Matching Items (5)
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- All Subjects: Phishing
- Creators: Ahn, Gail-Joon
Description
Answer Set Programming (ASP) is one of the most prominent and successful knowledge representation paradigms. The success of ASP is due to its expressive non-monotonic modeling language and its efficient computational methods originating from building propositional satisfiability solvers. The wide adoption of ASP has motivated several extensions to its modeling language in order to enhance expressivity, such as incorporating aggregates and interfaces with ontologies. Also, in order to overcome the grounding bottleneck of computation in ASP, there are increasing interests in integrating ASP with other computing paradigms, such as Constraint Programming (CP) and Satisfiability Modulo Theories (SMT). Due to the non-monotonic nature of the ASP semantics, such enhancements turned out to be non-trivial and the existing extensions are not fully satisfactory. We observe that one main reason for the difficulties rooted in the propositional semantics of ASP, which is limited in handling first-order constructs (such as aggregates and ontologies) and functions (such as constraint variables in CP and SMT) in natural ways. This dissertation presents a unifying view on these extensions by viewing them as instances of formulas with generalized quantifiers and intensional functions. We extend the first-order stable model semantics by by Ferraris, Lee, and Lifschitz to allow generalized quantifiers, which cover aggregate, DL-atoms, constraints and SMT theory atoms as special cases. Using this unifying framework, we study and relate different extensions of ASP. We also present a tight integration of ASP with SMT, based on which we enhance action language C+ to handle reasoning about continuous changes. Our framework yields a systematic approach to study and extend non-monotonic languages.
ContributorsMeng, Yunsong (Author) / Lee, Joohyung (Thesis advisor) / Ahn, Gail-Joon (Committee member) / Baral, Chitta (Committee member) / Fainekos, Georgios (Committee member) / Lifschitz, Vladimir (Committee member) / Arizona State University (Publisher)
Created2013
Description
Different logic-based knowledge representation formalisms have different limitations either with respect to expressivity or with respect to computational efficiency. First-order logic, which is the basis of Description Logics (DLs), is not suitable for defeasible reasoning due to its monotonic nature. The nonmonotonic formalisms that extend first-order logic, such as circumscription and default logic, are expressive but lack efficient implementations. The nonmonotonic formalisms that are based on the declarative logic programming approach, such as Answer Set Programming (ASP), have efficient implementations but are not expressive enough for representing and reasoning with open domains. This dissertation uses the first-order stable model semantics, which extends both first-order logic and ASP, to relate circumscription to ASP, and to integrate DLs and ASP, thereby partially overcoming the limitations of the formalisms. By exploiting the relationship between circumscription and ASP, well-known action formalisms, such as the situation calculus, the event calculus, and Temporal Action Logics, are reformulated in ASP. The advantages of these reformulations are shown with respect to the generality of the reasoning tasks that can be handled and with respect to the computational efficiency. The integration of DLs and ASP presented in this dissertation provides a framework for integrating rules and ontologies for the semantic web. This framework enables us to perform nonmonotonic reasoning with DL knowledge bases. Observing the need to integrate action theories and ontologies, the above results are used to reformulate the problem of integrating action theories and ontologies as a problem of integrating rules and ontologies, thus enabling us to use the computational tools developed in the context of the latter for the former.
ContributorsPalla, Ravi (Author) / Lee, Joohyung (Thesis advisor) / Baral, Chitta (Committee member) / Kambhampati, Subbarao (Committee member) / Lifschitz, Vladimir (Committee member) / Arizona State University (Publisher)
Created2012
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