Computer Sciences Commons^™

A Forensic First Look At A Pos Device: Searching For Pci Dss Data Storage Violations, Stephen Larson, James Jones, Jim Swauger

Journal of Digital Forensics, Security and Law

According to the Verizon 2018 Data Breach Investigations Report, 321 POS terminals (user devices) were involved in about 14% of the 2,216 data breaches in 2017 (Verizon, 2018). These data breaches involved standalone POS terminals as well as associated controller systems. This paper examines a standalone Point-of-Sale (POS) system which is ubiquitous in smaller retail stores and restaurants. An attempt to extract unencrypted data and identify possible violations of the Payment Card Industry Data Security Standard (PCI DSS) requirement to protect stored cardholder data were be made. Persistent storage (flash memory chips) were removed from the devices and their contents …

Modified Newtonian Dynamics Effects In A Region Dominated By Dark Matter And A Cosmological Constant Λ, Ioannis Haranas, Kristin Cobbett, Ioannis Gkigkitzis, Athanasios Alexiou, Eli Cavan

Physics and Computer Science Faculty Publications

We study the motion of a secondary celestial body under the influence of a corrected gravitational potential in a modified Newtonian dynamics scenario. Furthermore we look within the Milky-way where the first correction to the potential results from a modified Poisson equation, and includes two mew terms one of which is of the form ln(r/r_max) and the other is associated with the cosmological constant lambda L added to the Newtonian potential. The regions of influence of the two potentials are associated with regions of interested bounded by the conditions for the Newtonian potential, the logarithmic …

Comparing Variable Importance In Prediction Of Silence Behaviours Between Random Forest And Conditional Inference Forest Models., Stephen Barrett Dr, Geraldine Gray Dr, Colm Mcguinness Dr, Michael Knoll Dr.

Articles

This paper explores variable importance metrics of Conditional Inference Trees (CIT) and classical Classification And Regression Trees (CART) based Random Forests. The paper compares both algorithms variable importance rankings and highlights why CIT should be used when dealing with data with different levels of aggregation. The models analysed explored the role of cultural factors at individual and societal level when predicting Organisational Silence behaviours.

Contingency Planning Amidst A Pandemic, Natalie C. Belford

KSU Proceedings on Cybersecurity Education, Research and Practice

Proper prior planning prevents pitifully poor performance: The purpose of this research is to address mitigation approaches - disaster recovery, contingency planning, and continuity planning - and their benefits as they relate to university operations during a worldwide pandemic predicated by the Novel Coronavirus (COVID-19). The most relevant approach pertaining to the University’s needs and its response to the Coronavirus pandemic will be determined and evaluated in detail.

Developing An Ai-Powered Chatbot To Support The Administration Of Middle And High School Cybersecurity Camps, Jonathan He, Chunsheng Xin

KSU Proceedings on Cybersecurity Education, Research and Practice

Throughout the Internet, many chatbots have been deployed by various organizations to answer questions asked by customers. In recent years, we have been running cybersecurity summer camps for youth. Due to COVID-19, our in-person camp has been changed to virtual camps. As a result, we decided to develop a chatbot to reduce the number of emails, phone calls, as well as the human burden for answering the same or similar questions again and again based on questions we received from previous camps. This paper introduces our practical experience to implement an AI-powered chatbot for middle and high school cybersecurity camps …

A Survey Of Serious Games For Cybersecurity Education And Training, Winston Anthony Hill Jr., Mesafint Fanuel, Xiaohong Yuan, Jinghua Zhang, Sajad Sajad

KSU Proceedings on Cybersecurity Education, Research and Practice

Serious games can challenge users in competitive and entertaining ways. Educators have used serious games to increase student engagement in cybersecurity education. Serious games have been developed to teach students various cybersecurity topics such as safe online behavior, threats and attacks, malware, and more. They have been used in cybersecurity training and education at different levels. Serious games have targeted different audiences such as K-12 students, undergraduate and graduate students in academic institutions, and professionals in the cybersecurity workforce. In this paper, we provide a survey of serious games used in cybersecurity education and training. We categorize these games into …

Factors That Influence Hipaa Secure Compliance In Small And Medium-Size Health Care Facilities, Wlad Pierre-Francois, Indira Guzman

KSU Proceedings on Cybersecurity Education, Research and Practice

This study extends the body of literature concerning security compliance by investigating the antecedents of HIPPA security compliance. A conceptual model, specifying a set of hypothesized relationships between management support, security awareness, security culture; security behavior, and risk of sanctions to address their effect on HIPAA security compliance is presented. This model was developed based on the review of the literature, Protection Motivation Theory, and General Deterrence Theory. Specifically, the aim of the study is to examine the mediating role of risk of sanctions on HIPAA security compliance.

Towards An Assessment Of Pause Periods On User Habituation In Mitigation Of Phishing Attacks, Amy Antonucci, Yair Levy, Martha Snyder, Laurie Dringus

KSU Proceedings on Cybersecurity Education, Research and Practice

Social engineering is the technique in which the attacker sends messages to build a relationship with the victim and convinces the victim to take some actions that lead to significant damages and losses. Industry and law enforcement reports indicate that social engineering incidents costs organizations billions of dollars. Phishing is the most pervasive social engineering attack. While email filtering and warning messages have been implemented for over three decades, organizations are constantly falling for phishing attacks. Prior research indicated that attackers use phishing emails to create an urgency and fear response in their victims causing them to use quick heuristics, …

Towards An Assessment Of Judgment Errors In Social Engineering Attacks Due To Environment And Device Type, Tommy Pollock, Yair Levy, Wei Li, Ajoy Kumar

KSU Proceedings on Cybersecurity Education, Research and Practice

Phishing continues to be a significant invasive threat to computer and mobile device users. Cybercriminals continuously develop new phishing schemes using email, and malicious search engine links to gather personal information of unsuspecting users. This information is used for financial gains through identity theft schemes or draining financial accounts of victims. Users are often distracted and fail to fully process the phishing attacks then unknowingly fall victim to the scam until much later. Users operating mobile phones and computers are likely to make judgment errors when making decisions in distracting environments due to cognitive overload. Distracted users can fail to …

Cybersecurity Strategy Against Cyber Attacks Towards Smart Grids With Pvs, Fangyu Li, Maria Valero, Liang Zhao, Yousef Mahmoud

KSU Proceedings on Cybersecurity Education, Research and Practice

Cyber attacks threaten the security of distribution power grids, such as smart grids. The emerging renewable energy sources such as photovoltaics (PVs) with power electronics controllers introduce new potential vulnerabilities. Based on the electric waveform data measured by waveform sensors in the smart grids, we propose a novel cyber attack detection and identification approach. Firstly, we analyze the cyber attack impacts (including cyber attacks on the solar inverter causing unusual harmonics) on electric waveforms in distribution power grids. Then, we propose a novel deep learning based mechanism including attack detection and attack diagnosis. By leveraging the electric waveform sensor data …

Multi-Echo Quantitative Susceptibility Mapping For Strategically Acquired Gradient Echo (Stage) Imaging, Sara Gharabaghi, Saifeng Liu, Ying Wang, Yongsheng Chen, Sagar Buch, Mojtaba Jokar, Thomas Wischgoll, Nasser H. Kashou, Chunyan Zhang, Bo Wu, Jingliang Cheng, E. Mark Haacke

Computer Science and Engineering Faculty Publications

Purpose: To develop a method to reconstruct quantitative susceptibility mapping (QSM) from multi-echo, multi-flip angle data collected using strategically acquired gradient echo (STAGE) imaging. Methods: The proposed QSM reconstruction algorithm, referred to as “structurally constrained Susceptibility Weighted Imaging and Mapping” scSWIM, performs an ℓ1 and ℓ2 regularization-based reconstruction in a single step. The unique contrast of the T1 weighted enhanced (T1WE) image derived from STAGE imaging was used to extract reliable geometry constraints to protect the basal ganglia from over-smoothing. The multi-echo multi-flip angle data were used for improving the contrast-to-noise ratio in QSM through a weighted averaging scheme. The …

The Role Of Information And Knowledge Of Weather Warnings In Marine Access Behavior : A Field Experiment In Coastal Area Of Bangladesh, Khan Mehedi Hasan

Lingnan Theses (MPhil & PhD)

The world’s largest mangrove forest named Sundarban is located in the Bay of Bengal. Due to richness of aqua and forest resources, the coastal community of Khulna district of Bangladesh immensely depends on the forest for income and livelihoods, all the year round. For extracting resources, thousands of people enter into the forest by crossing river, generally with small boats. The region faces various natural disasters repeatedly. Each year about 13-14 cyclones are formed in the Bay of Bengal, which are threats for coastal households. Those hazards bear more risk for marine entrants. Analyzing coastal households’ marine access for two …

Towards High Performance Stock Market Prediction Methods, Warren M. Landis, Sangwhan Cha

Other Student Works

Stock markets of today, and will continue to in the future, rely on the metrics of timeliness and efficiency to reach optimal profits. A way stock investors have continued to strive for the best of these two factors of the business is through the use of predictive machine learning systems to help aid in their decision making. However, among the many systems currently in use, it could be said that the myriad of data that they are based on may not be sufficient. In an effort to devise an ensemble learning predictive system that will utilize an array of big …

Educational Simulation Design To Transform Learning In Earth And Environmental Sciences, Michelle Zhu, Matthew Johnson, Aditya Dutta, Nicole Panorkou, Bharath Samanthula, Pankaj Lal, Weitian Wang

Department of Computer Science Faculty Scholarship and Creative Works

This Innovative Practice Full Paper presents several educational simulation designs to transform learning in the earth and environmental sciences. In recent years, K-12 education has seen a widespread pedagogical shift from traditional textbook-based learning to a student-centered interactive learning environment. Innovative teaching technologies including games and simulations are exploited to make learning fun and engaging for students at various grades. As technology continues to advances, stakeholders such as teachers, parents, and educational policymakers are motivated to know the most effective technology platform to engage students in active learning. In this paper, we discuss some useful simulation techniques from the perspectives …

Collections In Scala, Raffi Khatchadourian

Open Educational Resources

No abstract provided.

Privacy And The Digital Divide: Investigating Strategies For Digital Safety By People Of Color, Denavious Hoover

Theses and Dissertations

People of color are becoming increasingly concerned with digital privacy. They are concerned about the obfuscated data collection and sharing practices of major social media plat- forms and the strong entitlement of other users in the online space to their content. This study examines how people of color conceptualize and behave to produce safety in the online space, or, in other words, digital privacy. This study challenges notions that people are not purposeful about privacy in the online space and highlights the voices of people of color, whom are not of- ten included in theorizing or decision making about the …

Investigating Factors Predicting Effective Learning In A Cs Professional Development Program For K–12 Teachers, Patrick Morrow

School of Computing: Dissertations, Theses, and Student Research

The demand for K-12 Computer Science (CS) education is growing and there is not an adequate number of educators to match the demand. Comprehensive research was carried out to investigate and understand the influence of a summer two-week professional development (PD) program on teachers’ CS content and pedagogical knowledge, their confidence in such knowledge, their interest in and perceived value of CS, and the factors influencing such impacts. Two courses designed to train K-12 teachers to teach CS, focusing on both concepts and pedagogy skills were taught over two separate summers to two separate cohorts of teachers. Statistical and SWOT …

Exploring The Potential Of Sparse Coding For Machine Learning, Sheng Yang Lundquist

Dissertations and Theses

While deep learning has proven to be successful for various tasks in the field of computer vision, there are several limitations of deep-learning models when compared to human performance. Specifically, human vision is largely robust to noise and distortions, whereas deep learning performance tends to be brittle to modifications of test images, including being susceptible to adversarial examples. Additionally, deep-learning methods typically require very large collections of training examples for good performance on a task, whereas humans can learn to perform the same task with a much smaller number of training examples.

In this dissertation, I investigate whether the use …

Finite-Time State Estimation For An Inverted Pendulum Under Input-Multiplicative Uncertainty, Sergey V. Drakunov, William Mackunis, Anu Kossery Jayaprakash, Krishna Bhavithavya Kidambi, Mahmut Reyhanoglu

Publications

A sliding mode observer is presented, which is rigorously proven to achieve finite-time state estimation of a dual-parallel underactuated (i.e., single-input multi-output) cart inverted pendulum system in the presence of parametric uncertainty. A salient feature of the proposed sliding mode observer design is that a rigorous analysis is provided, which proves finite-time estimation of the complete system state in the presence of input-multiplicative parametric uncertainty. The performance of the proposed observer design is demonstrated through numerical case studies using both sliding mode control (SMC)- and linear quadratic regulator (LQR)-based closed-loop control systems. The main contribution presented here is the rigorous …

Espade: An Efficient And Semantically Secure Shortest Path Discovery For Outsourced Location-Based Services, Bharath K. Samanthula, Divyadharshini Karthikeyan, Boxiang Dong, K. Anitha Kumari

Department of Computer Science Faculty Scholarship and Creative Works

With the rapid growth of smart devices and technological advancements in tracking geospatial data, the demand for Location-Based Services (LBS) is facing a constant rise in several domains, including military, healthcare and transportation. It is a natural step to migrate LBS to a cloud environment to achieve on-demand scalability and increased resiliency. Nonetheless, outsourcing sensitive location data to a third-party cloud provider raises a host of privacy concerns as the data owners have reduced visibility and control over the outsourced data. In this paper, we consider outsourced LBS where users want to retrieve map directions without disclosing their location information. …

A Partition Based Feature Selection Approach For Mixed Data Clustering, Ashish Dutt

Student Works (2020-2029)

Presently, educational institutions compile and store huge volumes of data, such as student enrolment and attendance records, as well as their examination results. Mining such data yields stimulating information that serves its handlers well. Rapid growth in educational data points to the fact that distilling massive amounts of data requires a more sophisticated set of algorithms. This issue led to the emergence of the field of Educational Data Mining (EDM). Traditional data mining algorithms cannot be directly applied to educational problems, as they may have a specific objective and function. This implies that a pre-processing algorithm has to be enforced …

Enhancing A Cluster-Based Tdma Mac Protocol For Vehicle-To-Vehicle Communications, Abubakar Bello Tambawal

Student Works (2020-2029)

Vehicular Ad hoc Network technology (VANET) is one of the emerging and promising wireless technology, providing support for vehicles to communicate and share resources, (such as safety messages) through vehicle-to-vehicle (V2V) communications. Sequel to that the Time Division Multiple Access (TDMA) MAC protocol using a cluster-based topology has been proposed by the research community. Most of the existing research works focused on the cluster head (CH) election with very few addressing other critical issues, including cluster formation, efficient time slot allocation, and cluster maintenance. These challenges result in an unstable cluster, which could affect the timely delivery of safety applications. …

Adaptive Data Migration In Load-Imbalanced Hpc Applications, Parsa Amini

LSU Doctoral Dissertations

Distributed parallel applications need to maximize and maintain computer resource utilization and be portable across different machines. Balanced execution of some applications requires more effort than others because their data distribution changes over time. Data re-distribution at runtime requires elaborate schemes that are expensive and may benefit particular applications.

This dissertation discusses a solution for HPX applications to monitor application execution with APEX and use AGAS migration to adaptively redistribute data and load balance applications at runtime to improve application performance and scaling behavior. This dissertation provides evidence for the practicality of using the Active Global Address Space as is …

Fall 2020

In The Loop

Studio CDM Documents Remote Initiatives; "Tom of Your Life" Film Release; Animation Jam Goes Virtual; DePaul Experimental Film Showcase 2020; Trackmania Soundtrack; Alumni Games at Pixel Pop; Alumnus Commemorates St. Vincent de Paul; Cybersecurity Champion Alina Kuzmenkova; Walking the Walk: Youth programs at CDM express DePaul’s Vincentian values; Fair Treatment: Three initiatives address racial inequity in health care; They've Got You Covered: A School of Design instructor leads a cottage industry of makers protecting essential workers from the novel coronavirus; Meet Would-Be Hot Topic Influencer Vera Drew; Data Detectives: CDM helps Chicago track the racial proportions of its COVID-19 cases

Research On Optimal Configuration Design Method Of Stewart Platform, Xuwei Fan, Lili Yang, Cheng Yu, Xiaoning Zhou, Yexin Zhang

Journal of System Simulation

Abstract: Based on the structural singularity and configuration singularity of parallel mechanism, a safety mechanism design scheme of the Stewart platform is proposed to improve the workspace efficiency and engineering practicability. Taking the Stewart platform without any particularity as the research object, and considering the singular constraints of the structure, a dexterity index is proposed to achieve the optimization of the structural parameters. Analyzing and constructing the kinematics model of the Stewart platform, analyzing the singularities of the configuration bifurcations of 16 typical extreme poses, a secure workspace verification algorithm is proposed to make the whole workspace free of singularity. …

Research On Adaptive Routing Algorithm For Wireless Weak-Connection Network, Hua Xiang, Hongjuan Yao, Wang Hai, Wang Zhao, Jietao Zhang, Lili Shu

Journal of System Simulation

Abstract: The wireless weak-connected network has the characteristics of long delay, high dynamic topology, and unstable links. With the lack of continuity from the source end to the destination end of network connection, in order to solve the problem of communication difficulty, the intelligence and adaptability of Physarum polycephalum are introduced, and the adaptive wireless weak-connected network routing algorithm is proposed. A wireless weak-connected network model is build and the mathematical relationships of link capacity is deduced. The next-hop selection strategy and optimal routing strategy is designed to achieve the best-effort delivery of data in wireless weak-connected network environmrnt. Simulation …

Visual Feedback Fuzzy Control For A Robot Manipulator Based On Svr Learning, Xianxia Zhang, Jinqiang Zhang, Zhiyuan Li, Shiwei Ma, Banghua Yang

Journal of System Simulation

Abstract: A fuzzy controller based on SVR learning is proposed for uncalibrated robot visual servoing. In this paper, a fuzzy controller is used to directly construct the nonlinear mapping between image features and robot joint motion. The fuzzy basis function of the fuzzy controller is taken as the kernel function of an SVR and the equivalent relationship between the SVR and the fuzzy controller is established. The learned support vector from the SVR is used as the rule of the fuzzy controller. Since all rules are learned from the data, there is no need to manually design the rules. …

Gru-Based Car-Following Behavior Simulation Model, Fei Rong, Liu Fang, Xie Guo, Hei Xinhong, Shasha Li, Hu Bo

Journal of System Simulation

Abstract: The accuracy of acceleration prediction can be effectively improved by the driver's memory in car-following behavior. A new car-following model based on the General Motors (GM) and the gate control unit (GRU) is proposed. The car-following data between small vehicles with similar driving behavior are obtained by data preprocessing. The established model is calibrated by the car-following data, and the optimal parameters and structure of the model are determined. According to car-following characteristics, the effectiveness of model is verified by simulation. It is confirmed that the model has high robustness and improved simulation accuracy comparing with the traditional models.

Estimation Of Space-Time Of Urban Building Population Based On Mobile Phone Big Data, Hu Yang, Xiaoyong Zhang, Xiao Di

Journal of System Simulation

Abstract: With the acceleration of the urbanization process, mastering the population distribution on a fine scale is of great significance for urban disaster assessment, emergency response management and public resource allocation. The rapid development of the Internet and the popularity of smartphones have prompted mobile phones to become the sensors of human activity. A method of urban building population estimation based on mobile phone big data is proposed. The method analyzes the crowd activity law of buildings with different functions based on mobile phone positioning big data in typical areas, calculates the population capacity of different functional buildings, and …

Simulation Research On Optimization Of Agv Charging Strategy For Automated Terminal, Danlan Xie, Guo Di, Ji Yuan, Zhipeng Gao

Journal of System Simulation

Abstract: As the automated container terminal is the development trend of terminal, AGV (automated guided vehicle) becomes the most widely used horizontal transportation tool and it is important to make its reasonable charging strategy. Aiming at the shortcomings of the current AGV, a charging strategy of offline charging being primary and online charging being auxiliary is proposed. In order to solve the problem of location selection of online charging station, a quick method of selecting effective stations by using heat zone map is proposed. Through a large number of simulation experiments using this strategy, the fact that the number …