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  • Designing an Instructional Informative Website for Pet Owners

    Jofre, Ana; Ortiz, Sherman; Jofre, Ana; First Reader; Lizardi, Ryan; Second Reader (SUNY Polytechnic Institute, 2020-12)
    Searching for pet owners' information on the world wide web can be difficult and confusing due to the many different websites on must scroll through depending on your search results. With the rise of social media, forums, online pet stores, and blogs there are more websites to look into. Although these websites have articles on pet information, they are often opinionated, lacking in follow up information or simply unfinished. This paper looks into the development of a site with the end users' navigation in mind for all their pets primary concerns. This website is made for user's accessing information about their pets. Using a classic template website builder, the site is able to be updated in real time with ease as more information becomes available. The website's user experience is evaluated by analyzing survey information. The end user experience, the most important aspect of any website, comprises navigation, clarity, and simplicity. Prototype website link: https://soaj926.wixsite.com/mysite-2
  • Style Guide Development For An Internal Reference Library

    Lizardi, Ryan; Bush, Sarah C.; Lizardi, Ryan; First Reader; Jofre, Ana; Second Reader (SUNY Polytechnic Institute, 2020-12)
    This work strives to present information design and user experience (UX) techniques to improve continuity of design and usability, before migrating a cross-functional internal reference library to a new content management system (CMS). Expert-curated content accessed through LinkedIn Learning, combined with scholarly research, result in a complimentary style guide for use in the workplace.
  • Game Streaming in the Wake of a Pandemic: Topic: Live Streaming and Branding

    Jofre, Ana; Martucci, Nicholas; Jofre, Ana; First Reader; Lizardi, Ryan; Second Reader (SUNY Polytechnic Institute, 2020-12)
    The purpose of this study is to determine what the key motivational factors for creating a live stream gaming channel in the wake of a global pandemic are. This is executed by generating a series of podcast interviews from live streamers, generating branding for the launch of a live stream gaming channel and launching the channel. With the world in a current state of emergency, the live streaming industry under Twitch.tv has boomed, giving streamers and viewers alike an opportunity to interact, communicate, and form communities like never before in the shadow of these pressing times. In light of this, we seek an escape from the harsh reality that is quarantine and find comfort in engaging with others all the while having fun indoors.
  • Critical Document Design: A Survey of Considerations for the Next Generation of Procedures Used in High-Risk Organizations

    Jofre, Ana; McLaren, Elizabeth A.; Jofre, Ana; First Reader; Lizardi, Ryan; Second Reader (SUNY Polytechnic Institute, 2020-12)
    Since the innovation of the pre-flight cockpit checklist first made the Boeing B-17 safe to fly, several high-risk industries have adopted the approach, to varying degrees. This paper reviews regulatory findings and recommendations from formal investigations following incidents across multiple high-risk industries to identify areas how checklists may be misused, misunderstood, or where they could have been of value to operators who didn't use them. The recommendations are then compared against current findings in information design and usability, and conclude with recommendations for how the future of electronic documentation may further improve the usability experience of operators and their teams, and ensure the safety of the public around them.
  • Bridges: An All-in-One Resource App to Help Narrow the Digital Divide

    Jofre, Ana; Torres, Abigail; Jofre, Ana; First Reader; Lizardi, Ryan; Second Reader (SUNY Polytechnic Institute, 2020-12)
    goal of this project is to design a user-friendly mobile application prototype for people who are disadvantaged by the digital divide. This includes people who are of lower income and those who live in areas where internet isn't accessible or expensive. This application can be accessed through IOS devices as well as a text messaging service system for those without said device. It is an application that will be extremely easy to use and manage, and where users will be able to access multiple resources to gain not only digital literacy but find places that offer computer labs and internet for free, in case neither is available at home. The mobile application's goal is to create resource where the user has everything, they need in a way that is easily accessible; a product that is not currently on the market. In this paper, I will not only explore research on what the digital divide is, but also how it affects those who are disadvantaged by it and ways we can start to help narrow the gap. I will also explore the advantages of mobile applications and how it can be used to narrow the digital divide.
  • THE POWER OF BRAND DEVELOPMENT: Leveraging Branding to Effectively Engage Customers

    Jofre, Ana; Pondolfino, Amy; Jofre, Ana; First Reader; Lizardi, Ryan; Second Reader (SUNY Polytechnic Institute, 2020-12)
    This paper explores the relationship between brand development and customer engagement. Customer brand engagement and customer engagement theories and models are examined through existing literature. Concrete steps to advance customer engagement are identified. Artifacts are created as part of a comprehensive branding package to guide a children's museum, Oneonta World of Learning (OWL), through their branding process.
  • Developing Helpdesk Mobile App to Support Classroom Technology During COVID-19 Pandemic

    Jofre, Ana; Reed, Allen; Jofre, Ana; First Reader; Yucel, Ibrahim; Second Reader (SUNY Polytechnic Institute, 2020-12)
    This research project is a prototype of a mobile application that would be dedicated to the Syracuse University faculty members and students and serve as service mobile app providing helpdesk support for classroom technology issues. The development of this mobile application uses Universal Design Principles focuses on Human-Centered Design theory, and the app build upon a user’s needs. The prototype has been created using Adobe XD, the prototypes are made to be high fidelity and fully interactive so it can be used for usability test. This research project paper intends to determine the benefit of using mobile app for helpdesk support during the COVID-19 pandemic, and to find out if using helpdesk mobile app could enhance IT services and the time needed to response to classroom technology issue. The fully interactive prototype can be viewed through the link below: https://xd.adobe.com/view/138af8a5-29bd-4571-ac9c-1fc76adff40a-46d9/?fullscreen&hints=off
  • Detection of Brain Tumor in Magnetic Resonance Imaging (MRI) Images using Fuzzy C-Means and Thresholding

    Andriamanalimanana, Bruno; Kalakuntla, Shashank; Andriamanalimanana, Bruno R.; First Reader; Novillo, Jorge E.; Second Reader; Spetka, Scott; Third Reader (SUNY Polytechnic Institute, 2020-08)
    Although many clinical experts or radiologists are well trained to identify tumors and other abnormalities in the brain, the identification, detection and segmentation of the affected area in the brain is observed to be a tedious and time consuming task. MRI has been a conventional and resultant image processing technique to visualize structures of the human body. It is very difficult to visualize abnormal structures of the brain using simple imaging techniques. MRI technique uses many imaging modalities that scan and capture the internal structure of the human brain. Even with the use of these techniques, it is a difficult and tedious task for a human eye to be always sophisticated in detecting brain tumors from these images. With emerging technology, we can provide a way to ease the process of detection. This project focuses on identification of brain tumor in MR images, it involves in removing noise using noise removal technique AMF followed by enhancing the images using Balance Enhancement Contrast technique (BCET).Further, image segmentation is performed using fuzzy c-means and finally the segmented images are produced as an input to a canny edge detection resulting with the tumor image. This report entices the approach, design, and implementation of the application and finally the results. I have tried implementing/developing this application in Python. The Jupyter notebook provides a block simulation for the entire flow of the project.
  • Non-Convex Optimization: RMSProp Based Optimization for Long Short-Term Memory Network

    Andriamanalimanana, Bruno; Yan, Jianzhi; Andriamanalimanana, Bruno; First Reader; Chiang, Chen-Fu; Second Reader; Novillo, Jorge; Third Reader (SUNY Polytechnic Institute, 2020-05-09)
    This project would give a comprehensive picture of non-convex optimization for deep learning, explain in details about Long Short-Term Memory (LSTM) and RMSProp. We start by illustrating the internal mechanisms of LSTM, like the network structure and backpropagation through time (BPTT). Then introducing RMSProp optimization, some relevant mathematical theorems and proofs in those sections, which give a clear picture of how RMSProp algorithm is helpful to escape the saddle point. After all the above, we apply it with LSTM with RMSProp for the experiment; the result would present the efficiency and accuracy, especially how our method beat traditional strategy in non-convex optimization.
  • Cyber Security Advantages of Optical Communications in SATCOM Networks

    Kholidy, Hisham A.; Baker, Cameron; Kholidy, Hisham A.; Advisor (SUNY Polytechnic Institute, 2020-12)
    Space-based communications, whether it is ground-to-space or inter-satellite communications, have so far been primarily within the RF spectrum. With the increase in space missions and the need for larger amounts of data being sent to and from satellites, the near infrared or optical spectrum has started to become more widely used instead of RF. Higher bandwidth is not the only advantage of using optics for communications over RF, there is also an inherent security advantage as well. Currently, there is far too little enforcement of security standards for space communications networks, and the use of RF only worsens the problem due to its very large beam spread when compared to optics. This paper will seek to prove that optics is a far more superior technology to be used for space communications networks from a security standpoint as well as providing an increase in available bandwidth. These points will be proven by first introducing the technology by examining current Free Space Optics (FSO) systems and space optics systems being provided by manufacturers. Secondly, this paper will discuss the current state of space communications security, and issues space communications networks are facing using RF with the recent advancement into low-cost SmallSat operations that threaten existing space vehicles, and the lack of standard security practices within these networks. Lastly, this paper will provide evidence into why optics communications can improve the security of spaced based communications due to its lower beam spread and the ability to incorporate quantum key distribution into the communications channel.
  • A Wireless Intrusion Detection for the Next Generation (5G) Networks

    Kholidy, Hisham A.; Ferrucci, Richard; Kholidy, Hisham A.; Advisor (SUNY Polytechnic Institute, 2020-05)
    5G data systems are closed to delivery to the public. The question remains how security will impact the release of this cutting edge architecture. 5G data systems will be sending massive amounts of personal data due to the fact that everybody in the world is using mobile phones these days. With everyone using a 5G device, this architecture will have a huge surface area for attackers to compromise. Using machine learning techniques previously applied to 802.11 networks. We will show that improving upon these previous works, we can have a better handle on security when it comes to 5G architecture security. We find that using a machine learning classifier known as LogIT boost, combined with a selected combination of feature selection, we can provide optimal results in identifying three different classes of traffic referred to as normal, flooding, and injection traffic. We drastically decrease the time taken to perform this classification while improving the results. We simulate the Device2Device (D2D) connections involved in the 5G systems using the AWID dataset. The evaluation and validation of the classification approach are discussed in details in this thesis.
  • ?Generic Datasets, Beamforming Vectors Prediction of 5G Celleular Networks

    Kholidy, Hisham A.; Singh, Manjit; Kholidy, Hisham A.; Advisor (SUNY Polytechnic Institute, 2020)
    The early stages of 5G evolution revolves around delivering higher data speeds, latency improvements and the functional redesign of mobile networks to enable greater agility, efficiency and openness. The millimeter-wave (mmWave) massive multiple-input-multiple-output (massive MIMO) system is one of the dominant technology that consistently features in the list of the 5G enablers and opens up new frontiers of services and applications for next-generation 5G cellular networks. The mmWave massive MIMO technology shows potentials to significantly raise user throughput, enhances spectral and energy efficiencies and increases the capacity of mobile networks using the joint capabilities of the huge available bandwidth in the mmWave frequency bands and high multiplexing gains achievable with massive antenna arrays. In this report, we present the preliminary outcomes of research on mmWave massive MIMO (as research on this subject is still in the exploratory phase) and study two papers related to the Millimeter Wave (mmwave) and massive MIMO for next-gen 5G wireless systems. We focus on how a generic dataset uses accurate real-world measurements using ray tracing data and how machine learning/Deep learning can find correlations for better beam prediction vectors through this ray tracing data. We also study a generated deep learning model to be trained using TensorFlow and Google Collaboratory.
  • Cloud-SCADA Penetrate: Practical Implementation for Hacking Cloud Computing and Critical SCADA Systems

    Kholidy, Hisham A. (SUNY Polytechnic Institute, 2020)
    In this report, we discuss some of our hacking and security solutions that we developed at our Advanced Cybersecurity Research Lab (ACRL). This report consists of the following five main experimental packages: 1) Exploiting the cloud computing system using a DDoS attack and developing a distributed deployment of a cloud based Intrusion Detection System (IDS) solution. 2) Hacking SCADA systems components. 3) Hacking Metasploitable machines. 4) Hacking Windows 7 system. 5) Windows Post Exploitation.
  • 5G Networks Security: Attack Detection Using the J48 and the Random Forest Tree Classifiers

    Kholidy, Hisham A.; Steele II, Bruce; Kholidy, Hisham A.; Advisor (SUNY Polytechnic Institute, 2020)
    5G is the next generation of cellular networks succeeding and improving upon the last generation of 4G Long Term Evolution (LTE) networks. With the introduction of 5G comes significant improvements over the previous generation with the ability to support new and emerging technologies in addition to the growth in the number of devices. The purpose of this report is to give a broad overview of what 5G encompasses including the architecture, underlying technology, advanced features, use cases/applications, and security, and to evaluate the security of this new networks using existing machine learning classification techniques such as The J48 Tree Classifier and the Random Forest tree classifier. The evaluation is based on the UNSW-NB15 dataset that was created at the Cyber Range Lab of the Australian Centre for Cyber Security (ACCS) at the University of New South Wales. Since 5G datasets have yet to have been created, there is no publicly available dataset for the 5G systems. However, While the UNSW-NB15 dataset is built using a standard wireless computer network, we will use it to simulate the device-to-device (D2D) connections that 5G will support. In the case with the UNSW dataset, the J48 tree classifier fits more accurately than the Random Forest classifier. The J48 tree classifier achieved an 86.422% of correctly classified instances. On the other hand, the Random Forest tree classifier achieved 85.8451% of correctly classified instances.
  • An Empirical Wi-Fi Intrusion Detection System

    Kholidy, Hisham A.; Basnet, Diwash Bikram; Kholidy, Hisham A.; Advisor (SUNY Polytechnic Institute, 2020-05)
    Today, the wireless network devices are growing rapidly, and it is of utmost importance for securing those devices. Attackers or hackers use new methods and techniques to trick the system and steal the most important data. Intrusion Detection Systems detect the attacks by inspecting the network traffics or logs. The work demonstrated the effectiveness of detecting the attacks using machine learning techniques on the AWID dataset, which is produced from real wireless network logging. The author of the AWID dataset may have used several supervised learning models to successfully detect the intrusions. In this paper, we propose a newer approach for intrusion detection model based on dense neural networks, and long short-term memory networks (LSTM) and evaluate the model against the AWID-CLS-R subset. To get the best results from the model, we applied feature selection by replacing the unknown data with the value of “none”, getting rid of all repeated values, and kept only the important features. We did preprocess and feature scaling of both training and testing dataset, additional we also change the 2-dimensional to the 3- dimensional array because LSTM takes an input of 3-dimensional array, and later we used flatten layers to change into a 2-dimensional array for output. A comprehensive evaluation of DNN and LSTM networks are used to classify and predict the attacks and compute the precision, recall, and F1 score. We perform binary classification and multiclass classification on the dataset using neural networks and achieve accuracy ranging from 86.70 % to 96.01%.
  • An Analysis of a Signature-based Approach for an Intrusion Detection System in a Wireless Body Area Network (WBAN) using Data Mining Techniques

    Kholidy, Hisham A.; Medina, Serene Elisabeth; Kholidy, Hisham A.; Advisor (SUNY Polytechnic Institute, 2020)
    Wireless Body Area Networks (WBANs) use biosensors worn on, or in the human body, which collect and monitor a patient’s medical condition. WBANs have become increasingly more beneficial in the medical field by lowering healthcare cost and providing more useful information that medical professionals can use for a more accurate, and faster diagnosis. Due to the fact that the data collected from a WBAN is transmitted over a wireless network, there are several security concerns involved. This research looks at the various attacks, and concerns involved with WBANs. A real physiological dataset, consisting of ECG signals obtained from a 25-year-old male, was used in this research to test accuracy of various decision tree classifiers. The Weka software was used to analysis the accuracy and detection rate results of this dataset in its original form, versus a reduced dataset consisting of less, more important attributes. The results concluded that the use of decision tree classifiers using data mining, is an efficient way to test the increased accuracy on a real dataset obtained from a WBAN once it has been altered. The original dataset produced results where the ROC curve ranged from 0.313 (31%) to 0.68 (68%), meaning their accuracy is not very high and the detection rate is low. Once an attribute selection feature was used on the dataset, the newly reduced set showed ROC curves ranging from 0.68 (68%) to 0.969 (97%) amongst the three classes. As a result, decision tree models were much more accurate with a higher detection rate when used on a real dataset that was reduced to function better as a detector for a WBAN.
  • Evaluating Variant Deep Learning and Machine Learning Approaches for the Detection of Cyberattacks on the Next Generation 5G Systems

    Kholidy, Hisham A.; Borgesen, Michael E.; Kholidy, Hisham A.; Advisor (SUNY Polytechnic Institute, 2020)
    5G technology promises to completely transform telecommunication networks, introducing a wealth of benefits such as faster download speeds, lower download times, low latency, high network capacity. These benefits will pave the way for additional new capabilities and support connectivity for applications like smart homes and cities, industrial automation, autonomous vehicles, telemedicine, and virtual/augmented reality. However, attackers use these resources in their advantages to speed up the attacking process. This report evaluates four different machine learning and deep learning approaches namely the Naïve Bayes model, the logistic regression model, the decision tree model, and the random forest model. The performance evaluation and the validation of these approaches are discussed in details in this report.
  • Building an Educational Website Dedicated to the Study of Violent Crime Perpetuated Through Social Media

    Maloney, Kristen; Lizardi, Ryan; First Reader; Jofre, Ana; Second Reader (2019-04)
    Computing technology has taken over every aspect of life, from business to socializing, the world is entirely dependent on the Internet. Social engineering, hacking, and phishing attempts have made protecting private information and finances more complex than ever. As new techniques and equipment are created by the day, law enforcement struggles to keep pace. With the rise of social media, online gaming, and crowdfunding, there are more outlets than ever for criminals to attempt to defraud unsuspecting victims. This study serves to examine what makes cybercrime so attractive, the types of attacks and targets, and the role of law enforcement in investigating crimes; with on how social media networks like Facebook or Twitter have allowed crime to cross into real life. Utilizing this information, I have created an educational website for use in public or academic spaces to make cybersecurity information accessible. This flexible platform can be updated in real time as more information becomes available – allowing for new risk and solutions to be added.
  • Using Video Tutorials to Aid Coherence of Failed or Unchangeable Designs

    Griggs, Danielle; Stam, Kathryn; Thesis Advisor; Lizardi, Ryan; Second Reader (2019-05)
    Information design exists to convey information to users. When users have trouble understanding or using the information, the design has failed its primary purpose (Katz 17). When a design has failed and cannot be adjusted, the logical next step is to create another design to assist in using the first design. Tutorials are the perfect opportunity to help fill the void in a failed design. With the assistance of video and/or screen sharing technology, designers can create guided step-by-step instruction to assist users in navigating a process. This paper will examine how video tutorials can fill the void in coherence and transparency left by a failed design, including methods for creating successful video tutorials and an examination of equipment necessary for recording.
  • Validating Network Security with Predictive Analytics: A Design Guide to Bridge Stochastic Modeling into a Computationally Intelligent Dashboard

    Galavotti, Christopher R.; Kahn, Russell; Thesis Advisor; Stam, Kathryn; Second Reader (2019-05)
    Network posture has historically relied on traditional and reactionary methods for protection. These methods most commonly consist of network segmentation, intrusion detection systems, intrusion prevention systems, and signature-based detections. However, these traditional security platforms have proven to be an inadequate deterrent to the complex threat matrix that we currently find ourselves in. It is only through computational intelligence that we can truly identify potential intrusion areas and network abnormalities. This study presents a path forward for industry professionals on how to implement this computational approach into their network security platforms, particularly through stochastic modeling and simulation. Acknowledging the complex nature of this approach, a human-centered design methodology is also outlined on how to integrate this science into the enterprise via a predictive analytical dashboard.

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