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- All Subjects: Computer Science
- Creators: Arizona State University
- Creators: Ye, Jieping
- Status: Published
Description
The advancement of cloud technology has impacted society positively in a number of ways, but it has also led to an increase in threats that target private information available on cloud systems. Intrusion prevention systems play a crucial role in protecting cloud systems from such threats. In this thesis, an intrusion prevention approach todetect and prevent such threats in real-time is proposed. This approach is designed for network-based intrusion prevention systems and leverages the power of supervised machine learning with Extreme Gradient Boosting (XGBoost) and Long Short-Term Memory (LSTM) algorithms, to analyze the flow of each packet that is sent to a cloud system through the network. The innovations of this thesis include developing a custom LSTM architecture, using this architecture to train a LSTM model to identify attacks and using TCP reset functionality to prevent attacks for cloud systems. The aim of this thesis is to provide a framework for an Intrusion Prevention System. Based on simulations and experimental results with the NF-UQ-NIDS-v2 dataset, the proposed system is accurate, fast, scalable and has a low rate of false positives, making it suitable for real world applications.
ContributorsGianchandani, Siddharth (Author) / Yau, Stephen (Thesis advisor) / Zhao, Ming (Committee member) / Lee, Kookjin (Committee member) / Arizona State University (Publisher)
Created2023
Description
Pan Tilt Traffic Cameras (PTTC) are a vital component of traffic managementsystems for monitoring/surveillance. In a real world scenario, if a vehicle is in pursuit
of another vehicle or an accident has occurred at an intersection causing traffic
stoppages, accurate and venerable data from PTTC is necessary to quickly localize
the cars on a map for adept emergency response as more and more traffic systems are
getting automated using machine learning concepts. However, the position(orientation)
of the PTTC with respect to the environment is often unknown as most of them
lack Inertial Measurement Units or Encoders. Current State Of the Art systems 1.
Demand high performance compute and use carbon footprint heavy Deep Neural
Networks(DNN), 2. Are only applicable to scenarios with appropriate lane markings or
only roundabouts, 3. Demand complex mathematical computations to determine focal
length and optical center first before determining the pose. A compute light approach
"TIPANGLE" is presented in this work. The approach uses the concept of Siamese
Neural Networks(SNN) encompassing simple mathematical functions i.e., Euclidian
Distance and Contrastive Loss to achieve the objective. The effectiveness of the
approach is reckoned with a thorough comparison study with alternative approaches
and also by executing the approach on an embedded system i.e., Raspberry Pi 3.
ContributorsJagadeesha, Shreehari (Author) / Shrivastava, Aviral (Thesis advisor) / Gopalan, Nakul (Committee member) / Arora, Aman (Committee member) / Arizona State University (Publisher)
Created2023
Description
This dissertation is an examination of collective systems of computationally limited agents that require coordination to achieve complex ensemble behaviors or goals. The design of coordination strategies can be framed as multiagent optimization problems, which are addressed in this work from both theoretical and practical perspectives. The primary foci of this study are models where computation is distributed over the agents themselves, which are assumed to possess onboard computational capabilities. There exist many assumption variants for distributed models, including fairness and concurrency properties. In general, there is a fundamental trade-off whereby weakening model assumptions increases the applicability of proposed solutions, while also increasing the difficulty of proving theoretical guarantees. This dissertation aims to produce a deeper understanding of this trade-off with respect to multiagent optimization and scalability in distributed settings. This study considers four multiagent optimization problems. The model assumptions begin with fully centralized computation for the all-or-nothing multicommodity flow problem, then progress to synchronous distributed models through examination of the unmapped multivehicle routing problem and the distributed target localization problem. The final model is again distributed but assumes an unfair asynchronous adversary in the context of the energy distribution problem for programmable matter. For these problems, a variety of algorithms are presented, each of which is grounded in a theoretical foundation that permits formal guarantees regarding correctness, running time, and other critical properties. These guarantees are then validated with in silico simulations and (in some cases) physical experiments, demonstrating empirically that they may carry over to the real world. Hence, this dissertation bridges a portion of the predictability-practicality gap with respect to multiagent optimization problems.
ContributorsWeber, Jamison Wayne (Author) / Richa, Andréa W (Thesis advisor) / Bertsekas, Dimitri P (Committee member) / Murphey, Todd D (Committee member) / Jiang, Zilin (Committee member) / Arizona State University (Publisher)
Created2023
Description
Due to the internet being in its infancy, there is no consensus regarding policy approaches that various countries have taken. These policies range from strict government control to liberal access to the internet which makes protecting individual private data difficult. There are too many loopholes and various forms of policy on how to approach protecting data. There must be effort by both the individual, government, and private entities by using theoretical mixed methods to approach protecting oneself properly online.
ContributorsPeralta, Christina A (Author) / Scheall, Scott (Thesis advisor) / Hollinger, Keith (Thesis advisor) / Alozie, Nicholas (Committee member) / Arizona State University (Publisher)
Created2023
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
Social media platforms provide a rich environment for analyzing user behavior. Recently, deep learning-based methods have been a mainstream approach for social media analysis models involving complex patterns. However, these methods are susceptible to biases in the training data, such as participation inequality. Basically, a mere 1% of users generate the majority of the content on social networking sites, while the remaining users, though engaged to varying degrees, tend to be less active in content creation and largely silent. These silent users consume and listen to information that is propagated on the platform.However, their voice, attitude, and interests are not reflected in the online content, making the decision of the current methods predisposed towards the opinion of the active users. So models can mistake the loudest users for the majority. To make the silent majority heard is to reveal the true landscape of the platform. In this dissertation, to compensate for this bias in the data, which is related to user-level data scarcity, I introduce three pieces of research work. Two of these proposed solutions deal with the data on hand while the other tries to augment the current data. Specifically, the first proposed approach modifies the weight of users' activity/interaction in the input space, while the second approach involves re-weighting the loss based on the users' activity levels during the downstream task training. Lastly, the third approach uses large language models (LLMs) and learns the user's writing behavior to expand the current data. In other words, by utilizing LLMs as a sophisticated knowledge base, this method aims to augment the silent user's data.
ContributorsKarami, Mansooreh (Author) / Liu, Huan (Thesis advisor) / Sen, Arunabha (Committee member) / Davulcu, Hasan (Committee member) / Mancenido, Michelle V. (Committee member) / Arizona State University (Publisher)
Created2023
Description
Human civilization within the last two decades has largely transformed into an online one, with many of its associated activities taking place on computers and complex networked systems -- their analog and real-world equivalents having been rendered obsolete.These activities run the gamut from the ordinary and mundane, like ordering food, to complex and large-scale, such as those involving critical infrastructure or global trade and communications.
Unfortunately, the activities of human civilization also involve criminal, adversarial, and malicious ones with the result that they also now have their digital equivalents.
Ransomware, malware, and targeted cyberattacks are a fact of life today and are instigated not only by organized criminal gangs, but adversarial nation-states and organizations as well.
Needless to say, such actions result in disastrous and harmful real-world consequences. As the complexity and variety of software has evolved, so too has the ingenuity of attacks that exploit them; for example modern cyberattacks typically involve sequential exploitation of multiple software vulnerabilities.Compared to a decade ago, modern software stacks on personal computers, laptops, servers, mobile phones, and even Internet of Things (IoT) devices involve a dizzying array of interdependent programs and software libraries, with each of these components presenting attractive attack-surfaces for adversarial actors.
However, the responses to this still rely on paradigms that can neither react quickly enough nor scale to increasingly dynamic, ever-changing, and complex software environments.
Better approaches are therefore needed, that can assess system readiness and vulnerabilities, identify potential attack vectors and strategies (including ways to counter them), and proactively detect vulnerabilities in complex software before they can be exploited. In this dissertation, I first present a mathematical model and associated algorithms to identify attacker strategies for sequential cyberattacks based on attacker state, attributes and publicly-available vulnerability information.Second, I extend the model and design algorithms to help identify defensive courses of action against attacker strategies.
Finally, I present my work to enhance the ability of coverage-based fuzzers to identify software vulnerabilities by providing visibility into complex, internal program-states.
ContributorsPaliath, Vivin Suresh (Author) / Doupe, Adam (Thesis advisor) / Shoshitaishvili, Yan (Thesis advisor) / Wang, Ruoyu (Committee member) / Shakarian, Paulo (Committee member) / Arizona State University (Publisher)
Created2023
Description
When looking at drawings of graphs, questions about graph density, community structures, local clustering and other graph properties may be of critical importance for analysis. While graph layout algorithms have focused on minimizing edge crossing, symmetry, and other such layout properties, there is not much known about how these algorithms relate to a user’s ability to perceive graph properties for a given graph layout. This study applies previously established methodologies for perceptual analysis to identify which graph drawing layout will help the user best perceive a particular graph property. A large scale (n = 588) crowdsourced experiment is conducted to investigate whether the perception of two graph properties (graph density and average local clustering coefficient) can be modeled using Weber’s law. Three graph layout algorithms from three representative classes (Force Directed - FD, Circular, and Multi-Dimensional Scaling - MDS) are studied, and the results of this experiment establish the precision of judgment for these graph layouts and properties. The findings demonstrate that the perception of graph density can be modeled with Weber’s law. Furthermore, the perception of the average clustering coefficient can be modeled as an inverse of Weber’s law, and the MDS layout showed a significantly different precision of judgment than the FD layout.
ContributorsSoni, Utkarsh (Author) / Maciejewski, Ross (Thesis advisor) / Kobourov, Stephen (Committee member) / Sefair, Jorge (Committee member) / Arizona State University (Publisher)
Created2018
Description
Mid-air ultrasound haptic technology can enhance user interaction and immersion in extended reality (XR) applications through contactless touch feedback. However, existing design tools for mid-air haptics primarily support the creation of static tactile sensations (tactons), which lack adaptability at runtime. These tactons do not offer the required expressiveness in interactive scenarios where a continuous closed-loop response to user movement or environmental states is desirable. This thesis proposes AdapTics, a toolkit featuring a graphical interface for the rapid prototyping of adaptive tactons—dynamic sensations that can adjust at runtime based on user interactions, environmental changes, or other inputs. A software library and a Unity package accompany the graphical interface to enable integration of adaptive tactons in existing applications. The design space provided by AdapTics for creating adaptive mid-air ultrasound tactons is presented, along with evidence that the design tool enhances Creativity Support Index ratings for Exploration and Expressiveness, as demonstrated in a user study involving 12 XR and haptic designers.
ContributorsJohn, Kevin (Author) / Seifi, Hasti (Thesis advisor) / Bryan, Chris (Committee member) / Schneider, Oliver (Committee member) / Arizona State University (Publisher)
Created2024