ASU Electronic Theses and Dissertations
This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.
In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.
Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.
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- All Subjects: Cybersecurity
- Creators: Xue, Guoliang
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
The omnipresent data, growing number of network devices, and evolving attack techniques have been challenging organizations’ security defenses over the past decade. With humongous volumes of logs generated by those network devices, looking for patterns of malicious activities and identifying them in time is growing beyond the capabilities of their defense systems. Deep Learning, a subset of Machine Learning (ML) and Artificial Intelligence (AI), fills in this gapwith its ability to learn from huge amounts of data, and improve its performance as the data it learns from increases. In this dissertation, I bring forward security issues pertaining to two top threats that most organizations fear, Advanced Persistent Threat (APT), and Distributed Denial of Service (DDoS), along with deep learning models built towards addressing those
security issues. First, I present a deep learning model, APT Detection, capable of detecting anomalous activities in a system. Evaluation of this model demonstrates how it can contribute to early detection of an APT attack with an Area Under the Curve (AUC) of up to 91% on a Receiver Operating Characteristic (ROC) curve. Second, I present DAPT2020, a first of its kind dataset capturing an APT attack exploiting web and system vulnerabilities in an emulated organization’s production network. Evaluation of the dataset using well known machine learning models demonstrates the need for better deep learning models to detect APT attacks. I then present DAPT2021, a semi-synthetic dataset capturing an APT attackexploiting human vulnerabilities, alongside 2 less skilled attacks. By emulating the normal behavior of the employees in a set target organization, DAPT2021 has been created to enable researchers study the causations and correlations among the captured data, a much-needed information to detect an underlying threat early. Finally, I present a distributed defense
framework, SmartDefense, that can detect and mitigate over 90% of DDoS traffic at the source and over 97.5% of the remaining DDoS traffic at the Internet Service Provider’s (ISP’s) edge network. Evaluation of this work shows how by using attributes sent by customer edge network, SmartDefense can further help ISPs prevent up to 51.95% of the DDoS traffic
from going to the destination.
ContributorsMyneni, Sowmya (Author) / Xue, Guoliang (Thesis advisor) / Doupe, Adam (Committee member) / Li, Baoxin (Committee member) / Baral, Chitta (Committee member) / Arizona State University (Publisher)
Created2022