Computer Sciences Commons^™

Work-In-Progress: Iot Device Signature Validation, Jeffrey Hemmes

Regis University Faculty Publications

Device fingerprinting is an area of security that has received renewed attention in recent years, with a number of classification methods proposed that rely on characteristics unique to a particular vendor or device type. Current works are limited to determining device type for purposes of access control and MAC address spoof prevention. This work synthesizes multiple sources of information to verify device capabilities in a device profile, which can be used in a number of applications not limited to authentication and authorization. The approach proposed in this paper relies on existing protocols and methods proposed in the literature, using a …

On Finding Two Posets That Cover Given Linear Orders, Ivy Ordanel, Proceso L. Fernandez Jr, Henry Adorna

Department of Information Systems & Computer Science Faculty Publications

The Poset Cover Problem is an optimization problem where the goal is to determine a minimum set of posets that covers a given set of linear orders. This problem is relevant in the field of data mining, specifically in determining directed networks or models that explain the ordering of objects in a large sequential dataset. It is already known that the decision version of the problem is NP-Hard while its variation where the goal is to determine only a single poset that covers the input is in P. In this study, we investigate the variation, which we call the 2-Poset …

Ml4iot: A Framework To Orchestrate Machine Learning Workflows On Internet Of Things Data, Jose Miguel Alves, Leonardo Honorio, Miriam A M Capretz

Electrical and Computer Engineering Publications

Internet of Things (IoT) applications generate vast amounts of real-time data. Temporal analysis of these data series to discover behavioural patterns may lead to qualified knowledge affecting a broad range of industries. Hence, the use of machine learning (ML) algorithms over IoT data has the potential to improve safety, economy, and performance in critical processes. However, creating ML workflows at scale is a challenging task that depends upon both production and specialized skills. Such tasks require investigation, understanding, selection, and implementation of specific ML workflows, which often lead to bottlenecks, production issues, and code management complexity and even then may …

Automated Morgan Keenan Classification Of Observed Stellar Spectra Collected By The Sloan Digital Sky Survey Using A Single Classifier, Michael J. Brice, Răzvan Andonie

All Faculty Scholarship for the College of the Sciences

The classification of stellar spectra is a fundamental task in stellar astrophysics. Stellar spectra from the Sloan Digital Sky Survey are applied to standard classification methods, k-nearest neighbors and random forest, to automatically classify the spectra. Stellar spectra are high dimensional data and the dimensionality is reduced using astronomical knowledge because classifiers work in low dimensional space. These methods are utilized to classify the stellar spectra into a complete Morgan Keenan classification (spectral and luminosity) using a single classifier. The motion of stars (radial velocity) causes machine-learning complications through the feature matrix when classifying stellar spectra. Due to the nature …

Vrsensory: Designing Inclusive Virtual Games With Neurodiverse Children, Ben Wasserman, Derek Prate, Bryce Purnell, Alex Muse, Kaitlyn Abdo, Kendra Day, Louanne Boyd

Engineering Faculty Articles and Research

We explore virtual environments and accompanying interaction styles to enable inclusive play. In designing games for three neurodiverse children, we explore how designing for sensory diversity can be understood through a formal game design framework. Our process reveals that by using sensory processing needs as requirements we can make sensory and social accessible play spaces. We contribute empirical findings for accommodating sensory differences for neurodiverse children in a way that supports inclusive play. Specifically, we detail the sensory driven design choices that not only support the enjoyability of the leisure activities, but that also support the social inclusion of sensory-diverse …

Protein Inter-Residue Distance Prediction Using Residual And Capsule Networks, Andrew Dillon

Theses

The protein folding problem, also known as protein structure prediction, is the task of building three-dimensional protein models given their one-dimensional amino acid sequence. New methods that have been successfully used in the most recent CASP challenge have demonstrated that predicting a protein's inter-residue distances is key to solving this problem. Various deep learning algorithms including fully convolutional neural networks and residual networks have been developed to solve the distance prediction problem. In this work, we develop a hybrid method based on residual networks and capsule networks. We demonstrate that our method can predict distances more accurately than the algorithms …

Enhancing Timeliness Of Drug Overdose Mortality Surveillance: A Machine Learning Approach, Patrick J. Ward, Peter J. Rock, Svetla Slavova, April M. Young, Terry L. Bunn, Ramakanth Kavuluru

Kentucky Injury Prevention and Research Center Faculty Publications

BACKGROUND: Timely data is key to effective public health responses to epidemics. Drug overdose deaths are identified in surveillance systems through ICD-10 codes present on death certificates. ICD-10 coding takes time, but free-text information is available on death certificates prior to ICD-10 coding. The objective of this study was to develop a machine learning method to classify free-text death certificates as drug overdoses to provide faster drug overdose mortality surveillance.

METHODS: Using 2017–2018 Kentucky death certificate data, free-text fields were tokenized and features were created from these tokens using natural language processing (NLP). Word, bigram, and trigram features were created …

Wearable Augmented Reality Application For Shoulder Rehabilitation, Giuseppe Turini, Sara Condino, Rosanna Viglialoro, Marco Gesi, Vincenzo Ferrari

Computer Science Publications

Augmented reality (AR) technology is gaining popularity and scholarly interest in the rehabilitation sector because of the possibility to generate controlled, user-specific environmental and perceptual stimuli which motivate the patient, while still preserving the possibility to interact with the real environment and other subjects, including the rehabilitation specialist. The paper presents the first wearable AR application for shoulder rehabilitation, based on Microsoft HoloLens, with real-time markerless tracking of the user’s hand. Potentialities and current limits of commercial head-mounted displays (HMDs) are described for the target medical field, and details of the proposed application are reported. A serious game was designed …

Generation Of Crowd Arrival And Destination Locations/Times In Complex Transit Facilities, Brian Ricks, Andrew Dobson, Athanasios Krontiris, Kostas Bekris, Mubbasir Kapadia, Fred Roberts

Computer Science Faculty Publications

In order to simulate virtual agents in the replica of a real facility across a long time span, a crowd simulation engine needs a list of agent arrival and destination locations and times that reflect those seen in the actual facility. Working together with a major metropolitan transportation authority, we propose a specification that can be used to procedurally generate this information. This specification is both uniquely compact and expressive—compact enough to mirror the mental model of building managers and expressive enough to handle the wide variety of crowds seen in real urban environments. We also propose a procedural algorithm …

Effectiveness Of Tools In Identifying Rogue Access Points On A Wireless Network, Ryan Vansickle, Tamirat Abegaz, Bryson Payne

KSU Proceedings on Cybersecurity Education, Research and Practice

Wireless access points have greatly improved users' ability to connect to the Internet. However, they often lack the security mechanisms needed to protect users. Malicious actors could create a rogue access point (RAP), using a device such as the WiFi Pineapple Nano, that could trick users into connecting to an illegitimate access point (AP). To make them look legitimate, adversaries tend to setup RAPs to include a captive portal. This is very effective, since most public networks use captive portals as a means to provide genuine access. The objective of this study is to examine the effectiveness of RAP identification …

Automatic Security Bug Detection With Findsecuritybugs Plugin, Hossain Shahriar, Kmarul Riad, Arabin Talukder, Hao Zhang, Zhuolin Li

KSU Proceedings on Cybersecurity Education, Research and Practice

The security threats to mobile application are growing explosively. Mobile app flaws and security defects could open doors for hackers to easily attack mobile apps. Secure software development must be addressed earlier in the development lifecycle rather than fixing the security holes after attacking. Early eliminating against possible security vulnerability will help us increase the security of software and mitigate the consequence of damages of data loss caused by potential malicious attacking. In this paper, we present a static security analysis approach with open source FindSecurityBugs plugin for Android StThe security threats to mobile application are growing explosively. Mobile app …

Automated Reverse Engineering Of Automotive Can Bus Controls, Charles Barron Kirby, Bryson Payne

KSU Proceedings on Cybersecurity Education, Research and Practice

This research provides a means of automating the process to reverse engineer an automobile’s CAN Bus to quickly recover CAN IDs and message values to control the various systems in a modern automobile. This approach involved the development of a Python script that uses several open-source tools to interact with the CAN Bus, and it takes advantage of several vulnerabilities associated with the CAN protocol. These vulnerabilities allow the script to conduct replay attacks against the CAN Bus and affect various systems in an automobile without the operator’s knowledge or interaction.

These replay attacks can be accomplished by capturing recorded …

A World Of Cyber Attacks (A Survey), Mubarak Banisakher, Marwan Omar

KSU Proceedings on Cybersecurity Education, Research and Practice

The massive global network that connects billions of humans and millions of devices and allow them to communicate with each other is known as the internet. Over the last couple of decades, the internet has grown expeditiously and became easier to use and became a great educational tool. Now it can used as a weapon that can steal someone’s identity, expose someone’s financial information, or can destroy your networking devices. Even in the last decade, there have been more cyber attacks and threats destroying major companies by breaching the databases that have millions of personal information that can be sold …

An Exploratory Analysis Of Mobile Security Tools, Hossain Shahriar, Md Arabin Talukder, Md Saiful Islam

KSU Proceedings on Cybersecurity Education, Research and Practice

The growing market of the mobile application is overtaking the web application. Mobile application development environment is open source, which attracts new inexperienced developers to gain hands on experience with applicationn development. However, the security of data and vulnerable coding practice is an issue. Among all mobile Operating systems such as, iOS (by Apple), Android (by Google) and Blackberry (RIM), Android dominates the market. The majority of malicious mobile attacks take advantage of vulnerabilities in mobile applications, such as sensitive data leakage via the inadvertent or side channel, unsecured sensitive data storage, data transition and many others. Most of these …

Iot: Challenges In Information Security Training, Lech J. Janczewski, Gerard Ward

KSU Proceedings on Cybersecurity Education, Research and Practice

Both consumers and businesses are rapidly adopting IoT premised on convenience and control. Industry and academic literature talk about billions of embedded IoT devices being implemented with use-cases ranging from smart speakers in the home, to autonomous trucks, and trains operating in remote industrial sites. Historically information systems supporting these disparate use-cases have been categorised as Information Technology (IT) or Operational Technology (OT), but IoT represents a fusion between these traditionally distinct information security models.

This paper presents a review of IEEE and Elsevier peer reviewed papers that identifies the direction in IoT education and training around information security. It …

Proposal For A Joint Cybersecurity And Information Technology Management Program, Christopher Simpson, Debra Bowen, William Reid, James Juarez

KSU Proceedings on Cybersecurity Education, Research and Practice

Cybersecurity and Information Technology Management programs have many similarities and many similar knowledge, skills, and abilities are taught across both programs. The skill mappings for the NICE Framework and the knowledge units required to become a National Security Agency and Department of Homeland Security Center of Academic Excellence in Cyber Defense Education contain many information technology management functions. This paper explores one university’s perception on how a joint Cybersecurity and Information Technology Management program could be developed to upskill students to be work force ready.

Adversarial Thinking: Teaching Students To Think Like A Hacker, Frank Katz

KSU Proceedings on Cybersecurity Education, Research and Practice

Today’s college and university cybersecurity programs often contain multiple laboratory activities on various different hardware and software-based cybersecurity tools. These include preventive tools such as firewalls, virtual private networks, and intrusion detection systems. Some of these are tools used in attacking a network, such as packet sniffers and learning how to craft cross-site scripting attacks or man-in-the-middle attacks. All of these are important in learning cybersecurity. However, there is another important component of cybersecurity education – teaching students how to protect a system or network from attackers by learning their motivations, and how they think, developing the students’ “abilities to …

Collaborating On Machine Reading: Training Algorithms To Read Complex Collections, Carrie M. Pirmann, Brian R. King, Bhagawat Acharya, Katherine M. Faull

Bucknell University Digital Scholarship Conference

Interdisciplinary collaboration between two faculty members in the humanities and computer science, a research librarian, and an undergraduate student has led to remarkable results in an ongoing international DH research project that has at its core 18th century manuscripts. The corpus stems from a vast collection of archival materials held by the Moravian Church in the UK, Germany, and the US. The number of pages to be transcribed, differences in handwriting styles, paper quality, and original language pose enormous problems for the feasibility of human transcription. This presentation will review the hypothesis, process, and findings of a summer research project …

Internet Core Functions: Security Today And Future State, Jeffrey Jones

KSU Proceedings on Cybersecurity Education, Research and Practice

Never in the history of the world has so much trust been given to something that so few understand. Jeff reviews three core functions of the Internet along with recent and upcoming changes that will impact security and the world.

Preparing For Tomorrow By Looking At Yesterday, Peter Dooley

KSU Proceedings on Cybersecurity Education, Research and Practice

Why do we learn? Why do we study history? Why do we research the work of others? The answer is that there is value today in what was already learned and experienced, successes and failures. Mr. Dooley, a 25-year security professional and 20-year hospitality executive, will share his experiences and how our history in security will help us in thinking about our future.

A Computational Model For Recovery From Brain Injury, Wayne Wakeland

Systems Science Friday Noon Seminar Series

A computational simulation model calculates recovery trajectories following traumatic brain injury (TBI). Prior publications include a multi-scale framework for studying concussion and a systems-level causal loop diagram (CLD) and discussion of feedback processes. The scope of the computational model goes beyond concussion to include all severities of TBI. A set of first order ordinary differential equations and their associated parameters determines recovery trajectories. While highly speculative, the model serves to demonstrate the potential utility of computational models in this context. Much more research will be needed to create a properly supported research model that could be used for clinical trial …

Update Frequency And Background Corpus Selection In Dynamic Tf-Idf Models For First Story Detection, Fei Wang, Robert J. Ross, John D. Kelleher

Conference papers

First Story Detection (FSD) requires a system to detect the very first story that mentions an event from a stream of stories. Nearest neighbour-based models, using the traditional term vector document representations like TF-IDF, currently achieve the state of the art in FSD. Because of its online nature, a dynamic term vector model that is incrementally updated during the detection process is usually adopted for FSD instead of a static model. However, very little research has investigated the selection of hyper-parameters and the background corpora for a dynamic model. In this paper, we analyse how a dynamic term vector model …

Concolic Testing Heap-Manipulating Programs, Long H. Pham, Quang Loc Le, Quoc-Sang Phan, Jun Sun

Research Collection School Of Computing and Information Systems

Concolic testing is a test generation technique which works effectively by integrating random testing generation and symbolic execution. Existing concolic testing engines focus on numeric programs. Heap-manipulating programs make extensive use of complex heap objects like trees and lists. Testing such programs is challenging due to multiple reasons. Firstly, test inputs for such program are required to satisfy non-trivial constraints which must be specified precisely. Secondly, precisely encoding and solving path conditions in such programs are challenging and often expensive. In this work, we propose the first concolic testing engine called CSF for heap-manipulating programs based on separation logic. CSF …

Development Of Spatiotemporal Congestion Pattern Observation Model Using Historical And Near Real Time Data, Betty Kretlow

Master of Science in Computer Science Theses

Traffic congestion is not foreign to major metropolitan areas. Congestion in large cities often is associated with dense land developments and continued economic growth. In general, congestion can be classified into two categories: recurring and nonrecurring. Recurring congestion often occurs at certain parts of highway networks, referred to as bottleneck locations. Nonrecurring congestion, on the other hand, can be caused by different reasons, including work zones, special events, accidents, inclement weather, poor signal timing, etc. The work presented here demonstrates an approach to effectively identifying spatiotemporal patterns of traffic congestion at a network level. The Metro Atlanta highway network was …

Integrating Mathematics And Biology In The Classroom: A Compendium Of Case Studies And Labs, Becky Sanft, Anne Walter

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

Adaptive Randomized Rounding In The Big Parsimony Problem, Sangho Shim, Sunil Chopra, Eunseok Kim

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

Quantifying Pollen Traits To Build A Mathematical Model Of Pollen Competition - A Mathematician's Perspective, Montana Ferita, Julie Fucarino, Alex Capaldi, Charlotte Beckford

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

Period Drift In A Neutrally Stable Stochastic Oscillator, Kevin Sanft

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

An Agent-Based Model Of An Endangered Florida Tillansia Utriculata Population, Erin N. Bodine, Alexandra Campbell, Anna C. Kula

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

Automatic Inference Of Causal Reasoning Chains From Student Essays, Simon Mark Hughes

College of Computing and Digital Media Dissertations

While there has been an increasing focus on higher-level thinking skills arising from the Common Core Standards, many high-school and middle-school students struggle to combine and integrate information from multiple sources when writing essays. Writing is an important learning skill, and there is increasing evidence that writing about a topic develops a deeper understanding in the student. However, grading essays is time consuming for teachers, resulting in an increasing focus on shallower forms of assessment that are easier to automate, such as multiple-choice tests. Existing essay grading software has attempted to ease this burden but relies on shallow lexico-syntactic features …