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Taxthemis: Interactive Mining And Exploration Of Suspicious Tax Evasion Group, Yating LIN, Kamkwai WONG, Yong WANG, Rong ZHANG, Bo DONG, Huamin QU, Qinghua ZHENG 2021 Singapore Management University

Taxthemis: Interactive Mining And Exploration Of Suspicious Tax Evasion Group, Yating Lin, Kamkwai Wong, Yong Wang, Rong Zhang, Bo Dong, Huamin Qu, Qinghua Zheng

Research Collection School Of Computing and Information Systems

Tax evasion is a serious economic problem for many countries, as it can undermine the government’s tax system and lead to an unfair business competition environment. Recent research has applied data analytics techniques to analyze and detect tax evasion behaviors of individual taxpayers. However, they have failed to support the analysis and exploration of the related party transaction tax evasion (RPTTE) behaviors (e.g., transfer pricing), where a group of taxpayers is involved. In this paper, we present TaxThemis, an interactive visual analytics system to help tax officers mine and explore suspicious tax evasion groups through analyzing heterogeneous tax-related ...


Analysis Of Theoretical And Applied Machine Learning Models For Network Intrusion Detection, Jonah Baron 2021 Dakota State University

Analysis Of Theoretical And Applied Machine Learning Models For Network Intrusion Detection, Jonah Baron

Masters Theses & Doctoral Dissertations

Network Intrusion Detection System (IDS) devices play a crucial role in the realm of network security. These systems generate alerts for security analysts by performing signature-based and anomaly-based detection on malicious network traffic. However, there are several challenges when configuring and fine-tuning these IDS devices for high accuracy and precision. Machine learning utilizes a variety of algorithms and unique dataset input to generate models for effective classification. These machine learning techniques can be applied to IDS devices to classify and filter anomalous network traffic. This combination of machine learning and network security provides improved automated network defense by developing highly-optimized ...


Semi-Supervised Spatial-Temporal Feature Learning On Anomaly-Based Network Intrusion Detection, Huy Mai 2021 University of Arkansas, Fayetteville

Semi-Supervised Spatial-Temporal Feature Learning On Anomaly-Based Network Intrusion Detection, Huy Mai

Computer Science and Computer Engineering Undergraduate Honors Theses

Due to a rapid increase in network traffic, it is growing more imperative to have systems that detect attacks that are both known and unknown to networks. Anomaly-based detection methods utilize deep learning techniques, including semi-supervised learning, in order to effectively detect these attacks. Semi-supervision is advantageous as it doesn't fully depend on the labelling of network traffic data points, which may be a daunting task especially considering the amount of traffic data collected. Even though deep learning models such as the convolutional neural network have been integrated into a number of proposed network intrusion detection systems in recent ...


Trust Models And Risk In The Internet Of Things, Jeffrey Hemmes 2021 Regis University

Trust Models And Risk In The Internet Of Things, Jeffrey Hemmes

Regis University Faculty Publications

The Internet of Things (IoT) is envisaged to be a large-scale, massively heterogeneous ecosystem of devices with varying purposes and capabilities. While architectures and frameworks have focused on functionality and performance, security is a critical aspect that must be integrated into system design. This work proposes a method of risk assessment of devices using both trust models and static capability profiles to determine the level of risk each device poses. By combining the concepts of trust and secure device fingerprinting, security mechanisms can be more efficiently allocated across networked IoT devices. Simultaneously, devices can be allowed a greater degree of ...


Exploring Ai And Multiplayer In Java, Ronni Kurtzhals 2021 Minnesota State University Moorhead

Exploring Ai And Multiplayer In Java, Ronni Kurtzhals

Student Academic Conference

I conducted research into three topics: artificial intelligence, package deployment, and multiplayer servers in Java. This research came together to form my project presentation on the implementation of these topics, which I felt accurately demonstrated the various things I have learned from my courses at Moorhead State University. Several resources were consulted throughout the project, including the work of W3Schools and StackOverflow as well as relevant assignments and textbooks from previous classes. I found this project relevant to computer science and information systems for several reasons, such as the AI component and use of SQL data tables; but it was ...


New Topology Control Base On Ant Colony Algorithm In Optimization Of Wireless Sensor Network, Zana Azeez Kakarash, Sarkhel H.Taher Karim, Nawroz Fadhil Ahmed, Govar Abubakr Omar 2021 Department of Engineering, Faculty of Engineering and Computer Science, Qaiwan International University, Sulaymaniyah, Iraq, Department of Information Technology, Kurdistan Technical Institute, Sulaymaniyah, Kurdistan Region, Iraq

New Topology Control Base On Ant Colony Algorithm In Optimization Of Wireless Sensor Network, Zana Azeez Kakarash, Sarkhel H.Taher Karim, Nawroz Fadhil Ahmed, Govar Abubakr Omar

Passer Journal

Wireless sensor networks (WSNs) have found great appeal and popularity among researchers, especially in the field of monitoring and surveillance tasks. However, it has become a challenging issue due to the need to balance different optimization criteria such as power consumption, packet loss rate, and network lifetime, and coverage. The novelty of this research discusses the applications, structures, challenges, and issues we face in designing WSNs. And proposed new Topology control mechanisms it will focus more on building a reliable and energy efficient network topology step by step through defining available amount of energy for each node within its cluster ...


Concentration Inequalities In The Wild: Case Studies In Blockchain & Reinforcement Learning, A. Pinar Ozisik 2021 University of Massachusetts Amherst

Concentration Inequalities In The Wild: Case Studies In Blockchain & Reinforcement Learning, A. Pinar Ozisik

Doctoral Dissertations

Concentration inequalities (CIs) are a powerful tool that provide probability bounds on how a random variable deviates from its expectation. In this dissertation, first I describe a blockchain protocol that I have developed, called Graphene, which uses CIs to provide probabilistic guarantees on performance. Second, I analyze the extent to which CIs are robust when the assumptions they require are violated, using Reinforcement Learning (RL) as the domain.

Graphene is a method for interactive set reconciliation among peers in blockchains and related distributed systems. Through the novel combination of a Bloom filter and an Invertible Bloom Lookup Table, Graphene uses ...


Practical Server-Side Wifi-Based Indoor Localization: Addressing Cardinality & Outlier Challenges For Improved Occupancy Estimation, Anuradha RAVI, Archan MISRA 2021 Singapore Management University

Practical Server-Side Wifi-Based Indoor Localization: Addressing Cardinality & Outlier Challenges For Improved Occupancy Estimation, Anuradha Ravi, Archan Misra

Research Collection School Of Computing and Information Systems

Server-side WiFi-based indoor localization offers a compelling approach for passive occupancy estimation (i.e., without requiring active participation by client devices, such as smartphones carried by visitors), but is known to suffer from median error of 6–8 meters. By analyzing the characteristics of an operationally-deployed, WiFi-based passive indoor location system, based on the classical RADAR algorithm, we identify and tackle 2 practical challenges for accurate individual device localization. The first challenge is the low-cardinality issue, whereby only the associated AP generates sufficiently frequent RSSI reports, causing a client to experience large localization error due to the absence of sufficient ...


Analysis Of System Performance Metrics Towards The Detection Of Cryptojacking In Iot Devices, Richard Matthews 2021 Dakota State University

Analysis Of System Performance Metrics Towards The Detection Of Cryptojacking In Iot Devices, Richard Matthews

Masters Theses & Doctoral Dissertations

This single-case mechanism study examined the effects of cryptojacking on Internet of Things (IoT) device performance metrics. Cryptojacking is a cyber-threat that involves stealing the computational resources of devices belonging to others to generate cryptocurrencies. The resources primarily include the processing cycles of devices and the additional electricity needed to power this additional load. The literature surveyed showed that cryptojacking has been gaining in popularity and is now one of the top cyberthreats. Cryptocurrencies offer anyone more freedom and anonymity than dealing with traditional financial institutions which make them especially attractive to cybercriminals. Other reasons for the increasing popularity of ...


On-Device Deep Learning Inference For System-On-Chip (Soc) Architectures, Tom Springer, Elia Eiroa-Lledo, Elizabeth Stevens, Erik Linstead 2021 Chapman University

On-Device Deep Learning Inference For System-On-Chip (Soc) Architectures, Tom Springer, Elia Eiroa-Lledo, Elizabeth Stevens, Erik Linstead

Engineering Faculty Articles and Research

As machine learning becomes ubiquitous, the need to deploy models on real-time, embedded systems will become increasingly critical. This is especially true for deep learning solutions, whose large models pose interesting challenges for target architectures at the “edge” that are resource-constrained. The realization of machine learning, and deep learning, is being driven by the availability of specialized hardware, such as system-on-chip solutions, which provide some alleviation of constraints. Equally important, however, are the operating systems that run on this hardware, and specifically the ability to leverage commercial real-time operating systems which, unlike general purpose operating systems such as Linux, can ...


Towards Identity Relationship Management For Internet Of Things, Mohammad Muntasir Nur 2021 Dakota State University

Towards Identity Relationship Management For Internet Of Things, Mohammad Muntasir Nur

Masters Theses & Doctoral Dissertations

Identity and Access Management (IAM) is in the core of any information systems. Traditional IAM systems manage users, applications, and devices within organizational boundaries, and utilize static intelligence for authentication and access control. Identity federation has helped a lot to deal with boundary limitation, but still limited to static intelligence – users, applications and devices must be under known boundaries. However, today’s IAM requirements are much more complex. Boundaries between enterprise and consumer space, on premises and cloud, personal devices and organization owned devices, and home, work and public places are fading away. These challenges get more complicated for Internet ...


Traversing Nat: A Problem, Tyler Flaagan 2021 Dakota State University

Traversing Nat: A Problem, Tyler Flaagan

Masters Theses & Doctoral Dissertations

This quasi-experimental before-and-after study measured and analyzed the impacts of adding security to a new bi-directional Network Address Translation (NAT). Literature revolves around various types of NAT, their advantages and disadvantages, their security models, and networking technologies’ adoption. The study of the newly created secure bi-directional model of NAT showed statistically significant changes in the variables than another model using port forwarding. Future research of how data will traverse networks is crucial in an ever-changing world of technology.


Load Balancing And Resource Allocation In Smart Cities Using Reinforcement Learning, Aseel AlOrbani 2021 The University of Western Ontario

Load Balancing And Resource Allocation In Smart Cities Using Reinforcement Learning, Aseel Alorbani

Electronic Thesis and Dissertation Repository

Today, smart city technology is being adopted by many municipal governments to improve their services and to adapt to growing and changing urban population. Implementing a smart city application can be one of the most challenging projects due to the complexity, requirements and constraints. Sensing devices and computing components can be numerous and heterogeneous. Increasingly, researchers working in the smart city arena are looking to leverage edge and cloud computing to support smart city development. This approach also brings a number of challenges. Two of the main challenges are resource allocation and load balancing of tasks associated with processing data ...


Building Effective Network Security Frameworks Using Deep Transfer Learning Techniques, Harsh Dhillon 2021 The University of Western Ontario

Building Effective Network Security Frameworks Using Deep Transfer Learning Techniques, Harsh Dhillon

Electronic Thesis and Dissertation Repository

Network traffic is growing at an outpaced speed globally. According to the 2020 Cisco Annual Report, nearly two-thirds of the global population will have internet connectivity by the year 2023. The number of devices connected to IP networks will also triple the total world population's size by the same year. The vastness of forecasted network infrastructure opens opportunities for new technologies and businesses to take shape, but it also increases the surface of security vulnerabilities. The number of cyberattacks are growing worldwide and are becoming more diverse and sophisticated. Classic network intrusion detection architectures monitor a system to detect ...


Automated Filtering Of Eye Movements Using Dynamic Aoi In Multiple Granularity Levels, Gavindya Jayawardena, Sampath Jayarathna 2021 Old Dominion University

Automated Filtering Of Eye Movements Using Dynamic Aoi In Multiple Granularity Levels, Gavindya Jayawardena, Sampath Jayarathna

Computer Science Faculty Publications

Eye-tracking experiments involve areas of interest (AOIs) for the analysis of eye gaze data. While there are tools to delineate AOIs to extract eye movement data, they may require users to manually draw boundaries of AOIs on eye tracking stimuli or use markers to define AOIs. This paper introduces two novel techniques to dynamically filter eye movement data from AOIs for the analysis of eye metrics from multiple levels of granularity. The authors incorporate pre-trained object detectors and object instance segmentation models for offline detection of dynamic AOIs in video streams. This research presents the implementation and evaluation of object ...


Integrating The Bullet Physics Engine Into Minecraft, Ethan Johnson 2021 Minnesota State University Moorhead

Integrating The Bullet Physics Engine Into Minecraft, Ethan Johnson

Student Academic Conference

During the past fall semester, I started a programming project called Rayon which is designed to be a realistic physics engine implementation that runs alongside the videogame Minecraft. It is a library which Minecraft mod developers can use to implement realistic entity movement into their own mods. Rayon, being entirely written in the Java programming language, currently uses a port of the Bullet physics engine called JBullet which is very outdated and no longer being maintained. To find a more performant solution, I have set out to replace JBullet with an alternative library called LibBulletJME which is designed to interface ...


Lightweight Encryption Based Security Package For Wireless Body Area Network, Sangwon Shin 2021 South Dakota State University

Lightweight Encryption Based Security Package For Wireless Body Area Network, Sangwon Shin

Electronic Theses and Dissertations

As the demand of individual health monitoring rose, Wireless Body Area Networks (WBAN) are becoming highly distinctive within health applications. Nowadays, WBAN is much easier to access then what it used to be. However, due to WBAN’s limitation, properly sophisticated security protocols do not exist. As WBAN devices deal with sensitive data and could be used as a threat to the owner of the data or their family, securing individual devices is highly important. Despite the importance in securing data, existing WBAN security methods are focused on providing light weight security methods. This led to most security methods for ...


Secure Network Access Via Ldap, Nicholas Valaitis 2021 The University of Akron

Secure Network Access Via Ldap, Nicholas Valaitis

Williams Honors College, Honors Research Projects

Networks need the ability to be access by secure accounts and users. The goal of this project is to configure and expand on LDAP configurations with considerations for AAA via TACACS+ and Radius for network equipment. This will provide adequate security for any given network in terms of access and prevent lose of access to devices which happens all to often with locally configured accounts on devices.


Preforming A Vulnerability Assessment On A Secured Network, Mathias Sovine 2021 The University of Akron

Preforming A Vulnerability Assessment On A Secured Network, Mathias Sovine

Williams Honors College, Honors Research Projects

A computer network will be built using 3 routers, 1 switch, and 4 computers. The network will be used to simulate the connections between an at home office and the internet. The network will be divided into 3 sub-networks. The routers will be secured using methods like access control lists, changing default admin passwords, and network encryption. The switch will be secured using methods like switchport security and setting access passwords. Once the network is secured, three penetration testing techniques and three exploits will be performed on the network. The results of the exploits and penetration testing techniques will be ...


Algorithms For Massive, Expensive, Or Otherwise Inconvenient Graphs, David Tench 2020 University of Massachusetts Amherst

Algorithms For Massive, Expensive, Or Otherwise Inconvenient Graphs, David Tench

Doctoral Dissertations

A long-standing assumption common in algorithm design is that any part of the input is accessible at any time for unit cost. However, as we work with increasingly large data sets, or as we build smaller devices, we must revisit this assumption. In this thesis, I present some of my work on graph algorithms designed for circumstances where traditional assumptions about inputs do not apply.
1. Classical graph algorithms require direct access to the input graph and this is not feasible when the graph is too large to fit in memory. For computation on massive graphs we consider the dynamic ...


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