Matching Items (38)
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- All Subjects: Computer Science
- Genre: Masters Thesis
- Creators: Doupe, Adam
Description
Honeypots – cyber deception technique used to lure attackers into a trap. They contain fake confidential information to make an attacker believe that their attack has been successful. One of the prerequisites for a honeypot to be effective is that it needs to be undetectable. Deploying sniffing and event logging tools alongside the honeypot also helps understand the mindset of the attacker after successful attacks. Is there any data that backs up the claim that honeypots are effective in real life scenarios? The answer is no.Game-theoretic models have been helpful to approximate attacker and defender actions in cyber security. However, in the past these models have relied on expert- created data. The goal of this research project is to determine the effectiveness of honeypots using real-world data. So, how to deploy effective honeypots? This is where honey-patches come into play. Honey-patches are software patches designed to hinder the attacker’s ability to determine whether an attack has been successful or not. When an attacker launches a successful attack on a software, the honey-patch transparently redirects the attacker into a honeypot. The honeypot contains fake information which makes the attacker believe they were successful while in reality they were not.
After conducting a series of experiments and analyzing the results, there is a clear indication that honey-patches are not the perfect application security solution having both pros and cons.
ContributorsChauhan, Purv Rakeshkumar (Author) / Doupe, Adam (Thesis advisor) / Bao, Youzhi (Committee member) / Wang, Ruoyu (Committee member) / Arizona State University (Publisher)
Created2022
Description
The rise in popularity of applications and services that charge for access to proprietary trained models has led to increased interest in the robustness of these models and the security of the environments in which inference is conducted. State-of-the-art attacks extract models and generate adversarial examples by inferring relationships between a model’s input and output. Popular variants of these attacks have been shown to be deterred by countermeasures that poison predicted class distributions and mask class boundary gradients. Neural networks are also vulnerable to timing side-channel attacks. This work builds on top of Subneural, an attack framework that uses floating point timing side channels to extract neural structures. Novel applications of addition timing side channels are introduced, allowing the signs and arrangements of leaked parameters to be discerned more efficiently. Addition timing is also used to leak network biases, making the framework applicable to a wider range of targets. The enhanced framework is shown to be effective against models protected by prediction poisoning and gradient masking adversarial countermeasures and to be competitive with adaptive black box adversarial attacks against stateful defenses. Mitigations necessary to protect against floating-point timing side-channel attacks are also presented.
ContributorsVipat, Gaurav (Author) / Shoshitaishvili, Yan (Thesis advisor) / Doupe, Adam (Committee member) / Srivastava, Siddharth (Committee member) / Arizona State University (Publisher)
Created2023
Description
This thesis presents a study on the fuzzing of Linux binaries to find occluded bugs. Fuzzing is a widely-used technique for identifying software bugs. Despite their effectiveness, state-of-the-art fuzzers suffer from limitations in efficiency and effectiveness. Fuzzers based on random mutations are fast but struggle to generate high-quality inputs. In contrast, fuzzers based on symbolic execution produce quality inputs but lack execution speed. This paper proposes FlakJack, a novel hybrid fuzzer that patches the binary on the go to detect occluded bugs guarded by surface bugs. To dynamically overcome the challenge of patching binaries, the paper introduces multiple patching strategies based on the type of bug detected. The performance of FlakJack was evaluated on ten widely-used real-world binaries and one chaff dataset binary. The results indicate that many bugs found recently were already present in previous versions but were occluded by surface bugs. FlakJack’s approach improved the bug-finding ability by patching surface bugs that usually guard occluded bugs, significantly reducing patching cycles. Despite its unbalanced approach compared to other coverage-guided fuzzers, FlakJack is fast, lightweight, and robust. False- Positives can be filtered out quickly, and the approach is practical in other parts of the target. The paper shows that the FlakJack approach can significantly improve fuzzing performance without relying on complex strategies.
ContributorsPraveen Menon, Gokulkrishna (Author) / Bao, Tiffany (Thesis advisor) / Shoshitaishvili, Yan (Thesis advisor) / Doupe, Adam (Committee member) / Arizona State University (Publisher)
Created2023
Description
Reverse engineering is a process focused on gaining an understanding for the intricaciesof a system. This practice is critical in cybersecurity as it promotes the
findings and patching of vulnerabilities as well as the counteracting of malware. Disassemblers
and decompilers have become essential when reverse engineering due to
the readability of information they transcribe from binary files. However, these tools
still tend to produce involved and complicated outputs that hinder the acquisition of
knowledge during binary analysis. Cognitive Load Theory (CLT) explains that this
hindrance is due to the human brain’s inability to process superfluous amounts of
data. CLT classifies this data into three cognitive load types — intrinsic, extraneous,
and germane — that each can help gauge complex procedures.
In this research paper, a novel program call graph is presented accounting for
these CLT principles. The goal of this graphical view is to reduce the cognitive load
tied to the depiction of binary information and to enhance the overall binary analysis
process. This feature was implemented within the binary analysis tool, angr and it’s
user interface counterpart, angr-management. Additionally, this paper will examine a
conducted user study to quantitatively and qualitatively evaluate the effectiveness of
the newly proposed proximity view (PV). The user study includes a binary challenge
solving portion measured by defined metrics and a survey phase to receive direct participant
feedback regarding the view. The results from this study show statistically
significant evidence that PV aids in challenge solving and improves the overall understanding
binaries. The results also signify that this improvement comes with the
cost of time. The survey section of the user study further indicates that users find
PV beneficial to the reverse engineering process, but additional information needs to
be included in future developments.
ContributorsSmits, Sean (Author) / Wang, Ruoyu (Thesis advisor) / Shoshitaishvili, Yan (Thesis advisor) / Doupe, Adam (Committee member) / Arizona State University (Publisher)
Created2022
Description
Binary analysis and software debugging are critical tools in the modern softwaresecurity ecosystem. With the security arms race between attackers discovering and
exploiting vulnerabilities and the development teams patching bugs ever-tightening,
there is an immense need for more tooling to streamline the binary analysis and
debugging processes. Whether attempting to find the root cause for a buffer overflow
or a segmentation fault, the analysis process often involves manually tracing the
movement of data throughout a program’s life cycle. Up until this point, there has
not been a viable solution to the human limitation of maintaining a cohesive mental
image of the intricacies of a program’s data flow.
This thesis proposes a novel data dependency graph (DDG) analysis as an addi-
tion to angr’s analyses suite. This new analysis ingests a symbolic execution trace
in order to generate a directed acyclic graph of the program’s data dependencies. In
addition to the development of the backend logic needed to generate this graph, an
angr management view to visualize the DDG was implemented. This user interface
provides functionality for ancestor and descendant dependency tracing and sub-graph
creation. To evaluate the analysis, a user study was conducted to measure the view’s
efficacy in regards to binary analysis and software debugging. The study consisted
of a control group and experimental group attempting to solve a series of 3 chal-
lenges and subsequently providing feedback concerning perceived functionality and
comprehensibility pertaining to the view.
The results show that the view had a positive trend in relation to challenge-solving
accuracy in its target domain, as participants solved 32% more challenges 21% faster
when using the analysis than when using vanilla angr management.
ContributorsCapuano, Bailey Kellen (Author) / Shoshitaishvili, Yan (Thesis advisor) / Wang, Ruoyu (Thesis advisor) / Doupe, Adam (Committee member) / Arizona State University (Publisher)
Created2022
Description
Network Management is a critical process for an enterprise to configure and monitor the network devices using cost effective methods. It is imperative for it to be robust and free from adversarial or accidental security flaws. With the advent of cloud computing and increasing demands for centralized network control, conventional management protocols like Simple Network Management Protocol (SNMP) appear inadequate and newer techniques like Network Management Datastore Architecture (NMDA) design and Network Configuration (NETCONF) have been invented. However, unlike SNMP which underwent improvements concentrating on security, the new data management and storage techniques have not been scrutinized for the inherent security flaws.
In this thesis, I identify several vulnerabilities in the widely used critical infrastructures which leverage the NMDA design. Software Defined Networking (SDN), a proponent of NMDA, heavily relies on its datastores to program and manage the network. I base my research on the security challenges put forth by the existing datastore’s design as implemented by the SDN controllers. The vulnerabilities identified in this work have a direct impact on the controllers like OpenDayLight, Open Network Operating System and their proprietary implementations (by CISCO, Ericsson, RedHat, Brocade, Juniper, etc). Using the threat detection methodology, I demonstrate how the NMDA-based implementations are vulnerable to attacks which compromise availability, integrity, and confidentiality of the network. I finally propose defense measures to address the security threats in the existing design and discuss the challenges faced while employing these countermeasures.
In this thesis, I identify several vulnerabilities in the widely used critical infrastructures which leverage the NMDA design. Software Defined Networking (SDN), a proponent of NMDA, heavily relies on its datastores to program and manage the network. I base my research on the security challenges put forth by the existing datastore’s design as implemented by the SDN controllers. The vulnerabilities identified in this work have a direct impact on the controllers like OpenDayLight, Open Network Operating System and their proprietary implementations (by CISCO, Ericsson, RedHat, Brocade, Juniper, etc). Using the threat detection methodology, I demonstrate how the NMDA-based implementations are vulnerable to attacks which compromise availability, integrity, and confidentiality of the network. I finally propose defense measures to address the security threats in the existing design and discuss the challenges faced while employing these countermeasures.
ContributorsDixit, Vaibhav Hemant (Author) / Ahn, Gail-Joon (Thesis advisor) / Doupe, Adam (Thesis advisor) / Shoshitaishvili, Yan (Committee member) / Zhao, Ziming (Committee member) / Arizona State University (Publisher)
Created2018
Description
With the rise of the Internet of Things, embedded systems have become an integral part of life and can be found almost anywhere. Their prevalence and increased interconnectivity has made them a prime target for malicious attacks. 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 in which to deploy various memory integrity and malware detection tools.
Even though Secure World can monitor the Normal World, attackers can attempt to bypass the security measures to retain control of a compromised system. CacheKit is a new type of rootkit that exploits such a vulnerability in the ARM architecture to hide in Normal World cache from memory introspection tools running in Secure World by exploiting cache locking mechanisms. If left unchecked, ARM processors that provide hardware assisted cache locking for performance and time-critical applications in real-time and embedded systems would be completely vulnerable to this undetectable and untraceable attack. Therefore, a new approach is needed to ensure the correct use of such mechanisms and prevent malicious code from being hidden in the cache.
CacheLight is a lightweight approach that leverages the TrustZone and Virtualization extensions of the ARM architecture to allow the system to continue to securely provide these hardware facilities to users while preventing attackers from exploiting them. CacheLight restricts the ability to lock the cache to the Secure World of the processor such that the Normal World can still request certain memory to be locked into the cache by the secure operating system (OS) through a Secure Monitor Call (SMC). This grants the secure OS the power to verify and validate the information that will be locked in the requested cache way thereby ensuring that any data that remains in the cache will not be inconsistent with what exists in main memory for inspection. Malicious attempts to hide data can be prevented and recovered for analysis while legitimate requests can still generate valid entries in the cache.
Even though Secure World can monitor the Normal World, attackers can attempt to bypass the security measures to retain control of a compromised system. CacheKit is a new type of rootkit that exploits such a vulnerability in the ARM architecture to hide in Normal World cache from memory introspection tools running in Secure World by exploiting cache locking mechanisms. If left unchecked, ARM processors that provide hardware assisted cache locking for performance and time-critical applications in real-time and embedded systems would be completely vulnerable to this undetectable and untraceable attack. Therefore, a new approach is needed to ensure the correct use of such mechanisms and prevent malicious code from being hidden in the cache.
CacheLight is a lightweight approach that leverages the TrustZone and Virtualization extensions of the ARM architecture to allow the system to continue to securely provide these hardware facilities to users while preventing attackers from exploiting them. CacheLight restricts the ability to lock the cache to the Secure World of the processor such that the Normal World can still request certain memory to be locked into the cache by the secure operating system (OS) through a Secure Monitor Call (SMC). This grants the secure OS the power to verify and validate the information that will be locked in the requested cache way thereby ensuring that any data that remains in the cache will not be inconsistent with what exists in main memory for inspection. Malicious attempts to hide data can be prevented and recovered for analysis while legitimate requests can still generate valid entries in the cache.
ContributorsGutierrez, Mauricio (Author) / Zhao, Ziming (Thesis advisor) / Doupe, Adam (Committee member) / Shoshitaishvili, Yan (Committee member) / Arizona State University (Publisher)
Created2018
Description
The advent of the Internet of Things (IoT) and its increasing appearances in
Small Office/Home Office (SOHO) networks pose a unique issue to the availability
and health of the Internet at large. Many of these devices are shipped insecurely, with
poor default user and password credentials and oftentimes the general consumer does
not have the technical knowledge of how they may secure their devices and networks.
The many vulnerabilities of the IoT coupled with the immense number of existing
devices provide opportunities for malicious actors to compromise such devices and
use them in large scale distributed denial of service attacks, preventing legitimate
users from using services and degrading the health of the Internet in general.
This thesis presents an approach that leverages the benefits of an Internet Engineering
Task Force (IETF) proposed standard named Manufacturer Usage Descriptions,
that is used in conjunction with the concept of Software Defined Networks
(SDN) in order to detect malicious traffic generated from IoT devices suspected of
being utilized in coordinated flooding attacks. The approach then works towards
the ability to detect these attacks at their sources through periodic monitoring of
preemptively permitted flow rules and determining which of the flows within the permitted
set are misbehaving by using an acceptable traffic range using Exponentially
Weighted Moving Averages (EWMA).
Small Office/Home Office (SOHO) networks pose a unique issue to the availability
and health of the Internet at large. Many of these devices are shipped insecurely, with
poor default user and password credentials and oftentimes the general consumer does
not have the technical knowledge of how they may secure their devices and networks.
The many vulnerabilities of the IoT coupled with the immense number of existing
devices provide opportunities for malicious actors to compromise such devices and
use them in large scale distributed denial of service attacks, preventing legitimate
users from using services and degrading the health of the Internet in general.
This thesis presents an approach that leverages the benefits of an Internet Engineering
Task Force (IETF) proposed standard named Manufacturer Usage Descriptions,
that is used in conjunction with the concept of Software Defined Networks
(SDN) in order to detect malicious traffic generated from IoT devices suspected of
being utilized in coordinated flooding attacks. The approach then works towards
the ability to detect these attacks at their sources through periodic monitoring of
preemptively permitted flow rules and determining which of the flows within the permitted
set are misbehaving by using an acceptable traffic range using Exponentially
Weighted Moving Averages (EWMA).
ContributorsChang, Laurence Hao (Author) / Yau, Stephen (Thesis advisor) / Doupe, Adam (Committee member) / Huang, Dijiang (Committee member) / Arizona State University (Publisher)
Created2018
Description
As the gap widens between the number of security threats and the number of security professionals, the need for automated security tools becomes increasingly important. These automated systems assist security professionals by identifying and/or fixing potential vulnerabilities before they can be exploited. One such category of tools is exploit generators, which craft exploits to demonstrate a vulnerability and provide guidance on how to repair it. Existing exploit generators largely use the application code, either through static or dynamic analysis, to locate crashes and craft a payload.
This thesis proposes the Automated Reflection of CTF Hostile Exploits (ARCHES), an exploit generator that learns by example. ARCHES uses an inductive programming library named IRE to generate exploits from exploit examples. In doing so, ARCHES can create an exploit only from example exploit payloads without interacting with the service. By representing each component of the exploit interaction as a collection of theories for how that component occurs, ARCHES can identify critical state information and replicate an executable exploit. This methodology learns rapidly and works with only a few examples. The ARCHES exploit generator is targeted towards Capture the Flag (CTF) events as a suitable environment for initial research.
The effectiveness of this methodology was evaluated on four exploits with features that demonstrate the capabilities and limitations of this methodology. ARCHES is capable of reproducing exploits that require an understanding of state dependent input, such as a flag id. Additionally, ARCHES can handle basic utilization of state information that is revealed through service output. However, limitations in this methodology result in failure to replicate exploits that require a loop, intricate mathematics, or multiple TCP connections.
Inductive programming has potential as a security tool to augment existing automated security tools. Future research into these techniques will provide more capabilities for security professionals in academia and in industry.
This thesis proposes the Automated Reflection of CTF Hostile Exploits (ARCHES), an exploit generator that learns by example. ARCHES uses an inductive programming library named IRE to generate exploits from exploit examples. In doing so, ARCHES can create an exploit only from example exploit payloads without interacting with the service. By representing each component of the exploit interaction as a collection of theories for how that component occurs, ARCHES can identify critical state information and replicate an executable exploit. This methodology learns rapidly and works with only a few examples. The ARCHES exploit generator is targeted towards Capture the Flag (CTF) events as a suitable environment for initial research.
The effectiveness of this methodology was evaluated on four exploits with features that demonstrate the capabilities and limitations of this methodology. ARCHES is capable of reproducing exploits that require an understanding of state dependent input, such as a flag id. Additionally, ARCHES can handle basic utilization of state information that is revealed through service output. However, limitations in this methodology result in failure to replicate exploits that require a loop, intricate mathematics, or multiple TCP connections.
Inductive programming has potential as a security tool to augment existing automated security tools. Future research into these techniques will provide more capabilities for security professionals in academia and in industry.
ContributorsCrosley, Zackary (Author) / Doupe, Adam (Thesis advisor) / Shoshitaishvili, Yan (Committee member) / Wang, Ruoyu (Committee member) / Arizona State University (Publisher)
Created2019
Description
Reverse engineering is critical to reasoning about how a system behaves. While complete access to a system inherently allows for perfect analysis, partial access is inherently uncertain. This is the case foran individual agent in a distributed system. Inductive Reverse Engineering (IRE) enables analysis under
such circumstances. IRE does this by producing program spaces consistent with individual input-output examples for a given domain-specific language. Then, IRE intersects those program spaces to produce a generalized program consistent with all examples. IRE, an easy to use framework, allows this domain-specific language to be specified in the form of Theorist s, which produce Theory s, a succinct way of representing the program space.
Programs are often much more complex than simple string transformations. One of the ways in which they are more complex is in the way that they follow a conversation-like behavior, potentially following some underlying protocol. As a result, IRE represents program interactions as Conversations in order to
more correctly model a distributed system. This, for instance, enables IRE to model dynamically captured inputs received from other agents in the distributed system.
While domain-specific knowledge provided by a user is extremely valuable, such information is not always possible. IRE mitigates this by automatically inferring program grammars, allowing it to still perform efficient searches of the program space. It does this by intersecting conversations prior to synthesis in order to understand what portions of conversations are constant.
IRE exists to be a tool that can aid in automatic reverse engineering across numerous domains. Further, IRE aspires to be a centralized location and interface for implementing program synthesis and automatic black box analysis techniques.
such circumstances. IRE does this by producing program spaces consistent with individual input-output examples for a given domain-specific language. Then, IRE intersects those program spaces to produce a generalized program consistent with all examples. IRE, an easy to use framework, allows this domain-specific language to be specified in the form of Theorist s, which produce Theory s, a succinct way of representing the program space.
Programs are often much more complex than simple string transformations. One of the ways in which they are more complex is in the way that they follow a conversation-like behavior, potentially following some underlying protocol. As a result, IRE represents program interactions as Conversations in order to
more correctly model a distributed system. This, for instance, enables IRE to model dynamically captured inputs received from other agents in the distributed system.
While domain-specific knowledge provided by a user is extremely valuable, such information is not always possible. IRE mitigates this by automatically inferring program grammars, allowing it to still perform efficient searches of the program space. It does this by intersecting conversations prior to synthesis in order to understand what portions of conversations are constant.
IRE exists to be a tool that can aid in automatic reverse engineering across numerous domains. Further, IRE aspires to be a centralized location and interface for implementing program synthesis and automatic black box analysis techniques.
ContributorsNelson, Connor David (Author) / Doupe, Adam (Thesis advisor) / Shoshitaishvili, Yan (Committee member) / Wang, Ruoyu (Committee member) / Arizona State University (Publisher)
Created2019