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- All Subjects: Computer Science
- Creators: Xue, Guoliang
- Member of: ASU Electronic Theses and Dissertations
- Status: Published
Description
The rapid growth in the high-throughput technologies last few decades makes the manual processing of the generated data to be impracticable. Even worse, the machine learning and data mining techniques seemed to be paralyzed against these massive datasets. High-dimensionality is one of the most common challenges for machine learning and data mining tasks. Feature selection aims to reduce dimensionality by selecting a small subset of the features that perform at least as good as the full feature set. Generally, the learning performance, e.g. classification accuracy, and algorithm complexity are used to measure the quality of the algorithm. Recently, the stability of feature selection algorithms has gained an increasing attention as a new indicator due to the necessity to select similar subsets of features each time when the algorithm is run on the same dataset even in the presence of a small amount of perturbation. In order to cure the selection stability issue, we should understand the cause of instability first. In this dissertation, we will investigate the causes of instability in high-dimensional datasets using well-known feature selection algorithms. As a result, we found that the stability mostly data-dependent. According to these findings, we propose a framework to improve selection stability by solving these main causes. In particular, we found that data noise greatly impacts the stability and the learning performance as well. So, we proposed to reduce it in order to improve both selection stability and learning performance. However, current noise reduction approaches are not able to distinguish between data noise and variation in samples from different classes. For this reason, we overcome this limitation by using Supervised noise reduction via Low Rank Matrix Approximation, SLRMA for short. The proposed framework has proved to be successful on different types of datasets with high-dimensionality, such as microarrays and images datasets. However, this framework cannot handle unlabeled, hence, we propose Local SVD to overcome this limitation.
ContributorsAlelyani, Salem (Author) / Liu, Huan (Thesis advisor) / Xue, Guoliang (Committee member) / Ye, Jieping (Committee member) / Zhao, Zheng (Committee member) / Arizona State University (Publisher)
Created2013
Description
While network problems have been addressed using a central administrative domain with a single objective, the devices in most networks are actually not owned by a single entity but by many individual entities. These entities make their decisions independently and selfishly, and maybe cooperate with a small group of other entities only when this form of coalition yields a better return. The interaction among multiple independent decision-makers necessitates the use of game theory, including economic notions related to markets and incentives. In this dissertation, we are interested in modeling, analyzing, addressing network problems caused by the selfish behavior of network entities. First, we study how the selfish behavior of network entities affects the system performance while users are competing for limited resource. For this resource allocation domain, we aim to study the selfish routing problem in networks with fair queuing on links, the relay assignment problem in cooperative networks, and the channel allocation problem in wireless networks. Another important aspect of this dissertation is the study of designing efficient mechanisms to incentivize network entities to achieve certain system objective. For this incentive mechanism domain, we aim to motivate wireless devices to serve as relays for cooperative communication, and to recruit smartphones for crowdsourcing. In addition, we apply different game theoretic approaches to problems in security and privacy domain. For this domain, we aim to analyze how a user could defend against a smart jammer, who can quickly learn about the user's transmission power. We also design mechanisms to encourage mobile phone users to participate in location privacy protection, in order to achieve k-anonymity.
ContributorsYang, Dejun (Author) / Xue, Guoliang (Thesis advisor) / Richa, Andrea (Committee member) / Sen, Arunabha (Committee member) / Zhang, Junshan (Committee member) / Arizona State University (Publisher)
Created2013
Description
Peer-to-peer systems are known to be vulnerable to the Sybil attack. The lack of a central authority allows a malicious user to create many fake identities (called Sybil nodes) pretending to be independent honest nodes. The goal of the malicious user is to influence the system on his/her behalf. In order to detect the Sybil nodes and prevent the attack, a reputation system is used for the nodes, built through observing its interactions with its peers. The construction makes every node a part of a distributed authority that keeps records on the reputation and behavior of the nodes. Records of interactions between nodes are broadcast by the interacting nodes and honest reporting proves to be a Nash Equilibrium for correct (non-Sybil) nodes. In this research is argued that in realistic communication schedule scenarios, simple graph-theoretic queries such as the computation of Strongly Connected Components and Densest Subgraphs, help in exposing those nodes most likely to be Sybil, which are then proved to be Sybil or not through a direct test executed by some peers.
ContributorsCárdenas-Haro, José Antonio (Author) / Konjevod, Goran (Thesis advisor) / Richa, Andréa W. (Thesis advisor) / Sen, Arunabha (Committee member) / Xue, Guoliang (Committee member) / Arizona State University (Publisher)
Created2010
Description
The omnipresent data, growing number of network devices, and evolving attack techniques have been challenging organizations’ security defenses over the past decade. With humongous volumes of logs generated by those network devices, looking for patterns of malicious activities and identifying them in time is growing beyond the capabilities of their defense systems. Deep Learning, a subset of Machine Learning (ML) and Artificial Intelligence (AI), fills in this gapwith its ability to learn from huge amounts of data, and improve its performance as the data it learns from increases. In this dissertation, I bring forward security issues pertaining to two top threats that most organizations fear, Advanced Persistent Threat (APT), and Distributed Denial of Service (DDoS), along with deep learning models built towards addressing those
security issues. First, I present a deep learning model, APT Detection, capable of detecting anomalous activities in a system. Evaluation of this model demonstrates how it can contribute to early detection of an APT attack with an Area Under the Curve (AUC) of up to 91% on a Receiver Operating Characteristic (ROC) curve. Second, I present DAPT2020, a first of its kind dataset capturing an APT attack exploiting web and system vulnerabilities in an emulated organization’s production network. Evaluation of the dataset using well known machine learning models demonstrates the need for better deep learning models to detect APT attacks. I then present DAPT2021, a semi-synthetic dataset capturing an APT attackexploiting human vulnerabilities, alongside 2 less skilled attacks. By emulating the normal behavior of the employees in a set target organization, DAPT2021 has been created to enable researchers study the causations and correlations among the captured data, a much-needed information to detect an underlying threat early. Finally, I present a distributed defense
framework, SmartDefense, that can detect and mitigate over 90% of DDoS traffic at the source and over 97.5% of the remaining DDoS traffic at the Internet Service Provider’s (ISP’s) edge network. Evaluation of this work shows how by using attributes sent by customer edge network, SmartDefense can further help ISPs prevent up to 51.95% of the DDoS traffic
from going to the destination.
ContributorsMyneni, Sowmya (Author) / Xue, Guoliang (Thesis advisor) / Doupe, Adam (Committee member) / Li, Baoxin (Committee member) / Baral, Chitta (Committee member) / Arizona State University (Publisher)
Created2022
Description
Recent advances in cyber-physical systems, artificial intelligence, and cloud computing have driven the widespread deployment of Internet-of-Things (IoT) devices in smart homes. However, the spate of cyber attacks exploiting the vulnerabilities and weak security management of smart home IoT devices have highlighted the urgency and challenges of designing efficient mechanisms for detecting, analyzing, and mitigating security threats towards them. In this dissertation, I seek to address the security and privacy issues of smart home IoT devices from the perspectives of traffic measurement, pattern recognition, and security applications. I first propose an efficient multidimensional smart home network traffic measurement framework, which enables me to deeply understand the smart home IoT ecosystem and detect various vulnerabilities and flaws. I further design intelligent schemes to efficiently extract security-related IoT device event and user activity patterns from the encrypted smart home network traffic. Based on the knowledge of how smart home operates, different systems for securing smart home networks are proposed and implemented, including abnormal network traffic detection across multiple IoT networking protocol layers, smart home safety monitoring with extracted spatial information about IoT device events, and system-level IoT vulnerability analysis and network hardening.
ContributorsWan, Yinxin (Author) / Xue, Guoliang (Thesis advisor) / Xu, Kuai (Thesis advisor) / Yang, Yezhou (Committee member) / Zhang, Yanchao (Committee member) / Arizona State University (Publisher)
Created2023
Description
Graph-structured data, ranging from social networks to financial transaction networks, from citation networks to gene regulatory networks, have been widely used for modeling a myriad of real-world systems. As a prevailing model architecture to model graph-structured data, graph neural networks (GNNs) has drawn much attention in both academic and industrial communities in the past decades. Despite their success in different graph learning tasks, existing methods usually rely on learning from ``big'' data, requiring a large amount of labeled data for model training. However, it is common that real-world graphs are associated with ``small'' labeled data as data annotation and labeling on graphs is always time and resource-consuming. Therefore, it is imperative to investigate graph machine learning (Graph ML) with low-cost human supervision for low-resource settings where limited or even no labeled data is available. This dissertation investigates a new research field -- Data-Efficient Graph Learning, which aims to push forward the performance boundary of graph machine learning (Graph ML) models with different kinds of low-cost supervision signals. To achieve this goal, a series of studies are conducted for solving different data-efficient graph learning problems, including graph few-shot learning, graph weakly-supervised learning, and graph self-supervised learning.
ContributorsDing, Kaize (Author) / Liu, Huan (Thesis advisor) / Xue, Guoliang (Committee member) / Yang, Yezhou (Committee member) / Caverlee, James (Committee member) / Arizona State University (Publisher)
Created2023
Description
Modern data center networks require efficient and scalable security analysis approaches that can analyze the relationship between the vulnerabilities. Utilizing the Attack Representation Methods (ARMs) and Attack Graphs (AGs) enables the security administrator to understand the cloud network’s current security situation at the low-level. However, the AG approach suffers from scalability challenges. It relies on the connectivity between the services and the vulnerabilities associated with the services to allow the system administrator to realize its security state. In addition, the security policies created by the administrator can have conflicts among them, which is often detected in the data plane of the Software Defined Networking (SDN) system. Such conflicts can cause security breaches and increase the flow rules processing delay. This dissertation addresses these challenges with novel solutions to tackle the scalability issue of Attack Graphs and detect security policy conflictsin the application plane before they are transmitted into the data plane for final
installation. Specifically, it introduces a segmentation-based scalable security state
(S3) framework for the cloud network. This framework utilizes the well-known divide-and-conquer approach to divide the large network region into smaller, manageable segments. It follows a well-known segmentation approach derived from the K-means clustering algorithm to partition the system into segments based on the similarity between the services. Furthermore, the dissertation presents unified intent rules that abstract the network administration from the underlying network controller’s format. It develops a networking service solution to use a bounded formal model for network service compliance checking that significantly reduces the complexity of flow rule conflict checking at the data plane level. The solution can be expended from a single SDN domain to multiple SDN domains and hybrid networks by applying network service function chaining (SFC) for inter-domain policy management.
ContributorsSabur, Abdulhakim (Author) / Zhao, Ming (Thesis advisor) / Xue, Guoliang (Committee member) / Davulcu, Hasan (Committee member) / Zhang, Yanchao (Committee member) / Arizona State University (Publisher)
Created2023
Description
The problem of monitoring complex networks for the detection of anomalous behavior is well known. Sensors are usually deployed for the purpose of monitoring these networks for anomalies and Sensor Placement Optimization (SPO) is the problem of determining where these sensors should be placed (deployed) in the network. Prior works have utilized the well known Set Cover formulation in order to determine the locations where sensors should be placed in the network, so that anomalies can be effectively detected. However, such works cannot be utilized to address the problem when the objective is to not only detect the presence of anomalies, but also to detect (distinguish) the source(s) of the detected anomalies, i.e., uniquely monitoring the network. In this dissertation, I attempt to fill in this gap by utilizing the mathematical concept of Identifying Codes and illustrating how it not only can overcome the aforementioned limitation, but also it, and its variants, can be utilized to monitor complex networks modeled from multiple domains. Over the course of this dissertation, I make key contributions which further enhance the efficacy and applicability of Identifying Codes as a monitoring strategy. First, I show how Identifying Codes are superior to not only the Set Cover formulation but also standard graph centrality metrics, for the purpose of uniquely monitoring complex networks. Second, I study novel problems such as the budget constrained Identifying Code, scalable Identifying Code, robust Identifying Code etc., and present algorithms and results for the respective problems. Third, I present useful Identifying Code results for restricted graph classes such as Unit Interval Bigraphs and Unit Disc Bigraphs. Finally, I show the universality of Identifying Codes by applying it to multiple domains.
ContributorsBasu, Kaustav (Author) / Sen, Arunabha (Thesis advisor) / Davulcu, Hasan (Committee member) / Liu, Huan (Committee member) / Xue, Guoliang (Committee member) / Arizona State University (Publisher)
Created2022
Description
This dissertation investigates the problem of efficiently and effectively prioritizing a vulnerability risk in a computer networking system. Vulnerability prioritization is one of the most challenging issues in vulnerability management, which affects allocating preventive and defensive resources in a computer networking system. Due to the large number of identified vulnerabilities, it is very challenging to remediate them all in a timely fashion. Thus, an efficient and effective vulnerability prioritization framework is required. To deal with this challenge, this dissertation proposes a novel risk-based vulnerability prioritization framework that integrates the recent artificial intelligence techniques (i.e., neuro-symbolic computing and logic reasoning). The proposed work enhances the vulnerability management process by prioritizing vulnerabilities with high risk by refining the initial risk assessment with the network constraints. This dissertation is organized as follows. The first part of this dissertation presents the overview of the proposed risk-based vulnerability prioritization framework, which contains two stages. The second part of the dissertation investigates vulnerability risk features in a computer networking system. The third part proposes the first stage of this framework, a vulnerability risk assessment model. The proposed assessment model captures the pattern of vulnerability risk features to provide a more comprehensive risk assessment for a vulnerability. The fourth part proposes the second stage of this framework, a vulnerability prioritization reasoning engine. This reasoning engine derives network constraints from interactions between vulnerabilities and network environment elements based on network and system setups. This proposed framework assesses a vulnerability in a computer networking system based on its actual security impact by refining the initial risk assessment with the network constraints.
ContributorsZeng, Zhen (Author) / Xue, Guoliang (Thesis advisor) / Liu, Huan (Committee member) / Zhao, Ming (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2022
Description
Nowadays, wireless communications and networks have been widely used in our daily lives. One of the most important topics related to networking research is using optimization tools to improve the utilization of network resources. In this dissertation, we concentrate on optimization for resource-constrained wireless networks, and study two fundamental resource-allocation problems: 1) distributed routing optimization and 2) anypath routing optimization. The study on the distributed routing optimization problem is composed of two main thrusts, targeted at understanding distributed routing and resource optimization for multihop wireless networks. The first thrust is dedicated to understanding the impact of full-duplex transmission on wireless network resource optimization. We propose two provably good distributed algorithms to optimize the resources in a full-duplex wireless network. We prove their optimality and also provide network status analysis using dual space information. The second thrust is dedicated to understanding the influence of network entity load constraints on network resource allocation and routing computation. We propose a provably good distributed algorithm to allocate wireless resources. In addition, we propose a new subgradient optimization framework, which can provide findgrained convergence, optimality, and dual space information at each iteration. This framework can provide a useful theoretical foundation for many networking optimization problems. The study on the anypath routing optimization problem is composed of two main thrusts. The first thrust is dedicated to understanding the computational complexity of multi-constrained anypath routing and designing approximate solutions. We prove that this problem is NP-hard when the number of constraints is larger than one. We present two polynomial time K-approximation algorithms. One is a centralized algorithm while the other one is a distributed algorithm. For the second thrust, we study directional anypath routing and present a cross-layer design of MAC and routing. For the MAC layer, we present a directional anycast MAC. For the routing layer, we propose two polynomial time routing algorithms to compute directional anypaths based on two antenna models, and prove their ptimality based on the packet delivery ratio metric.
ContributorsFang, Xi (Author) / Xue, Guoliang (Thesis advisor) / Yau, Sik-Sang (Committee member) / Ye, Jieping (Committee member) / Zhang, Junshan (Committee member) / Arizona State University (Publisher)
Created2013