Matching Items (16)
Filtering by
- All Subjects: Machine Learning
- Creators: Baral, Chitta
- Member of: Theses and Dissertations
Description
Reasoning about the activities of cyber threat actors is critical to defend against cyber
attacks. However, this task is difficult for a variety of reasons. In simple terms, it is difficult
to determine who the attacker is, what the desired goals are of the attacker, and how they will
carry out their attacks. These three questions essentially entail understanding the attacker’s
use of deception, the capabilities available, and the intent of launching the attack. These
three issues are highly inter-related. If an adversary can hide their intent, they can better
deceive a defender. If an adversary’s capabilities are not well understood, then determining
what their goals are becomes difficult as the defender is uncertain if they have the necessary
tools to accomplish them. However, the understanding of these aspects are also mutually
supportive. If we have a clear picture of capabilities, intent can better be deciphered. If we
understand intent and capabilities, a defender may be able to see through deception schemes.
In this dissertation, I present three pieces of work to tackle these questions to obtain
a better understanding of cyber threats. First, we introduce a new reasoning framework
to address deception. We evaluate the framework by building a dataset from DEFCON
capture-the-flag exercise to identify the person or group responsible for a cyber attack.
We demonstrate that the framework not only handles cases of deception but also provides
transparent decision making in identifying the threat actor. The second task uses a cognitive
learning model to determine the intent – goals of the threat actor on the target system.
The third task looks at understanding the capabilities of threat actors to target systems by
identifying at-risk systems from hacker discussions on darkweb websites. To achieve this
task we gather discussions from more than 300 darkweb websites relating to malicious
hacking.
attacks. However, this task is difficult for a variety of reasons. In simple terms, it is difficult
to determine who the attacker is, what the desired goals are of the attacker, and how they will
carry out their attacks. These three questions essentially entail understanding the attacker’s
use of deception, the capabilities available, and the intent of launching the attack. These
three issues are highly inter-related. If an adversary can hide their intent, they can better
deceive a defender. If an adversary’s capabilities are not well understood, then determining
what their goals are becomes difficult as the defender is uncertain if they have the necessary
tools to accomplish them. However, the understanding of these aspects are also mutually
supportive. If we have a clear picture of capabilities, intent can better be deciphered. If we
understand intent and capabilities, a defender may be able to see through deception schemes.
In this dissertation, I present three pieces of work to tackle these questions to obtain
a better understanding of cyber threats. First, we introduce a new reasoning framework
to address deception. We evaluate the framework by building a dataset from DEFCON
capture-the-flag exercise to identify the person or group responsible for a cyber attack.
We demonstrate that the framework not only handles cases of deception but also provides
transparent decision making in identifying the threat actor. The second task uses a cognitive
learning model to determine the intent – goals of the threat actor on the target system.
The third task looks at understanding the capabilities of threat actors to target systems by
identifying at-risk systems from hacker discussions on darkweb websites. To achieve this
task we gather discussions from more than 300 darkweb websites relating to malicious
hacking.
ContributorsNunes, Eric (Author) / Shakarian, Paulo (Thesis advisor) / Ahn, Gail-Joon (Committee member) / Baral, Chitta (Committee member) / Cooke, Nancy J. (Committee member) / Arizona State University (Publisher)
Created2018
Description
The pervasive use of social media gives it a crucial role in helping the public perceive reliable information. Meanwhile, the openness and timeliness of social networking sites also allow for the rapid creation and dissemination of misinformation. It becomes increasingly difficult for online users to find accurate and trustworthy information. As witnessed in recent incidents of misinformation, it escalates quickly and can impact social media users with undesirable consequences and wreak havoc instantaneously. Different from some existing research in psychology and social sciences about misinformation, social media platforms pose unprecedented challenges for misinformation detection. First, intentional spreaders of misinformation will actively disguise themselves. Second, content of misinformation may be manipulated to avoid being detected, while abundant contextual information may play a vital role in detecting it. Third, not only accuracy, earliness of a detection method is also important in containing misinformation from being viral. Fourth, social media platforms have been used as a fundamental data source for various disciplines, and these research may have been conducted in the presence of misinformation. To tackle the challenges, we focus on developing machine learning algorithms that are robust to adversarial manipulation and data scarcity.
The main objective of this dissertation is to provide a systematic study of misinformation detection in social media. To tackle the challenges of adversarial attacks, I propose adaptive detection algorithms to deal with the active manipulations of misinformation spreaders via content and networks. To facilitate content-based approaches, I analyze the contextual data of misinformation and propose to incorporate the specific contextual patterns of misinformation into a principled detection framework. Considering its rapidly growing nature, I study how misinformation can be detected at an early stage. In particular, I focus on the challenge of data scarcity and propose a novel framework to enable historical data to be utilized for emerging incidents that are seemingly irrelevant. With misinformation being viral, applications that rely on social media data face the challenge of corrupted data. To this end, I present robust statistical relational learning and personalization algorithms to minimize the negative effect of misinformation.
The main objective of this dissertation is to provide a systematic study of misinformation detection in social media. To tackle the challenges of adversarial attacks, I propose adaptive detection algorithms to deal with the active manipulations of misinformation spreaders via content and networks. To facilitate content-based approaches, I analyze the contextual data of misinformation and propose to incorporate the specific contextual patterns of misinformation into a principled detection framework. Considering its rapidly growing nature, I study how misinformation can be detected at an early stage. In particular, I focus on the challenge of data scarcity and propose a novel framework to enable historical data to be utilized for emerging incidents that are seemingly irrelevant. With misinformation being viral, applications that rely on social media data face the challenge of corrupted data. To this end, I present robust statistical relational learning and personalization algorithms to minimize the negative effect of misinformation.
ContributorsWu, Liang (Author) / Liu, Huan (Thesis advisor) / Tong, Hanghang (Committee member) / Doupe, Adam (Committee member) / Davison, Brian D. (Committee member) / Arizona State University (Publisher)
Created2019
Description
Medical records are increasingly being recorded in the form of electronic health records (EHRs), with a significant amount of patient data recorded as unstructured natural language text. Consequently, being able to extract and utilize clinical data present within these records is an important step in furthering clinical care. One important aspect within these records is the presence of prescription information. Existing techniques for extracting prescription information — which includes medication names, dosages, frequencies, reasons for taking, and mode of administration — from unstructured text have focused on the application of rule- and classifier-based methods. While state-of-the-art systems can be effective in extracting many types of information, they require significant effort to develop hand-crafted rules and conduct effective feature engineering. This paper presents the use of a bidirectional LSTM with CRF tagging model initialized with precomputed word embeddings for extracting prescription information from sentences without requiring significant feature engineering. The experimental results, run on the i2b2 2009 dataset, achieve an F1 macro measure of 0.8562, and scores above 0.9449 on four of the six categories, indicating significant potential for this model.
ContributorsRawal, Samarth Chetan (Author) / Baral, Chitta (Thesis director) / Anwar, Saadat (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
For the past three decades, the design of an effective strategy for generating poetry that matches that of a human’s creative capabilities and complexities has been an elusive goal in artificial intelligence (AI) and natural language generation (NLG) research, and among linguistic creativity researchers in particular. This thesis presents a novel approach to fixed verse poetry generation using neural word embeddings. During the course of generation, a two layered poetry classifier is developed. The first layer uses a lexicon based method to classify poems into types based on form and structure, and the second layer uses a supervised classification method to classify poems into subtypes based on content with an accuracy of 92%. The system then uses a two-layer neural network to generate poetry based on word similarities and word movements in a 50-dimensional vector space.
The verses generated by the system are evaluated using rhyme, rhythm, syllable counts and stress patterns. These computational features of language are considered for generating haikus, limericks and iambic pentameter verses. The generated poems are evaluated using a Turing test on both experts and non-experts. The user study finds that only 38% computer generated poems were correctly identified by nonexperts while 65% of the computer generated poems were correctly identified by experts. Although the system does not pass the Turing test, the results from the Turing test suggest an improvement of over 17% when compared to previous methods which use Turing tests to evaluate poetry generators.
The verses generated by the system are evaluated using rhyme, rhythm, syllable counts and stress patterns. These computational features of language are considered for generating haikus, limericks and iambic pentameter verses. The generated poems are evaluated using a Turing test on both experts and non-experts. The user study finds that only 38% computer generated poems were correctly identified by nonexperts while 65% of the computer generated poems were correctly identified by experts. Although the system does not pass the Turing test, the results from the Turing test suggest an improvement of over 17% when compared to previous methods which use Turing tests to evaluate poetry generators.
ContributorsMagge, Arjun (Author) / Syrotiuk, Violet R. (Thesis advisor) / Baral, Chitta (Committee member) / Hogue, Cynthia (Committee member) / Bazzi, Rida (Committee member) / Arizona State University (Publisher)
Created2016
Description
Due to vast resources brought by social media services, social data mining has
received increasing attention in recent years. The availability of sheer amounts of
user-generated data presents data scientists both opportunities and challenges. Opportunities are presented with additional data sources. The abundant link information
in social networks could provide another rich source in deriving implicit information
for social data mining. However, the vast majority of existing studies overwhelmingly
focus on positive links between users while negative links are also prevailing in real-
world social networks such as distrust relations in Epinions and foe links in Slashdot.
Though recent studies show that negative links have some added value over positive
links, it is dicult to directly employ them because of its distinct characteristics from
positive interactions. Another challenge is that label information is rather limited
in social media as the labeling process requires human attention and may be very
expensive. Hence, alternative criteria are needed to guide the learning process for
many tasks such as feature selection and sentiment analysis.
To address above-mentioned issues, I study two novel problems for signed social
networks mining, (1) unsupervised feature selection in signed social networks; and
(2) unsupervised sentiment analysis with signed social networks. To tackle the first problem, I propose a novel unsupervised feature selection framework SignedFS. In
particular, I model positive and negative links simultaneously for user preference
learning, and then embed the user preference learning into feature selection. To study the second problem, I incorporate explicit sentiment signals in textual terms and
implicit sentiment signals from signed social networks into a coherent model Signed-
Senti. Empirical experiments on real-world datasets corroborate the effectiveness of
these two frameworks on the tasks of feature selection and sentiment analysis.
received increasing attention in recent years. The availability of sheer amounts of
user-generated data presents data scientists both opportunities and challenges. Opportunities are presented with additional data sources. The abundant link information
in social networks could provide another rich source in deriving implicit information
for social data mining. However, the vast majority of existing studies overwhelmingly
focus on positive links between users while negative links are also prevailing in real-
world social networks such as distrust relations in Epinions and foe links in Slashdot.
Though recent studies show that negative links have some added value over positive
links, it is dicult to directly employ them because of its distinct characteristics from
positive interactions. Another challenge is that label information is rather limited
in social media as the labeling process requires human attention and may be very
expensive. Hence, alternative criteria are needed to guide the learning process for
many tasks such as feature selection and sentiment analysis.
To address above-mentioned issues, I study two novel problems for signed social
networks mining, (1) unsupervised feature selection in signed social networks; and
(2) unsupervised sentiment analysis with signed social networks. To tackle the first problem, I propose a novel unsupervised feature selection framework SignedFS. In
particular, I model positive and negative links simultaneously for user preference
learning, and then embed the user preference learning into feature selection. To study the second problem, I incorporate explicit sentiment signals in textual terms and
implicit sentiment signals from signed social networks into a coherent model Signed-
Senti. Empirical experiments on real-world datasets corroborate the effectiveness of
these two frameworks on the tasks of feature selection and sentiment analysis.
ContributorsCheng, Kewei (Author) / Liu, Huan (Thesis advisor) / Tong, Hanghang (Committee member) / Baral, Chitta (Committee member) / Arizona State University (Publisher)
Created2017
Description
Artificial Intelligence, as the hottest research topic nowadays, is mostly driven by data. There is no doubt that data is the king in the age of AI. However, natural high-quality data is precious and rare. In order to obtain enough and eligible data to support AI tasks, data processing is always required. To be even worse, the data preprocessing tasks are often dull and heavy, which require huge human labors to deal with. Statistics show 70% - 80% of the data scientists' time is spent on data integration process. Among various reasons, schema changes that commonly exist in the data warehouse are one significant obstacle that impedes the automation of the end-to-end data integration process. Traditional data integration applications rely on data processing operators such as join, union, aggregation and so on. Those operations are fragile and can be easily interrupted by schema changes. Whenever schema changes happen, the data integration applications will require human labors to solve the interruptions and downtime. The industries as well as the data scientists need a new mechanism to handle the schema changes in data integration tasks. This work proposes a new direction of data integration applications based on deep learning models. The data integration problem is defined in the scenario of integrating tabular-format data with natural schema changes, using the cell-based data abstraction. In addition, data augmentation and adversarial learning are investigated to boost the model robustness to schema changes. The experiments are tested on two real-world data integration scenarios, and the results demonstrate the effectiveness of the proposed approach.
ContributorsWang, Zijie (Author) / Zou, Jia (Thesis advisor) / Baral, Chitta (Committee member) / Candan, K. Selcuk (Committee member) / Arizona State University (Publisher)
Created2021
Description
T-cells are an integral component of the immune system, enabling the body to distinguish between pathogens and the self. The primary mechanism which enables this is their T-cell receptors (TCR) which bind to antigen epitopes foreign to the body. This detection mechanism allows the T-cell to determine when an immune response is necessary. The computational prediction of TCR-epitope binding is important to researchers for both medical applications and for furthering their understanding of the biological mechanisms that impact immunity. Models which have been developed for this purpose fail to account for the interrelationships between amino acids and demonstrate poor out-of-sample performance. Small changes to the amino acids in these protein sequences can drastically change their structure and function. In recent years, attention-based deep learning models have shown success in their ability to learn rich contextual representations of data. To capture the contextual biological relationships between the amino acids, a multi-head self-attention model was created to predict the binding affinity between given TCR and epitope sequences. By learning the structural nuances of the sequences, this model is able to improve upon existing model performance and grant insights into the underlying mechanisms which impact binding.
ContributorsCai, Michael Ray (Author) / Lee, Heewook (Thesis advisor) / Bang, Seojin (Committee member) / Baral, Chitta (Committee member) / Arizona State University (Publisher)
Created2021
Description
Models that learn from data are widely and rapidly being deployed today for real-world use, and have become an integral and embedded part of human lives. While these technological advances are exciting and impactful, such data-driven computer vision systems often fail in inscrutable ways. This dissertation seeks to study and improve the reliability of machine learning models from several perspectives including the development of robust training algorithms to mitigate the risks of such failures, construction of new datasets that provide a new perspective on capabilities of vision models, and the design of evaluation metrics for re-calibrating the perception of performance improvements. I will first address distribution shift in image classification with the following contributions: (1) two methods for improving the robustness of image classifiers to distribution shift by leveraging the classifier's failures into an adversarial data transformation pipeline guided by domain knowledge, (2) an interpolation-based technique for flagging out-of-distribution samples, and (3) an intriguing trade-off between distributional and adversarial robustness resulting from data modification strategies. I will then explore reliability considerations for \textit{semantic vision} models that learn from both visual and natural language data; I will discuss how logical and semantic sentence transformations affect the performance of vision--language models and my contributions towards developing knowledge-guided learning algorithms to mitigate these failures. Finally, I will describe the effort towards building and evaluating complex reasoning capabilities of vision--language models towards the long-term goal of robust and reliable computer vision models that can communicate, collaborate, and reason with humans.
ContributorsGokhale, Tejas (Author) / Yang, Yezhou (Thesis advisor) / Baral, Chitta (Thesis advisor) / Ben Amor, Heni (Committee member) / Anirudh, Rushil (Committee member) / Arizona State University (Publisher)
Created2023
Description
Machine learning models are increasingly being deployed in real-world applications where their predictions are used to make critical decisions in a variety of domains. The proliferation of such models has led to a burgeoning need to ensure the reliability and safety of these models, given the potential negative consequences of model vulnerabilities. The complexity of machine learning models, along with the extensive data sets they analyze, can result in unpredictable and unintended outcomes. Model vulnerabilities may manifest due to errors in data input, algorithm design, or model deployment, which can have significant implications for both individuals and society. To prevent such negative outcomes, it is imperative to identify model vulnerabilities at an early stage in the development process. This will aid in guaranteeing the integrity, dependability, and safety of the models, thus mitigating potential risks and enabling the full potential of these technologies to be realized. However, enumerating vulnerabilities can be challenging due to the complexity of the real-world environment. Visual analytics, situated at the intersection of human-computer interaction, computer graphics, and artificial intelligence, offers a promising approach for achieving high interpretability of complex black-box models, thus reducing the cost of obtaining insights into potential vulnerabilities of models. This research is devoted to designing novel visual analytics methods to support the identification and analysis of model vulnerabilities. Specifically, generalizable visual analytics frameworks are instantiated to explore vulnerabilities in machine learning models concerning security (adversarial attacks and data perturbation) and fairness (algorithmic bias). In the end, a visual analytics approach is proposed to enable domain experts to explain and diagnose the model improvement of addressing identified vulnerabilities of machine learning models in a human-in-the-loop fashion. The proposed methods hold the potential to enhance the security and fairness of machine learning models deployed in critical real-world applications.
ContributorsXie, Tiankai (Author) / Maciejewski, Ross (Thesis advisor) / Liu, Huan (Committee member) / Bryan, Chris (Committee member) / Tong, Hanghang (Committee member) / Arizona State University (Publisher)
Created2023
Description
Significance of real-world knowledge for Natural Language Understanding(NLU) is well-known for decades. With advancements in technology, challenging tasks like question-answering, text-summarizing, and machine translation are made possible with continuous efforts in the field of Natural Language Processing(NLP). Yet, knowledge integration to answer common sense questions is still a daunting task. Logical reasoning has been a resort for many of the problems in NLP and has achieved considerable results in the field, but it is difficult to resolve the ambiguities in a natural language. Co-reference resolution is one of the problems where ambiguity arises due to the semantics of the sentence. Another such problem is the cause and result statements which require causal commonsense reasoning to resolve the ambiguity. Modeling these type of problems is not a simple task with rules or logic. State-of-the-art systems addressing these problems use a trained neural network model, which claims to have overall knowledge from a huge trained corpus. These systems answer the questions by using the knowledge embedded in their trained language model. Although the language models embed the knowledge from the data, they use occurrences of words and frequency of co-existing words to solve the prevailing ambiguity. This limits the performance of language models to solve the problems in common-sense reasoning task as it generalizes the concept rather than trying to answer the problem specific to its context. For example, "The painting in Mark's living room shows an oak tree. It is to the right of a house", is a co-reference resolution problem which requires knowledge. Language models can resolve whether "it" refers to "painting" or "tree", since "house" and "tree" are two common co-occurring words so the models can resolve "tree" to be the co-reference. On the other hand, "The large ball crashed right through the table. Because it was made of Styrofoam ." to resolve for "it" which can be either "table" or "ball", is difficult for a language model as it requires more information about the problem.
In this work, I have built an end-to-end framework, which uses the automatically extracted knowledge based on the problem. This knowledge is augmented with the language models using an explicit reasoning module to resolve the ambiguity. This system is built to improve the accuracy of the language models based approaches for commonsense reasoning. This system has proved to achieve the state of the art accuracy on the Winograd Schema Challenge.
In this work, I have built an end-to-end framework, which uses the automatically extracted knowledge based on the problem. This knowledge is augmented with the language models using an explicit reasoning module to resolve the ambiguity. This system is built to improve the accuracy of the language models based approaches for commonsense reasoning. This system has proved to achieve the state of the art accuracy on the Winograd Schema Challenge.
ContributorsPrakash, Ashok (Author) / Baral, Chitta (Thesis advisor) / Devarakonda, Murthy (Committee member) / Anwar, Saadat (Committee member) / Arizona State University (Publisher)
Created2019