Taxthemis: Interactive Mining And Exploration Of Suspicious Tax Evasion Group, 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 ...
Windows Kernel Hijacking Is Not An Option: Memoryranger Comes To The Rescue Again, 2021 Independent Researcher
Windows Kernel Hijacking Is Not An Option: Memoryranger Comes To The Rescue Again, Igor Korkin
Journal of Digital Forensics, Security and Law
The security of a computer system depends on OS kernel protection. It is crucial to reveal and inspect new attacks on kernel data, as these are used by hackers. The purpose of this paper is to continue research into attacks on dynamically allocated data in the Windows OS kernel and demonstrate the capacity of MemoryRanger to prevent these attacks. This paper discusses three new hijacking attacks on kernel data, which are based on bypassing OS security mechanisms. The first two hijacking attacks result in illegal access to files open in exclusive access. The third attack escalates process privileges, without applying ...
Witness For Two-Site Enabled Coordination, 2021 San Jose State University
Witness For Two-Site Enabled Coordination, Sriram Priyatham Siram
Many replicated data services utilize majority quorums to safely replicate data changes in the presence of server failures. Majority quorum-based services require a simple majority of the servers to be operational for the service to stay available. A key limitation of the majority quorum is that if a service is composed of just two servers, progress cannot be made even if a single server fails because the majority quorum size is also two. This is called the Two-Server problem. A problem similar to the Two-Server problem occurs when a service’s servers are spread across only two failure domains. Servers ...
Pier Ocean Pier, 2021 California Polytechnic State University, San Luis Obispo
Pier Ocean Pier, Brandon J. Nowak
Pier Ocean Peer is a weatherproof box containing a Jetson Nano, connected to a cell modem and camera, and powered by a Lithium Iron Phosphate battery charged by a 50W solar panel. This system can currently provide photos to monitor the harbor seal population that likes to haul out at the base of the Cal Poly Pier, but more importantly it provides a platform for future expansion by other students either though adding new sensors directly to the Jetson Nano or by connecting to the jetson nano remotely through a wireless protocol of their choice.
Higher-Order Link Prediction Using Node And Subgraph Embeddings, 2021 San Jose State University
Higher-Order Link Prediction Using Node And Subgraph Embeddings, Kalpnil Anjan
Social media, academia collaborations, e-commerce websites, biological structures, and other real-world networks are modeled as graphs to represent their entities and relationships in an abstract way. Such graphs are becoming more complex and informative, and by analyzing them we can solve various problems and find hidden insights. Some applications include predicting relationships and potential links between nodes, classifying nodes, and finding the most influential nodes in the graph, etc.
A large amount of research is being done in the field of predicting links between two nodes. However, predicting a future relationship among three or more nodes in a graph is ...
Overlapping Community Detection In Social Networks, 2021 San Jose State University
Overlapping Community Detection In Social Networks, Akshar Panchal
Social networking sites are important to connect with the world virtually. As the number of users accessing these sites increase, the data and information keeps on increasing. There are communities and groups which are formed virtually based on different factors. We can visualize these communities as networks of users or nodes and the relationships or connections between them as edges. This helps in evaluating and analyzing different factors that influence community formation in such a dense network. Community detection helps in revealing certain characteristics which makes these groups in the network unique and different from one another. We can use ...
Fake News Analysis And Graph Classification On A Covid-19 Twitter Dataset, 2021 San Jose State University
Fake News Analysis And Graph Classification On A Covid-19 Twitter Dataset, Kriti Gupta
Earlier researches have showed that the spread of fake news through social media can have a huge impact to society and also to individuals in an extremely negative way. In this work we aim to study the spread of fake news compared to real news in a social network. We do that by performing classical social network analysis to discover various characteristics, and formulate the problem as a binary classification, where we have graphs modeling the spread of fake and real news. For our experiments we rely on how news are propagated through a popular social media services such as ...
Cyberbullying Classification Based On Social Network Analysis, 2021 San Jose State University
Cyberbullying Classification Based On Social Network Analysis, Anqi Wang
With the popularity of social media platforms such as Facebook, Twitter, and Instagram, people widely share their opinions and comments over the Internet. Exten- sive use of social media has also caused a lot of problems. A representative problem is Cyberbullying, which is a serious social problem, mostly among teenagers. Cyber- bullying occurs when a social media user posts aggressive words or phrases to harass other users, and that leads to negatively affects on their mental and social well-being. Additionally, it may ruin the reputation of that media. We are considering the problem of detecting posts that are aggressive. Moreover ...
Using Oracle To Solve Zookeeper On Two-Replica Problems, 2021 San Jose State University
Using Oracle To Solve Zookeeper On Two-Replica Problems, Ching-Chan Lee
The project introduces an Oracle, a failure detector, in Apache ZooKeeper and makes it fault-tolerant in a two-node system. The project demonstrates the Oracle authorizes the primary process to maintain the liveness when the majority’s rule becomes an obstacle to continue Apache ZooKeeper service. In addition to the property of accuracy and completeness from Chandra et al.’s research, the project proposes the property of see to avoid losing transactions and the property of mutual exclusion to avoid split-brain issues. The hybrid properties render not only more sounder flexibility in the implementation but also stronger guarantees on safety. Thus ...
Analysis Of Theoretical And Applied Machine Learning Models For Network Intrusion Detection, 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, 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 ...
A Framework To Detect The Susceptibility Of Employees To Social Engineering Attacks, 2021 Embry-Riddle Aeronautical University
A Framework To Detect The Susceptibility Of Employees To Social Engineering Attacks, Hashim H. Alneami
PhD Dissertations and Master's Theses
Social engineering attacks (SE-attacks) in enterprises are hastily growing and are becoming increasingly sophisticated. Generally, SE-attacks involve the psychological manipulation of employees into revealing confidential and valuable company data to cybercriminals. The ramifications could bring devastating financial and irreparable reputation loss to the companies. Because SE-attacks involve a human element, preventing these attacks can be tricky and challenging and has become a topic of interest for many researchers and security experts. While methods exist for detecting SE-attacks, our literature review of existing methods identified many crucial factors such as the national cultural, organizational, and personality traits of employees that enable ...
Performance Implications Of Memory Affinity On Filesystem Caches In A Non-Uniform Memory Access Environment, Jacob Adams
Undergraduate Honors Theses
Non-Uniform Memory Access imposes unique challenges on every component of an operating system and the applications that run on it. One such component is the filesystem which, while not directly impacted by NUMA in most cases, typically has some form of cache whose performance is constrained by the latency and bandwidth of the memory that it is stored in. One such filesystem is ZFS, which contains its own custom caching system, known as the Adaptive Replacement Cache. This work looks at the impact of NUMA on this cache via sequential read operations, shows how current solutions intended to reduce this ...
Trust Models And Risk In The Internet Of Things, 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, 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, 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
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, 2021 University of Massachusetts Amherst
Concentration Inequalities In The Wild: Case Studies In Blockchain & Reinforcement Learning, A. Pinar Ozisik
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 ...
Toward A Quantum Neural Network: Proposing The Qaoa Algorithm To Replace A Feed Forward Neural Network, 2021 University of Nevada, Las Vegas
Toward A Quantum Neural Network: Proposing The Qaoa Algorithm To Replace A Feed Forward Neural Network, Erick Serrano
Undergraduate Research Symposium Posters
With a surge in popularity of machine learning as a whole, many researchers have sought optimization methods to reduce the complexity of neural networks; however, only recent attempts have been made to optimize neural networks via quantum computing methods. In this paper, we describe the training process of a feed forward neural network (FFNN) and the time complexity of the training process. We highlight the inefficiencies of the FFNN training process, particularly when implemented with gradient descent, and introduce a call to action for optimization of a FFNN. Afterward, we discuss the strides made in quantum computing to improve the ...
Practical Server-Side Wifi-Based Indoor Localization: Addressing Cardinality & Outlier Challenges For Improved Occupancy Estimation, 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, 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 ...