Matching Items (6)
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- All Subjects: Phishing
- Creators: Ahn, Gail-Joon
- Resource Type: Text
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
In this dissertation, two interrelated problems of service-based systems (SBS) are addressed: protecting users' data confidentiality from service providers, and managing performance of multiple workflows in SBS. Current SBSs pose serious limitations to protecting users' data confidentiality. Since users' sensitive data is sent in unencrypted forms to remote machines owned and operated by third-party service providers, there are risks of unauthorized use of the users' sensitive data by service providers. Although there are many techniques for protecting users' data from outside attackers, currently there is no effective way to protect users' sensitive data from service providers. In this dissertation, an approach is presented to protecting the confidentiality of users' data from service providers, and ensuring that service providers cannot collect users' confidential data while the data is processed or stored in cloud computing systems. The approach has four major features: (1) separation of software service providers and infrastructure service providers, (2) hiding the information of the owners of data, (3) data obfuscation, and (4) software module decomposition and distributed execution. Since the approach to protecting users' data confidentiality includes software module decomposition and distributed execution, it is very important to effectively allocate the resource of servers in SBS to each of the software module to manage the overall performance of workflows in SBS. An approach is presented to resource allocation for SBS to adaptively allocating the system resources of servers to their software modules in runtime in order to satisfy the performance requirements of multiple workflows in SBS. Experimental results show that the dynamic resource allocation approach can substantially increase the throughput of a SBS and the optimal resource allocation can be found in polynomial time
ContributorsAn, Ho Geun (Author) / Yau, Sik-Sang (Thesis advisor) / Huang, Dijiang (Committee member) / Ahn, Gail-Joon (Committee member) / Santanam, Raghu (Committee member) / Arizona State University (Publisher)
Created2012
Description
The purpose of this project was to implement and analyze a new proposed rootkit that claims a greater level of stealth by hiding in cache. Today, the vast majority of embedded devices are powered by ARM processors. To protect their processors from attacks, ARM introduced a hardware security extension known as TrustZone. It provides an isolated execution environment within the embedded device that enables us to run various memory integrity and malware detection tools to identify possible breaches in security to the normal world. Although TrustZone provides this additional layer of security, it also adds another layer of complexity, and thus comes with its own set of vulnerabilities. This new rootkit identifies and exploits a cache incoherence in the ARM device as a result of TrustZone. The newly proposed rootkit, called CacheKit, takes advantage of this cache incoherence to avoid memory introspection from tools in secure world. We implement CacheKit on the i.MX53 development board, which features a single ARM Cortex A8 processor, to analyze the limitations and vulnerabilities described in the original paper. We set up the Linux environment on the computer to be able to cross-compile for the development board which will be running the FreeScale android 2.3.4 platform with a 2.6.33 Linux kernel. The project is implemented as a kernel module that once installed on the board can manipulate cache as desired to conceal the rootkit. The module exploits the fact that in TrustZone, the secure world does not have access to the normal world cache. First, a technique known as Cache-asRAM is used to ensure that the rootkit is loaded only into cache of the normal world where it can avoid detection from the secure world. Then, we employ the cache maintenance instructions and resisters provided in the cp15 coprocessor to keep the code persistent in cache. Furthermore, the cache lines are mapped to unused I/O address space so that if cache content is flushed to RAM for inspection, the data is simply lost. This ensures that even if the rootkit were to be flushed into memory, any trace of the malicious code would be lost. CacheKit prevents defenders from analyzing the code and destroys any forensic evidence. This provides attackers with a new and powerful tool that is excellent for certain scenarios that were previously thought to be secure. Finally, we determine the limitations of the prototype to determine possible areas for future growth and research into the security of networked embedded devices.
ContributorsGutierrez Barnett, Mauricio Antonio (Author) / Zhao, Ziming (Thesis director) / Doupe, Adam (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12
Description
Phishing is one of most common and effective attack vectors in modern cybercrime. Rather than targeting a technical vulnerability in a computer system, phishing attacks target human behavioral or emotional tendencies through manipulative emails, text messages, or phone calls. Through PyAntiPhish, I attempt to create my own version of an anti-phishing solution, through a series of experiments testing different machine learning classifiers and URL features. With an end-goal implementation as a Chromium browser extension utilizing Python-based machine learning classifiers (those available via the scikit-learn library), my project uses a combination of Python, TypeScript, Node.js, as well as AWS Lambda and API Gateway to act as a solution capable of blocking phishing attacks from the web browser.
ContributorsYang, Branden (Author) / Osburn, Steven (Thesis director) / Malpe, Adwith (Committee member) / Ahn, Gail-Joon (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2024-05
Description
Despite an abundance of defenses that work to protect Internet users from online threats, malicious actors continue deploying relentless large-scale phishing attacks that target these users. Effectively mitigating phishing attacks remains a challenge for the security community due to attackers' ability to evolve and adapt to defenses, the cross-organizational nature of the infrastructure abused for phishing, and discrepancies between theoretical and realistic anti-phishing systems. Although technical countermeasures cannot always compensate for the human weakness exploited by social engineers, maintaining a clear and up-to-date understanding of the motivation behind---and execution of---modern phishing attacks is essential to optimizing such countermeasures.
In this dissertation, I analyze the state of the anti-phishing ecosystem and show that phishers use evasion techniques, including cloaking, to bypass anti-phishing mitigations in hopes of maximizing the return-on-investment of their attacks. I develop three novel, scalable data-collection and analysis frameworks to pinpoint the ecosystem vulnerabilities that sophisticated phishing websites exploit. The frameworks, which operate on real-world data and are designed for continuous deployment by anti-phishing organizations, empirically measure the robustness of industry-standard anti-phishing blacklists (PhishFarm and PhishTime) and proactively detect and map phishing attacks prior to launch (Golden Hour). Using these frameworks, I conduct a longitudinal study of blacklist performance and the first large-scale end-to-end analysis of phishing attacks (from spamming through monetization). As a result, I thoroughly characterize modern phishing websites and identify desirable characteristics for enhanced anti-phishing systems, such as more reliable methods for the ecosystem to collectively detect phishing websites and meaningfully share the corresponding intelligence. In addition, findings from these studies led to actionable security recommendations that were implemented by key organizations within the ecosystem to help improve the security of Internet users worldwide.
In this dissertation, I analyze the state of the anti-phishing ecosystem and show that phishers use evasion techniques, including cloaking, to bypass anti-phishing mitigations in hopes of maximizing the return-on-investment of their attacks. I develop three novel, scalable data-collection and analysis frameworks to pinpoint the ecosystem vulnerabilities that sophisticated phishing websites exploit. The frameworks, which operate on real-world data and are designed for continuous deployment by anti-phishing organizations, empirically measure the robustness of industry-standard anti-phishing blacklists (PhishFarm and PhishTime) and proactively detect and map phishing attacks prior to launch (Golden Hour). Using these frameworks, I conduct a longitudinal study of blacklist performance and the first large-scale end-to-end analysis of phishing attacks (from spamming through monetization). As a result, I thoroughly characterize modern phishing websites and identify desirable characteristics for enhanced anti-phishing systems, such as more reliable methods for the ecosystem to collectively detect phishing websites and meaningfully share the corresponding intelligence. In addition, findings from these studies led to actionable security recommendations that were implemented by key organizations within the ecosystem to help improve the security of Internet users worldwide.
ContributorsOest, Adam (Author) / Ahn, Gail-Joon (Thesis advisor) / Doupe, Adam (Thesis advisor) / Shoshitaishvili, Yan (Committee member) / Johnson, RC (Committee member) / Arizona State University (Publisher)
Created2020
Description
Despite extensive research by the security community, cyberattacks such as phishing and Internet of Things (IoT) attacks remain profitable to criminals and continue to cause substantial damage not only to the victim users that they target, but also the organizations they impersonate. In recent years, phishing websites have taken the place of malware websites as the most prevalent web-based threat. Even though technical countermeasures effectively mitigate web-based malware, phishing websites continue to grow in sophistication and successfully slip past modern defenses. Phishing attack and its countermeasure have entered into a new era, where one side has upgraded their weapon, attempting to conquer the other. In addition, the amount and usage of IoT devices increases rapidly because of the development and deployment of 5G network. Although researchers have proposed secure execution environment, attacks targeting those devices can often succeed. Therefore, the security community desperately needs detection and prevention methodologies to fight against phishing and IoT attacks. In this dissertation, I design a framework, named CrawlPhish, to understand the prevalence and nature of such sophistications, including cloaking, in phishing attacks, which evade detections from the anti-phishing ecosystem by distinguishing the traffic between a crawler and a real Internet user and hence maximize the return-on-investment from phishing attacks. CrawlPhish also detects and categorizes client-side cloaking techniques in phishing with scalability and automation. Furthermore, I focus on the analysis redirection abuse in advanced phishing websites and hence propose mitigations to classify malicious redirection use via machine learning algorithms. Based on the observations from previous work, from the perspective of prevention, I design a novel anti-phishing system called Spartacus that can be deployed from the user end to completely neutralize phishing attacks.
Lastly, inspired by Spartacus, I propose iCore, which proactively monitors the operations in the trusted execution environment to identify any maliciousness.
ContributorsZhang, Penghui (Author) / Ahn, Gail-Joon (Thesis advisor) / Doupe, Adam (Thesis advisor) / Oest, Adam (Committee member) / Kapravelos, Alexandros (Committee member) / Arizona State University (Publisher)
Created2022