations to share their stories and connect with people.}''). Such interactions help users to become more social and are publicly accessible to everyone. Thus, visibilities of these interactions are beyond the control of their profile settings. I explore such unprotected interactions so that users' are well aware of these new vulnerabilities and adopt measures to mitigate them further. In particular, {\em are users' personal attributes predictable using only the unprotected interactions}? To answer this question, I address a novel problem of predictability of users' personal attributes with unprotected interactions. The extreme sparsity patterns in users' unprotected interactions pose a serious challenge. Therefore, I approach to mitigating the data sparsity challenge by designing a novel attribute prediction framework using only the unprotected interactions. Experimental results on Facebook dataset demonstrates that the proposed framework can predict users' personal attributes.
ContributorsGundecha, Pritam S (Author) / Liu, Huan (Thesis advisor) / Ahn, Gail-Joon (Committee member) / Ye, Jieping (Committee member) / Barbier, Geoffrey (Committee member) / Arizona State University (Publisher)
Created2015
Description
This dissertation is focused on building scalable Attribute Based Security Systems (ABSS), including efficient and privacy-preserving attribute based encryption schemes and applications to group communications and cloud computing. First of all, a Constant Ciphertext Policy Attribute Based Encryption (CCP-ABE) is proposed. Existing Attribute Based Encryption (ABE) schemes usually incur large, linearly increasing ciphertext. The proposed CCP-ABE dramatically reduces the ciphertext to small, constant size. This is the first existing ABE scheme that achieves constant ciphertext size. Also, the proposed CCP-ABE scheme is fully collusion-resistant such that users can not combine their attributes to elevate their decryption capacity. Next step, efficient ABE schemes are applied to construct optimal group communication schemes and broadcast encryption schemes. An attribute based Optimal Group Key (OGK) management scheme that attains communication-storage optimality without collusion vulnerability is presented. Then, a novel broadcast encryption model: Attribute Based Broadcast Encryption (ABBE) is introduced, which exploits the many-to-many nature of attributes to dramatically reduce the storage complexity from linear to logarithm and enable expressive attribute based access policies. The privacy issues are also considered and addressed in ABSS. Firstly, a hidden policy based ABE schemes is proposed to protect receivers' privacy by hiding the access policy. Secondly,a new concept: Gradual Identity Exposure (GIE) is introduced to address the restrictions of hidden policy based ABE schemes. GIE's approach is to reveal the receivers' information gradually by allowing ciphertext recipients to decrypt the message using their possessed attributes one-by-one. If the receiver does not possess one attribute in this procedure, the rest of attributes are still hidden. Compared to hidden-policy based solutions, GIE provides significant performance improvement in terms of reducing both computation and communication overhead. Last but not least, ABSS are incorporated into the mobile cloud computing scenarios. In the proposed secure mobile cloud data management framework, the light weight mobile devices can securely outsource expensive ABE operations and data storage to untrusted cloud service providers. The reported scheme includes two components: (1) a Cloud-Assisted Attribute-Based Encryption/Decryption (CA-ABE) scheme and (2) An Attribute-Based Data Storage (ABDS) scheme that achieves information theoretical optimality.
ContributorsZhou, Zhibin (Author) / Huang, Dijiang (Thesis advisor) / Yau, Sik-Sang (Committee member) / Ahn, Gail-Joon (Committee member) / Reisslein, Martin (Committee member) / Arizona State University (Publisher)
Created2011
Description
In modern healthcare environments, there is a strong need to create an infrastructure that reduces time-consuming efforts and costly operations to obtain a patient's complete medical record and uniformly integrates this heterogeneous collection of medical data to deliver it to the healthcare professionals. As a result, healthcare providers are more willing to shift their electronic medical record (EMR) systems to clouds that can remove the geographical distance barriers among providers and patient. Even though cloud-based EMRs have received considerable attention since it would help achieve lower operational cost and better interoperability with other healthcare providers, the adoption of security-aware cloud systems has become an extremely important prerequisite for bringing interoperability and efficient management to the healthcare industry. Since a shared electronic health record (EHR) essentially represents a virtualized aggregation of distributed clinical records from multiple healthcare providers, sharing of such integrated EHRs may comply with various authorization policies from these data providers. In this work, we focus on the authorized and selective sharing of EHRs among several parties with different duties and objectives that satisfies access control and compliance issues in healthcare cloud computing environments. We present a secure medical data sharing framework to support selective sharing of composite EHRs aggregated from various healthcare providers and compliance of HIPAA regulations. Our approach also ensures that privacy concerns need to be accommodated for processing access requests to patients' healthcare information. To realize our proposed approach, we design and implement a cloud-based EHRs sharing system. In addition, we describe case studies and evaluation results to demonstrate the effectiveness and efficiency of our approach.
ContributorsWu, Ruoyu (Author) / Ahn, Gail-Joon (Thesis advisor) / Yau, Stephen S. (Committee member) / Huang, Dijiang (Committee member) / Arizona State University (Publisher)
Created2012
Description
Cloud computing is known as a new and powerful computing paradigm. This new generation of network computing model delivers both software and hardware as on-demand resources and various services over the Internet. However, the security concerns prevent users from adopting the cloud-based solutions to fulfill the IT requirement for many business critical computing. Due to the resource-sharing and multi-tenant nature of cloud-based solutions, cloud security is especially the most concern in the Infrastructure as a Service (IaaS). It has been attracting a lot of research and development effort in the past few years.
Virtualization is the main technology of cloud computing to enable multi-tenancy.
Computing power, storage, and network are all virtualizable to be shared in an IaaS system. This important technology makes abstract infrastructure and resources available to users as isolated virtual machines (VMs) and virtual networks (VNs). However, it also increases vulnerabilities and possible attack surfaces in the system, since all users in a cloud share these resources with others or even the attackers. The promising protection mechanism is required to ensure strong isolation, mediated sharing, and secure communications between VMs. Technologies for detecting anomalous traffic and protecting normal traffic in VNs are also needed. Therefore, how to secure and protect the private traffic in VNs and how to prevent the malicious traffic from shared resources are major security research challenges in a cloud system.
This dissertation proposes four novel frameworks to address challenges mentioned above. The first work is a new multi-phase distributed vulnerability, measurement, and countermeasure selection mechanism based on the attack graph analytical model. The second work is a hybrid intrusion detection and prevention system to protect VN and VM using virtual machines introspection (VMI) and software defined networking (SDN) technologies. The third work further improves the previous works by introducing a VM profiler and VM Security Index (VSI) to keep track the security status of each VM and suggest the optimal countermeasure to mitigate potential threats. The final work is a SDN-based proactive defense mechanism for a cloud system using a reconfiguration model and moving target defense approaches to actively and dynamically change the virtual network configuration of a cloud system.
Virtualization is the main technology of cloud computing to enable multi-tenancy.
Computing power, storage, and network are all virtualizable to be shared in an IaaS system. This important technology makes abstract infrastructure and resources available to users as isolated virtual machines (VMs) and virtual networks (VNs). However, it also increases vulnerabilities and possible attack surfaces in the system, since all users in a cloud share these resources with others or even the attackers. The promising protection mechanism is required to ensure strong isolation, mediated sharing, and secure communications between VMs. Technologies for detecting anomalous traffic and protecting normal traffic in VNs are also needed. Therefore, how to secure and protect the private traffic in VNs and how to prevent the malicious traffic from shared resources are major security research challenges in a cloud system.
This dissertation proposes four novel frameworks to address challenges mentioned above. The first work is a new multi-phase distributed vulnerability, measurement, and countermeasure selection mechanism based on the attack graph analytical model. The second work is a hybrid intrusion detection and prevention system to protect VN and VM using virtual machines introspection (VMI) and software defined networking (SDN) technologies. The third work further improves the previous works by introducing a VM profiler and VM Security Index (VSI) to keep track the security status of each VM and suggest the optimal countermeasure to mitigate potential threats. The final work is a SDN-based proactive defense mechanism for a cloud system using a reconfiguration model and moving target defense approaches to actively and dynamically change the virtual network configuration of a cloud system.
ContributorsChung, Chun-Jen (Author) / Huang, Dijiang (Thesis advisor) / Ahn, Gail-Joon (Committee member) / Xue, Guoliang (Committee member) / Zhang, Yanchao (Committee member) / Arizona State University (Publisher)
Created2015
Description
Reasoning about the activities of cyber threat actors is critical to defend against cyber
attacks. However, this task is difficult for a variety of reasons. In simple terms, it is difficult
to determine who the attacker is, what the desired goals are of the attacker, and how they will
carry out their attacks. These three questions essentially entail understanding the attacker’s
use of deception, the capabilities available, and the intent of launching the attack. These
three issues are highly inter-related. If an adversary can hide their intent, they can better
deceive a defender. If an adversary’s capabilities are not well understood, then determining
what their goals are becomes difficult as the defender is uncertain if they have the necessary
tools to accomplish them. However, the understanding of these aspects are also mutually
supportive. If we have a clear picture of capabilities, intent can better be deciphered. If we
understand intent and capabilities, a defender may be able to see through deception schemes.
In this dissertation, I present three pieces of work to tackle these questions to obtain
a better understanding of cyber threats. First, we introduce a new reasoning framework
to address deception. We evaluate the framework by building a dataset from DEFCON
capture-the-flag exercise to identify the person or group responsible for a cyber attack.
We demonstrate that the framework not only handles cases of deception but also provides
transparent decision making in identifying the threat actor. The second task uses a cognitive
learning model to determine the intent – goals of the threat actor on the target system.
The third task looks at understanding the capabilities of threat actors to target systems by
identifying at-risk systems from hacker discussions on darkweb websites. To achieve this
task we gather discussions from more than 300 darkweb websites relating to malicious
hacking.
attacks. However, this task is difficult for a variety of reasons. In simple terms, it is difficult
to determine who the attacker is, what the desired goals are of the attacker, and how they will
carry out their attacks. These three questions essentially entail understanding the attacker’s
use of deception, the capabilities available, and the intent of launching the attack. These
three issues are highly inter-related. If an adversary can hide their intent, they can better
deceive a defender. If an adversary’s capabilities are not well understood, then determining
what their goals are becomes difficult as the defender is uncertain if they have the necessary
tools to accomplish them. However, the understanding of these aspects are also mutually
supportive. If we have a clear picture of capabilities, intent can better be deciphered. If we
understand intent and capabilities, a defender may be able to see through deception schemes.
In this dissertation, I present three pieces of work to tackle these questions to obtain
a better understanding of cyber threats. First, we introduce a new reasoning framework
to address deception. We evaluate the framework by building a dataset from DEFCON
capture-the-flag exercise to identify the person or group responsible for a cyber attack.
We demonstrate that the framework not only handles cases of deception but also provides
transparent decision making in identifying the threat actor. The second task uses a cognitive
learning model to determine the intent – goals of the threat actor on the target system.
The third task looks at understanding the capabilities of threat actors to target systems by
identifying at-risk systems from hacker discussions on darkweb websites. To achieve this
task we gather discussions from more than 300 darkweb websites relating to malicious
hacking.
ContributorsNunes, Eric (Author) / Shakarian, Paulo (Thesis advisor) / Ahn, Gail-Joon (Committee member) / Baral, Chitta (Committee member) / Cooke, Nancy J. (Committee member) / Arizona State University (Publisher)
Created2018
Description
With the software-defined networking trend growing, several network virtualization controllers have been developed in recent years. These controllers, also called network hypervisors, attempt to manage physical SDN based networks so that multiple tenants can safely share the same forwarding plane hardware without risk of being affected by or affecting other tenants. However, many areas remain unexplored by current network hypervisor implementations. This thesis presents and evaluates some of the features offered by network hypervisors, such as full header space availability, isolation, and transparent traffic forwarding capabilities for tenants. Flow setup time and throughput are also measured and compared among different network hypervisors. Three different network hypervisors are evaluated: FlowVisor, VeRTIGO and OpenVirteX. These virtualization tools are assessed with experiments conducted on three different testbeds: an emulated Mininet scenario, a physical single-switch testbed, and also a remote GENI testbed. The results indicate that network hypervisors bring SDN flexibility to network virtualization, making it easier for network administrators to define with precision how the network is sliced and divided among tenants. This increased flexibility, however, may come with the cost of decreased performance, and also brings additional risks of interoperability due to a lack of standardization of virtualization methods.
ContributorsStall Rechia, Felipe (Author) / Syrotiuk, Violet R. (Thesis advisor) / Ahn, Gail-Joon (Committee member) / Huang, Dijiang (Committee member) / Arizona State University (Publisher)
Created2016