Matching Items (177)
Description
In the area of hardware, reverse engineering was traditionally focused on developing clones—duplicated components that performed the same functionality of the original component. While reverse engineering techniques have been applied to software, these techniques have instead focused on understanding high-level software designs to ease the software maintenance burden. This approach works well for traditional applications that contain source code, however, there are circumstances, particularly regarding web applications, where it would be very beneficial to clone a web application and no source code is present, e.g., for security testing of the application or for offline mock testing of a third-party web service. We call this the web application cloning problem.
This thesis presents a possible solution to the problem of web application cloning. Our approach is a novel application of inductive programming, which we call inductive reverse engineering. The goal of inductive reverse engineering is to automatically reverse engineer an abstraction of the web application’s code in a completely black-box manner. We build this approach using recent advances in inductive programming, and we solve several technical challenges to scale the inductive programming techniques to realistic-sized web applications. We target the initial version of our inductive reverse engineering tool to a subset of web applications, i.e., those that do not store state and those that do not have loops. We introduce an evaluation methodology for web application cloning techniques and evaluate our approach on several real-world web applications. The results indicate that inductive reverse engineering can effectively reverse engineer specific types of web applications. In the future, we hope to extend the power of inductive reverse engineering to web applications with state and to learn loops, while still maintaining tractability.
This thesis presents a possible solution to the problem of web application cloning. Our approach is a novel application of inductive programming, which we call inductive reverse engineering. The goal of inductive reverse engineering is to automatically reverse engineer an abstraction of the web application’s code in a completely black-box manner. We build this approach using recent advances in inductive programming, and we solve several technical challenges to scale the inductive programming techniques to realistic-sized web applications. We target the initial version of our inductive reverse engineering tool to a subset of web applications, i.e., those that do not store state and those that do not have loops. We introduce an evaluation methodology for web application cloning techniques and evaluate our approach on several real-world web applications. The results indicate that inductive reverse engineering can effectively reverse engineer specific types of web applications. In the future, we hope to extend the power of inductive reverse engineering to web applications with state and to learn loops, while still maintaining tractability.
ContributorsLiao, Kevin (Author) / Doupe, Adam (Thesis director) / Ahn, Gail-Joon (Committee member) / Zhao, Ziming (Committee member) / Computer Science and Engineering Program (Contributor, Contributor) / W. P. Carey School of Business (Contributor) / Barrett, The Honors College (Contributor)
Created2017-05
Description
This thesis project focused on malicious hacking community activities accessible through the I2P protocol. We visited 315 distinct I2P sites to identify those with malicious hacking content. We also wrote software to scrape and parse data from relevant I2P sites. The data was integrated into the CySIS databases for further analysis to contribute to the larger CySIS Lab Darkweb Cyber Threat Intelligence Mining research. We found that the I2P cryptonet was slow and had only a small amount of malicious hacking community activity. However, we also found evidence of a growing perception that Tor anonymity could be compromised. This work will contribute to understanding the malicious hacker community as some Tor users, seeking assured anonymity, transition to I2P.
ContributorsHutchins, James Keith (Author) / Shakarian, Paulo (Thesis director) / Ahn, Gail-Joon (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12
Description
IoT Media broadcast devices, such as the Roku stick, Amazon Fire, and Chromecast have been emerging onto the market recently as a portable and inexpensive alternative to cable and disk players, allowing easy integration between home and business Wi-Fi networks and television systems capable of supporting HDMI inputs without the additional overhead of setting up a heavy or complicated player or computer. The rapid expansion of these products as a mechanism to provide for TV Everywhere services for entertainment as well as cheap office appliances brings yet another node in the rapidly expanding network of IoT that surrounds us today. However, the security implications of these devices are nearly unexplored, despite their prevalence. In this thesis, I will go over the structure and mechanisms of Chromecast, and explore some of the potential exploits and consequences of the device. The thesis contains an overview of the inner workings of Chromecast, goes over the segregation and limited control and fundamental design choices of the Android based OS. It then identifies the objectives of security, four different potential methods of exploit to compromise those objectives on a Chromecast and/or its attached network, including rogue applications, traffic sniffing, evil access points and the most effective one: deauthentication attack. Tests or relevant analysis were carried out for each of these methods, and conclusions were drawn on their effectiveness. There is then a conclusion revolving around the consequences, mitigation and the future implications of security issues on Chromecast and the larger IoT landscape.
ContributorsHuang, Kaiyi (Author) / Zhao, Ziming (Thesis director) / Ahn, Gail-Joon (Committee member) / W. P. Carey School of Business (Contributor) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12
Description
The SAE Baja series is a competition that challenges university student teams on all aspects of designing, building, and testing an all-terrain vehicle. In the competition, the teams present their engineering analysis of all components of their vehicle to a panel of professional engineers to show why the team's design is the overall best in performance and in manufacturing cost. Currently Arizona State University's SAE Baja team does not have a method to analyze their vehicle's suspension system, especially on the car's shock absorbers. The current solution to this problem is to change the shock absorber parameters, test drive the car, and repeat the shock absorber tuning until the car is able to produce the performance that the team desires. The following paper introduces and demonstrates three different methods, ADAMS Car, SOLIDWORKS, and MATLAB, that can be used to analyze the suspension system and gather data that can be used in the competition presentation. ADAMS Car is a power software that is used in the automotive and other engineering fields. The program does have a steep learning curve, but once the team is comfortable using it, ADAMS is very helpful with subsystem analysis and full body analysis. SOLIDWORKS can be used to perform motion analysis and drop tests, which can then be exported into ADAMS for further analysis. MATLAB can be used to model the Baja vehicle as a quarter model, which makes it easier for the team to model. Using the methods presented in this paper, ASU's Baja team can test coil-over and air shock absorbers to determine which type is more suitable for the performance and overall cost of the whole vehicle.
ContributorsPerez, Marcos (Author) / Contes, James (Thesis director) / Redkar, Sangram (Committee member) / Engineering Programs (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12
Description
Malware that perform identity theft or steal bank credentials are becoming increasingly common and can cause millions of dollars of damage annually. A large area of research focus is the automated detection and removal of such malware, due to their large impact on millions of people each year. Such a detector will be beneficial to any industry that is regularly the target of malware, such as the financial sector. Typical detection approaches such as those found in commercial anti-malware software include signature-based scanning, in which malware executables are identified based on a unique signature or fingerprint developed for that malware. However, as malware authors continue to modify and obfuscate their malware, heuristic detection is increasingly popular, in which the behaviors of the malware are identified and patterns recognized. We explore a malware analysis and classification framework using machine learning to train classifiers to distinguish between malware and benign programs based upon their features and behaviors. Using both decision tree learning and support vector machines as classifier models, we obtained overall classification accuracies of around 80%. Due to limitations primarily including the usage of a small data set, our approach may not be suitable for practical classification of malware and benign programs, as evident by a high error rate.
ContributorsAnwar, Sajid (Co-author) / Chan, Tsz (Co-author) / Ahn, Gail-Joon (Thesis director) / Zhao, Ziming (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Suction stabilized floats have been implemented into a variety of applications such as supporting wind turbines in off-shore wind farms and for stabilizing cargo ships. This thesis proposes an alternative use for the technology in creating a system of suction stabilized floats equipped with real time location modules to help first responders establish a localized coordinate system to assist in rescues. The floats create a stabilized platform for each anchor module due to the inverse slack tank effect established by the inner water chamber. The design of the float has also been proven to be stable in most cases of amplitudes and frequencies ranging from 0 to 100 except for when the frequency ranges from 23 to 60 Hz for almost all values of the amplitude. The modules in the system form a coordinate grid based off the anchors that can track the location of a tag module within the range of the system using ultra-wideband communications. This method of location identification allows responders to use the system in GPS denied environments. The system can be accessed through an Android app with Bluetooth communications in close ranges or through internet of things (IoT) using a module as a listener, a Raspberry Pi and an internet source. The system has proven to identify the location of the tag in moderate ranges with an approximate accuracy of the tag location being 15 cm.
ContributorsDye, Michaela (Author) / Redkar, Sangram (Thesis advisor) / Sugar, Thomas (Committee member) / Rogers, Bradley (Committee member) / Arizona State University (Publisher)
Created2020
Description
Fraud is defined as the utilization of deception for illegal gain by hiding the true nature of the activity. While organizations lose around $3.7 trillion in revenue due to financial crimes and fraud worldwide, they can affect all levels of society significantly. In this dissertation, I focus on credit card fraud in online transactions. Every online transaction comes with a fraud risk and it is the merchant's liability to detect and stop fraudulent transactions. Merchants utilize various mechanisms to prevent and manage fraud such as automated fraud detection systems and manual transaction reviews by expert fraud analysts. Many proposed solutions mostly focus on fraud detection accuracy and ignore financial considerations. Also, the highly effective manual review process is overlooked. First, I propose Profit Optimizing Neural Risk Manager (PONRM), a selective classifier that (a) constitutes optimal collaboration between machine learning models and human expertise under industrial constraints, (b) is cost and profit sensitive. I suggest directions on how to characterize fraudulent behavior and assess the risk of a transaction. I show that my framework outperforms cost-sensitive and cost-insensitive baselines on three real-world merchant datasets. While PONRM is able to work with many supervised learners and obtain convincing results, utilizing probability outputs directly from the trained model itself can pose problems, especially in deep learning as softmax output is not a true uncertainty measure. This phenomenon, and the wide and rapid adoption of deep learning by practitioners brought unintended consequences in many situations such as in the infamous case of Google Photos' racist image recognition algorithm; thus, necessitated the utilization of the quantified uncertainty for each prediction. There have been recent efforts towards quantifying uncertainty in conventional deep learning methods (e.g., dropout as Bayesian approximation); however, their optimal use in decision making is often overlooked and understudied. Thus, I present a mixed-integer programming framework for selective classification called MIPSC, that investigates and combines model uncertainty and predictive mean to identify optimal classification and rejection regions. I also extend this framework to cost-sensitive settings (MIPCSC) and focus on the critical real-world problem, online fraud management and show that my approach outperforms industry standard methods significantly for online fraud management in real-world settings.
ContributorsYildirim, Mehmet Yigit (Author) / Davulcu, Hasan (Thesis advisor) / Bakkaloglu, Bertan (Committee member) / Huang, Dijiang (Committee member) / Hsiao, Ihan (Committee member) / Arizona State University (Publisher)
Created2019
Description
Trajectory forecasting is used in many fields such as vehicle future trajectory prediction, stock market price prediction, human motion prediction and so on. Also, robots having the capability to reason about human behavior is an important aspect in human robot interaction. In trajectory prediction with regards to human motion prediction, implicit learning and reproduction of human behavior is the major challenge. This work tries to compare some of the recent advances taking a phenomenological approach to trajectory prediction. \par The work is expected to mainly target on generating future events or trajectories based on the previous data observed across many time intervals. In particular, this work presents and compares machine learning models to generate various human handwriting trajectories. Although the behavior of every individual is unique, it is still possible to broadly generalize and learn the underlying human behavior from the current observations to predict future human writing trajectories. This enables the machine or the robot to generate future handwriting trajectories given an initial trajectory from the individual thus helping the person to fill up the rest of the letter or curve. This work tests and compares the performance of Conditional Variational Autoencoders and Sinusoidal Representation Network models on handwriting trajectory prediction and reconstruction.
ContributorsKota, Venkata Anil (Author) / Ben Amor, Hani (Thesis advisor) / Venkateswara, Hemanth Kumar Demakethepalli (Committee member) / Redkar, Sangram (Committee member) / Arizona State University (Publisher)
Created2021
Description
Reinforcement Learning(RL) algorithms have made a remarkable contribution in the eld of robotics and training human-like agents. On the other hand, Evolutionary Algorithms(EA) are not well explored and promoted to use in the robotics field. However, they have an excellent potential to perform well. In thesis work, various RL learning algorithms like Q-learning, Deep Deterministic Policy Gradient(DDPG), and Evolutionary Algorithms(EA) like Harmony Search Algorithm(HSA) are tested for a customized Penalty Kick Robot environment. The experiments are done with both discrete and continuous action space for a penalty kick agent. The main goal is to identify which algorithm suites best in which scenario. Furthermore, a goalkeeper agent is also introduced to block the ball from reaching the goal post using the multiagent learning algorithm.
ContributorsTrivedi, Maitry Ronakbhai (Author) / Amor, Heni Ben (Thesis advisor) / Redkar, Sangram (Thesis advisor) / Sugar, Thomas (Committee member) / Arizona State University (Publisher)
Created2021
Description
Realizing the applications of Internet of Things (IoT) with the goal of achieving a more efficient and automated world requires billions of connected smart devices and the minimization of hardware cost in these devices. As a result, many IoT devices do not have sufficient resources to support various protocols required in many IoT applications. Because of this, new protocols have been introduced to support the integration of these devices. One of these protocols is the increasingly popular routing protocol for low-power and lossy networks (RPL). However, this protocol is well known to attract blackhole and sinkhole attacks and cause serious difficulties when using more computationally intensive techniques to protect against these attacks, such as intrusion detection systems and rank authentication schemes. In this paper, an effective approach is presented to protect RPL networks against blackhole attacks. The approach does not address sinkhole attacks because they cause low damage and are often used along blackhole attacks and can be detected when blackhole attaches are detected. This approach uses the feature of multiple parents per node and a parent evaluation system enabling nodes to select more reliable routes. Simulations have been conducted, compared to existing approaches this approach would provide better protection against blackhole attacks with much lower overheads for small RPL networks.
ContributorsSanders, Kent (Author) / Yau, Stephen S (Thesis advisor) / Huang, Dijiang (Committee member) / Sen, Arunabha (Committee member) / Arizona State University (Publisher)
Created2021