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Computational models for relatively complex systems are subject to many difficulties, among which is the ability for the models to be discretely understandable and applicable to specific problem types and their solutions. This demands the specification of a dynamic system as a collection of models, including metamodels. In this context, new modeling approaches and tools can help provide a richer understanding and, therefore, the development of sophisticated behavior in system dynamics. From this vantage point, an activity specification is proposed as a modeling approach based on a time-based discrete event system abstraction. Such models are founded upon set-theoretic principles and methods for modeling and simulation with the intent of making them subject to specific and profound questions for user-defined experiments.
Because developing models is becoming more time-consuming and expensive, some research has focused on the acquisition of concrete means targeted at the early stages of component-based system analysis and design. The model-driven architecture (MDA) framework provides some means for the behavioral modeling of discrete systems. The development of models can benefit from simplifications and elaborations enabled by the MDA meta-layers, which is essential for managing model complexity. Although metamodels pose difficulties, especially for developing complex behavior, as opposed to structure, they are advantageous and complementary to formal models and concrete implementations in programming languages.
The developed approach is focused on action and control concepts across the MDA meta-layers and is proposed for the parallel Discrete Event System Specification (P-DEVS) formalism. The Unified Modeling Language (UML) activity meta-models are used with syntax and semantics that conform to the DEVS formalism and its execution protocol. The notions of the DEVS component and state are used together according to their underlying system-theoretic foundation. A prototype tool supporting activity modeling was developed to demonstrate the degree to which action-based behavior can be modeled using the MDA and DEVS. The parallel DEVS, as a formal approach, supports identifying the semantics of the UML activities. Another prototype was developed to create activity models and support their execution with the DEVS-Suite simulator, and a set of prototypical multiprocessor architecture model specifications were designed, simulated, and analyzed.
Because developing models is becoming more time-consuming and expensive, some research has focused on the acquisition of concrete means targeted at the early stages of component-based system analysis and design. The model-driven architecture (MDA) framework provides some means for the behavioral modeling of discrete systems. The development of models can benefit from simplifications and elaborations enabled by the MDA meta-layers, which is essential for managing model complexity. Although metamodels pose difficulties, especially for developing complex behavior, as opposed to structure, they are advantageous and complementary to formal models and concrete implementations in programming languages.
The developed approach is focused on action and control concepts across the MDA meta-layers and is proposed for the parallel Discrete Event System Specification (P-DEVS) formalism. The Unified Modeling Language (UML) activity meta-models are used with syntax and semantics that conform to the DEVS formalism and its execution protocol. The notions of the DEVS component and state are used together according to their underlying system-theoretic foundation. A prototype tool supporting activity modeling was developed to demonstrate the degree to which action-based behavior can be modeled using the MDA and DEVS. The parallel DEVS, as a formal approach, supports identifying the semantics of the UML activities. Another prototype was developed to create activity models and support their execution with the DEVS-Suite simulator, and a set of prototypical multiprocessor architecture model specifications were designed, simulated, and analyzed.
ContributorsAlshareef, Abdurrahman (Author) / Sarjoughian, Hessam S. (Thesis advisor) / Fainekos, Georgios (Committee member) / Lee, Joohyung (Committee member) / Zhao, Ming (Committee member) / Arizona State University (Publisher)
Created2019
Description
Emerging from years of research and development, the Internet-of-Things (IoT) has finally paved its way into our daily lives. From smart home to Industry 4.0, IoT has been fundamentally transforming numerous domains with its unique superpower of interconnecting world-wide devices. However, the capability of IoT is largely constrained by the limited resources it can employ in various application scenarios, including computing power, network resource, dedicated hardware, etc. The situation is further exacerbated by the stringent quality-of-service (QoS) requirements of many IoT applications, such as delay, bandwidth, security, reliability, and more. This mismatch in resources and demands has greatly hindered the deployment and utilization of IoT services in many resource-intense and QoS-sensitive scenarios like autonomous driving and virtual reality.
I believe that the resource issue in IoT will persist in the near future due to technological, economic and environmental factors. In this dissertation, I seek to address this issue by means of smart resource allocation. I propose mathematical models to formally describe various resource constraints and application scenarios in IoT. Based on these, I design smart resource allocation algorithms and protocols to maximize the system performance in face of resource restrictions. Different aspects are tackled, including networking, security, and economics of the entire IoT ecosystem. For different problems, different algorithmic solutions are devised, including optimal algorithms, provable approximation algorithms, and distributed protocols. The solutions are validated with rigorous theoretical analysis and/or extensive simulation experiments.
I believe that the resource issue in IoT will persist in the near future due to technological, economic and environmental factors. In this dissertation, I seek to address this issue by means of smart resource allocation. I propose mathematical models to formally describe various resource constraints and application scenarios in IoT. Based on these, I design smart resource allocation algorithms and protocols to maximize the system performance in face of resource restrictions. Different aspects are tackled, including networking, security, and economics of the entire IoT ecosystem. For different problems, different algorithmic solutions are devised, including optimal algorithms, provable approximation algorithms, and distributed protocols. The solutions are validated with rigorous theoretical analysis and/or extensive simulation experiments.
ContributorsYu, Ruozhou, Ph.D (Author) / Xue, Guoliang (Thesis advisor) / Huang, Dijiang (Committee member) / Sen, Arunabha (Committee member) / Zhang, Yanchao (Committee member) / Arizona State University (Publisher)
Created2019
Description
The rapid advancements of technology have greatly extended the ubiquitous nature of smartphones acting as a gateway to numerous social media applications. This brings an immense convenience to the users of these applications wishing to stay connected to other individuals through sharing their statuses, posting their opinions, experiences, suggestions, etc on online social networks (OSNs). Exploring and analyzing this data has a great potential to enable deep and fine-grained insights into the behavior, emotions, and language of individuals in a society. This proposed dissertation focuses on utilizing these online social footprints to research two main threads – 1) Analysis: to study the behavior of individuals online (content analysis) and 2) Synthesis: to build models that influence the behavior of individuals offline (incomplete action models for decision-making).
A large percentage of posts shared online are in an unrestricted natural language format that is meant for human consumption. One of the demanding problems in this context is to leverage and develop approaches to automatically extract important insights from this incessant massive data pool. Efforts in this direction emphasize mining or extracting the wealth of latent information in the data from multiple OSNs independently. The first thread of this dissertation focuses on analytics to investigate the differentiated content-sharing behavior of individuals. The second thread of this dissertation attempts to build decision-making systems using social media data.
The results of the proposed dissertation emphasize the importance of considering multiple data types while interpreting the content shared on OSNs. They highlight the unique ways in which the data and the extracted patterns from text-based platforms or visual-based platforms complement and contrast in terms of their content. The proposed research demonstrated that, in many ways, the results obtained by focusing on either only text or only visual elements of content shared online could lead to biased insights. On the other hand, it also shows the power of a sequential set of patterns that have some sort of precedence relationships and collaboration between humans and automated planners.
A large percentage of posts shared online are in an unrestricted natural language format that is meant for human consumption. One of the demanding problems in this context is to leverage and develop approaches to automatically extract important insights from this incessant massive data pool. Efforts in this direction emphasize mining or extracting the wealth of latent information in the data from multiple OSNs independently. The first thread of this dissertation focuses on analytics to investigate the differentiated content-sharing behavior of individuals. The second thread of this dissertation attempts to build decision-making systems using social media data.
The results of the proposed dissertation emphasize the importance of considering multiple data types while interpreting the content shared on OSNs. They highlight the unique ways in which the data and the extracted patterns from text-based platforms or visual-based platforms complement and contrast in terms of their content. The proposed research demonstrated that, in many ways, the results obtained by focusing on either only text or only visual elements of content shared online could lead to biased insights. On the other hand, it also shows the power of a sequential set of patterns that have some sort of precedence relationships and collaboration between humans and automated planners.
ContributorsManikonda, Lydia (Author) / Kambhampati, Subbarao (Thesis advisor) / Liu, Huan (Committee member) / Li, Baoxin (Committee member) / De Choudhury, Munmun (Committee member) / Kamar, Ece (Committee member) / Arizona State University (Publisher)
Created2019
Description
In recent years, the rise in social media usage both vertically in terms of the number of users by platform and horizontally in terms of the number of platforms per user has led to data explosion.
User-generated social media content provides an excellent opportunity to mine data of interest and to build resourceful applications. The rise in the number of healthcare-related social media platforms and the volume of healthcare knowledge available online in the last decade has resulted in increased social media usage for personal healthcare. In the United States, nearly ninety percent of adults, in the age group 50-75, have used social media to seek and share health information. Motivated by the growth of social media usage, this thesis focuses on healthcare-related applications, study various challenges posed by social media data, and address them through novel and effective machine learning algorithms.
The major challenges for effectively and efficiently mining social media data to build functional applications include: (1) Data reliability and acceptance: most social media data (especially in the context of healthcare-related social media) is not regulated and little has been studied on the benefits of healthcare-specific social media; (2) Data heterogeneity: social media data is generated by users with both demographic and geographic diversity; (3) Model transparency and trustworthiness: most existing machine learning models for addressing heterogeneity are considered as black box models, not many providing explanations for why they do what they do to trust them.
In response to these challenges, three main research directions have been investigated in this thesis: (1) Analyzing social media influence on healthcare: to study the real world impact of social media as a source to offer or seek support for patients with chronic health conditions; (2) Learning from task heterogeneity: to propose various models and algorithms that are adaptable to new social media platforms and robust to dynamic social media data, specifically on modeling user behaviors, identifying similar actors across platforms, and adapting black box models to a specific learning scenario; (3) Explaining heterogeneous models: to interpret predictive models in the presence of task heterogeneity. In this thesis, novel algorithms with theoretical analysis from various aspects (e.g., time complexity, convergence properties) have been proposed. The effectiveness and efficiency of the proposed algorithms is demonstrated by comparison with state-of-the-art methods and relevant case studies.
User-generated social media content provides an excellent opportunity to mine data of interest and to build resourceful applications. The rise in the number of healthcare-related social media platforms and the volume of healthcare knowledge available online in the last decade has resulted in increased social media usage for personal healthcare. In the United States, nearly ninety percent of adults, in the age group 50-75, have used social media to seek and share health information. Motivated by the growth of social media usage, this thesis focuses on healthcare-related applications, study various challenges posed by social media data, and address them through novel and effective machine learning algorithms.
The major challenges for effectively and efficiently mining social media data to build functional applications include: (1) Data reliability and acceptance: most social media data (especially in the context of healthcare-related social media) is not regulated and little has been studied on the benefits of healthcare-specific social media; (2) Data heterogeneity: social media data is generated by users with both demographic and geographic diversity; (3) Model transparency and trustworthiness: most existing machine learning models for addressing heterogeneity are considered as black box models, not many providing explanations for why they do what they do to trust them.
In response to these challenges, three main research directions have been investigated in this thesis: (1) Analyzing social media influence on healthcare: to study the real world impact of social media as a source to offer or seek support for patients with chronic health conditions; (2) Learning from task heterogeneity: to propose various models and algorithms that are adaptable to new social media platforms and robust to dynamic social media data, specifically on modeling user behaviors, identifying similar actors across platforms, and adapting black box models to a specific learning scenario; (3) Explaining heterogeneous models: to interpret predictive models in the presence of task heterogeneity. In this thesis, novel algorithms with theoretical analysis from various aspects (e.g., time complexity, convergence properties) have been proposed. The effectiveness and efficiency of the proposed algorithms is demonstrated by comparison with state-of-the-art methods and relevant case studies.
ContributorsNelakurthi, Arun Reddy (Author) / He, Jingrui (Thesis advisor) / Cook, Curtiss B (Committee member) / Maciejewski, Ross (Committee member) / Tong, Hanghang (Committee member) / Arizona State University (Publisher)
Created2019
Description
Attributes - that delineating the properties of data, and connections - that describing the dependencies of data, are two essential components to characterize most real-world phenomena. The synergy between these two principal elements renders a unique data representation - the attributed networks. In many cases, people are inundated with vast amounts of data that can be structured into attributed networks, and their use has been attractive to researchers and practitioners in different disciplines. For example, in social media, users interact with each other and also post personalized content; in scientific collaboration, researchers cooperate and are distinct from peers by their unique research interests; in complex diseases studies, rich gene expression complements to the gene-regulatory networks. Clearly, attributed networks are ubiquitous and form a critical component of modern information infrastructure. To gain deep insights from such networks, it requires a fundamental understanding of their unique characteristics and be aware of the related computational challenges.
My dissertation research aims to develop a suite of novel learning algorithms to understand, characterize, and gain actionable insights from attributed networks, to benefit high-impact real-world applications. In the first part of this dissertation, I mainly focus on developing learning algorithms for attributed networks in a static environment at two different levels: (i) attribute level - by designing feature selection algorithms to find high-quality features that are tightly correlated with the network topology; and (ii) node level - by presenting network embedding algorithms to learn discriminative node embeddings by preserving node proximity w.r.t. network topology structure and node attribute similarity. As changes are essential components of attributed networks and the results of learning algorithms will become stale over time, in the second part of this dissertation, I propose a family of online algorithms for attributed networks in a dynamic environment to continuously update the learning results on the fly. In fact, developing application-aware learning algorithms is more desired with a clear understanding of the application domains and their unique intents. As such, in the third part of this dissertation, I am also committed to advancing real-world applications on attributed networks by incorporating the objectives of external tasks into the learning process.
My dissertation research aims to develop a suite of novel learning algorithms to understand, characterize, and gain actionable insights from attributed networks, to benefit high-impact real-world applications. In the first part of this dissertation, I mainly focus on developing learning algorithms for attributed networks in a static environment at two different levels: (i) attribute level - by designing feature selection algorithms to find high-quality features that are tightly correlated with the network topology; and (ii) node level - by presenting network embedding algorithms to learn discriminative node embeddings by preserving node proximity w.r.t. network topology structure and node attribute similarity. As changes are essential components of attributed networks and the results of learning algorithms will become stale over time, in the second part of this dissertation, I propose a family of online algorithms for attributed networks in a dynamic environment to continuously update the learning results on the fly. In fact, developing application-aware learning algorithms is more desired with a clear understanding of the application domains and their unique intents. As such, in the third part of this dissertation, I am also committed to advancing real-world applications on attributed networks by incorporating the objectives of external tasks into the learning process.
ContributorsLi, Jundong (Author) / Liu, Huan (Thesis advisor) / Faloutsos, Christos (Committee member) / He, Jingrui (Committee member) / Xue, Guoliang (Committee member) / Arizona State University (Publisher)
Created2019
Description
Many existing applications of machine learning (ML) to cybersecurity are focused on detecting malicious activity already present in an enterprise. However, recent high-profile cyberattacks proved that certain threats could have been avoided. The speed of contemporary attacks along with the high costs of remediation incentivizes avoidance over response. Yet, avoidance implies the ability to predict - a notoriously difficult task due to high rates of false positives, difficulty in finding data that is indicative of future events, and the unexplainable results from machine learning algorithms.
In this dissertation, these challenges are addressed by presenting three artificial intelligence (AI) approaches to support prioritizing defense measures. The first two approaches leverage ML on cyberthreat intelligence data to predict if exploits are going to be used in the wild. The first work focuses on what data feeds are generated after vulnerability disclosures. The developed ML models outperform the current industry-standard method with F1 score more than doubled. Then, an approach to derive features about who generated the said data feeds is developed. The addition of these features increase recall by over 19% while maintaining precision. Finally, frequent itemset mining is combined with a variant of a probabilistic temporal logic framework to predict when attacks are likely to occur. In this approach, rules correlating malicious activity in the hacking community platforms with real-world cyberattacks are mined. They are then used in a deductive reasoning approach to generate predictions. The developed approach predicted unseen real-world attacks with an average increase in the value of F1 score by over 45%, compared to a baseline approach.
In this dissertation, these challenges are addressed by presenting three artificial intelligence (AI) approaches to support prioritizing defense measures. The first two approaches leverage ML on cyberthreat intelligence data to predict if exploits are going to be used in the wild. The first work focuses on what data feeds are generated after vulnerability disclosures. The developed ML models outperform the current industry-standard method with F1 score more than doubled. Then, an approach to derive features about who generated the said data feeds is developed. The addition of these features increase recall by over 19% while maintaining precision. Finally, frequent itemset mining is combined with a variant of a probabilistic temporal logic framework to predict when attacks are likely to occur. In this approach, rules correlating malicious activity in the hacking community platforms with real-world cyberattacks are mined. They are then used in a deductive reasoning approach to generate predictions. The developed approach predicted unseen real-world attacks with an average increase in the value of F1 score by over 45%, compared to a baseline approach.
ContributorsAlmukaynizi, Mohammed (Author) / Shakarian, Paulo (Thesis advisor) / Huang, Dijiang (Committee member) / Maciejewski, Ross (Committee member) / Simari, Gerardo I. (Committee member) / Arizona State University (Publisher)
Created2019
Description
In this thesis, I present two new datasets and a modification to the existing models in the form of a novel attention mechanism for Natural Language Inference (NLI). The new datasets have been carefully synthesized from various existing corpora released for different tasks.
The task of NLI is to determine the possibility of a sentence referred to as “Hypothesis” being true given that another sentence referred to as “Premise” is true. In other words, the task is to identify whether the “Premise” entails, contradicts or remains neutral with regards to the “Hypothesis”. NLI is a precursor to solving many Natural Language Processing (NLP) tasks such as Question Answering and Semantic Search. For example, in Question Answering systems, the question is paraphrased to form a declarative statement which is treated as the hypothesis. The options are treated as the premise. The option with the maximum entailment score is considered as the answer. Considering the applications of NLI, the importance of having a strong NLI system can't be stressed enough.
Many large-scale datasets and models have been released in order to advance the field of NLI. While all of these models do get good accuracy on the test sets of the datasets they were trained on, they fail to capture the basic understanding of “Entities” and “Roles”. They often make the mistake of inferring that “John went to the market.” from “Peter went to the market.” failing to capture the notion of “Entities”. In other cases, these models don't understand the difference in the “Roles” played by the same entities in “Premise” and “Hypothesis” sentences and end up wrongly inferring that “Peter drove John to the stadium.” from “John drove Peter to the stadium.”
The lack of understanding of “Roles” can be attributed to the lack of such examples in the various existing datasets. The reason for the existing model’s failure in capturing the notion of “Entities” is not just due to the lack of such examples in the existing NLI datasets. It can also be attributed to the strict use of vector similarity in the “word-to-word” attention mechanism being used in the existing architectures.
To overcome these issues, I present two new datasets to help make the NLI systems capture the notion of “Entities” and “Roles”. The “NER Changed” (NC) dataset and the “Role-Switched” (RS) dataset contains examples of Premise-Hypothesis pairs that require the understanding of “Entities” and “Roles” respectively in order to be able to make correct inferences. This work shows how the existing architectures perform poorly on the “NER Changed” (NC) dataset even after being trained on the new datasets. In order to help the existing architectures, understand the notion of “Entities”, this work proposes a modification to the “word-to-word” attention mechanism. Instead of relying on vector similarity alone, the modified architectures learn to incorporate the “Symbolic Similarity” as well by using the Named-Entity features of the Premise and Hypothesis sentences. The new modified architectures not only perform significantly better than the unmodified architectures on the “NER Changed” (NC) dataset but also performs as well on the existing datasets.
The task of NLI is to determine the possibility of a sentence referred to as “Hypothesis” being true given that another sentence referred to as “Premise” is true. In other words, the task is to identify whether the “Premise” entails, contradicts or remains neutral with regards to the “Hypothesis”. NLI is a precursor to solving many Natural Language Processing (NLP) tasks such as Question Answering and Semantic Search. For example, in Question Answering systems, the question is paraphrased to form a declarative statement which is treated as the hypothesis. The options are treated as the premise. The option with the maximum entailment score is considered as the answer. Considering the applications of NLI, the importance of having a strong NLI system can't be stressed enough.
Many large-scale datasets and models have been released in order to advance the field of NLI. While all of these models do get good accuracy on the test sets of the datasets they were trained on, they fail to capture the basic understanding of “Entities” and “Roles”. They often make the mistake of inferring that “John went to the market.” from “Peter went to the market.” failing to capture the notion of “Entities”. In other cases, these models don't understand the difference in the “Roles” played by the same entities in “Premise” and “Hypothesis” sentences and end up wrongly inferring that “Peter drove John to the stadium.” from “John drove Peter to the stadium.”
The lack of understanding of “Roles” can be attributed to the lack of such examples in the various existing datasets. The reason for the existing model’s failure in capturing the notion of “Entities” is not just due to the lack of such examples in the existing NLI datasets. It can also be attributed to the strict use of vector similarity in the “word-to-word” attention mechanism being used in the existing architectures.
To overcome these issues, I present two new datasets to help make the NLI systems capture the notion of “Entities” and “Roles”. The “NER Changed” (NC) dataset and the “Role-Switched” (RS) dataset contains examples of Premise-Hypothesis pairs that require the understanding of “Entities” and “Roles” respectively in order to be able to make correct inferences. This work shows how the existing architectures perform poorly on the “NER Changed” (NC) dataset even after being trained on the new datasets. In order to help the existing architectures, understand the notion of “Entities”, this work proposes a modification to the “word-to-word” attention mechanism. Instead of relying on vector similarity alone, the modified architectures learn to incorporate the “Symbolic Similarity” as well by using the Named-Entity features of the Premise and Hypothesis sentences. The new modified architectures not only perform significantly better than the unmodified architectures on the “NER Changed” (NC) dataset but also performs as well on the existing datasets.
ContributorsShrivastava, Ishan (Author) / Baral, Chitta (Thesis advisor) / Anwar, Saadat (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2019
Description
Time series forecasting is the prediction of future data after analyzing the past data for temporal trends. This work investigates two fields of time series forecasting in the form of Stock Data Prediction and the Opioid Incident Prediction. In this thesis, the Stock Data Prediction Problem investigates methods which could predict the trends in the NYSE and NASDAQ stock markets for ten different companies, nine of which are part of the Dow Jones Industrial Average (DJIA). A novel deep learning model which uses a Generative Adversarial Network (GAN) is used to predict future data and the results are compared with the existing regression techniques like Linear, Huber, and Ridge regression and neural network models such as Long-Short Term Memory (LSTMs) models.
In this thesis, the Opioid Incident Prediction Problem investigates methods which could predict the location of future opioid overdose incidences using the past opioid overdose incidences data. A similar deep learning model is used to predict the location of the future overdose incidences given the two datasets of the past incidences (Connecticut and Cincinnati Opioid incidence datasets) and compared with the existing neural network models such as Convolution LSTMs, Attention-based Convolution LSTMs, and Encoder-Decoder frameworks. Experimental results on the above-mentioned datasets for both the problems show the superiority of the proposed architectures over the standard statistical models.
In this thesis, the Opioid Incident Prediction Problem investigates methods which could predict the location of future opioid overdose incidences using the past opioid overdose incidences data. A similar deep learning model is used to predict the location of the future overdose incidences given the two datasets of the past incidences (Connecticut and Cincinnati Opioid incidence datasets) and compared with the existing neural network models such as Convolution LSTMs, Attention-based Convolution LSTMs, and Encoder-Decoder frameworks. Experimental results on the above-mentioned datasets for both the problems show the superiority of the proposed architectures over the standard statistical models.
ContributorsThomas, Kevin, M.S (Author) / Sen, Arunabha (Thesis advisor) / Davulcu, Hasan (Committee member) / Banerjee, Ayan (Committee member) / Arizona State University (Publisher)
Created2019
Description
Playlists have become a significant part of the music listening experience today because of the digital cloud-based services such as Spotify, Pandora, Apple Music. Owing to the meteoric rise in usage of playlists, recommending playlists is crucial to music services today. Although there has been a lot of work done in playlist prediction, the area of playlist representation hasn't received that level of attention. Over the last few years, sequence-to-sequence models, especially in the field of natural language processing have shown the effectiveness of learned embeddings in capturing the semantic characteristics of sequences. Similar concepts can be applied to music to learn fixed length representations for playlists and the learned representations can then be used for downstream tasks such as playlist comparison and recommendation.
In this thesis, the problem of learning a fixed-length representation is formulated in an unsupervised manner, using Neural Machine Translation (NMT), where playlists are interpreted as sentences and songs as words. This approach is compared with other encoding architectures and evaluated using the suite of tasks commonly used for evaluating sentence embeddings, along with a few additional tasks pertaining to music. The aim of the evaluation is to study the traits captured by the playlist embeddings such that these can be leveraged for music recommendation purposes. This work lays down the foundation for analyzing music playlists and learning the patterns that exist in the playlists in an end-to-end manner. This thesis finally concludes with a discussion on the future direction for this research and its potential impact in the domain of Music Information Retrieval.
In this thesis, the problem of learning a fixed-length representation is formulated in an unsupervised manner, using Neural Machine Translation (NMT), where playlists are interpreted as sentences and songs as words. This approach is compared with other encoding architectures and evaluated using the suite of tasks commonly used for evaluating sentence embeddings, along with a few additional tasks pertaining to music. The aim of the evaluation is to study the traits captured by the playlist embeddings such that these can be leveraged for music recommendation purposes. This work lays down the foundation for analyzing music playlists and learning the patterns that exist in the playlists in an end-to-end manner. This thesis finally concludes with a discussion on the future direction for this research and its potential impact in the domain of Music Information Retrieval.
ContributorsPapreja, Piyush (Author) / Panchanathan, Sethuraman (Thesis advisor) / Demakethepalli Venkateswara, Hemanth Kumar (Committee member) / Amor, Heni Ben (Committee member) / Arizona State University (Publisher)
Created2019
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