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Articles 361 - 390 of 1161
Full-Text Articles in Computer Sciences
Dynamic Knowledge Elicitation: Leveraging Student Feedback For Improved Language Model Distillation, Reuven Muller
Dynamic Knowledge Elicitation: Leveraging Student Feedback For Improved Language Model Distillation, Reuven Muller
Master's Theses
Large Language Models (LLMs) have significantly advanced the field of natural language processing but remain resource-intensive and impractical for many organizations. Specialist models offer a viable alternative, often developed through Knowledge Distillation (KD) techniques. However, traditional KD methods rely on predefined static datasets to elicit knowledge from the teacher model, failing to dynamically address the weaknesses of the student model during training. This research introduces two novel methods for adaptive knowledge elicitation: Feedback-Driven Question Generation and Agent-Based Targeted Question Generation. These methods iteratively expand the training dataset based on the student model’s performance, leveraging a teacher model to generate targeted …
Gmc-137 Iot Security Vulnerabilities And How To Improve Them, Austin D Klein, Ryeon N Naderi, Tyler J Hood, Keren Bassourou
Gmc-137 Iot Security Vulnerabilities And How To Improve Them, Austin D Klein, Ryeon N Naderi, Tyler J Hood, Keren Bassourou
C-Day Computing Showcase
With the increased usage of IoT devices in homes as well as different industries, vulnerabilities have also increased significantly. The IoT devices are small in size, and it is hard to incorporate security in the software because security has high demand for computation. We have been conducting this research in order to find more suitable security methods that are lightweight as well as efficient. We have decided to move away from key hiding algorithms, which have increased time and space consumption, in favor of smaller and quicker block cipher algorithms.
Gmc-157 Text-To-Digital Person Video Generator: Digitalavatargen, Akansha Kesharwani, Nisha Bagdwal, Md E Hossain, Drashti Patel, Nikhil Adigoppula
Gmc-157 Text-To-Digital Person Video Generator: Digitalavatargen, Akansha Kesharwani, Nisha Bagdwal, Md E Hossain, Drashti Patel, Nikhil Adigoppula
C-Day Computing Showcase
The Text-to-digital person video generator: DigitalAvatarGen project uses AI to create lifelike videos of 2D digital avatars from user text input. Users enter text, select a voice and select or upload an avatar, and generate a video using DigitalAvatarGen web application which uses Google TTS and SadTalker, to synchronize voice, expressions, and lip movements. Key contributions include a customizable user interface, personalized voice and avatar options, and an optimized backend for efficient video generation. This tool provides an engaging, realistic solution for applications in education, media, and customer interaction.
Gmc-2162 Prompt Engineering And Its Effects On Ai And Human Relationships: A Contemporary Approach, Francis Madu, Naga Janaki Madhav Kadiyala, Nivesh Thallapally
Gmc-2162 Prompt Engineering And Its Effects On Ai And Human Relationships: A Contemporary Approach, Francis Madu, Naga Janaki Madhav Kadiyala, Nivesh Thallapally
C-Day Computing Showcase
A. Background: Prompt engineering refers to the process of designing and refining input prompts for AI models (especially language models like GPT) to improve their outputs. It has become a critical tool in maximizing the performance and utility of AI models in diverse applications, from customer service to content creation. Beyond technical aspects, the interaction between humans and AI is increasingly shaped by the effectiveness of these prompts. B. Motivation: As AI becomes more integrated into daily life, the way humans interact with AI models is profoundly influenced by prompt engineering. Misaligned prompts can lead to misunderstanding, confusion, or unintended …
Gmr-229 Semantic Search Using Sentence Transformers, Roshni Satish, Arpana Challa
Gmr-229 Semantic Search Using Sentence Transformers, Roshni Satish, Arpana Challa
C-Day Computing Showcase
Traditional keyword-based search engines struggle to accurately capture the semantics of user queries in today's enormous digital resources. Our research study focuses on creating a semantic search engine that uses Sentence Transformers to improve information retrieval by understanding the context of queries and documents. Our method creates sentence embeddings for documents and user queries, allowing retrieval based on semantic similarity rather than keyword matching. The project involves data collection and preprocessing, feature extraction with Sentence Transformers, and implementation of a search engine that ranks documents based on cosine similarity to query embeddings. According to preliminary testing, this method greatly improves …
Gpr-1194 Computer Vision-Enhanced Spectroscopy For Glucose Prediction: An In Vitro Validation Study, El Arbi Belfarsi
Gpr-1194 Computer Vision-Enhanced Spectroscopy For Glucose Prediction: An In Vitro Validation Study, El Arbi Belfarsi
C-Day Computing Showcase
This study introduces a novel computer vision-based spectral approach for non-invasive glucose detection using synthetic blood samples. We developed an experimental setup with glucose concentrations from 70 to 120 mg/dL, using two dye methods. Light sources tested included an 850 nm LED, 850 nm laser, 808 nm laser, and 650 nm laser, with image capture via a 1080p IR camera. Data augmentation, including Gaussian noise, contrast and brightness adjustments, rotations, and zooming, produced seven variants per image. Three machine learning models—CNN, AdaBoost, and ResNet—were evaluated, with the 850 nm light source yielding the best results: 87.5% of predictions fell within …
Gpr-6126 Utilizing Ml Techniques For A Quantum Augmented Http Protocol, Nitin Jha
Gpr-6126 Utilizing Ml Techniques For A Quantum Augmented Http Protocol, Nitin Jha
C-Day Computing Showcase
Over the past decade, several small-scale quantum key distribution (QKD) networks have been implemented worldwide. However, achieving scalable, large-scale quantum networks relies on advancements in quantum repeaters, channels, memories, and network protocols. To enhance the security of current networks while utilizing available quantum technologies, integrating classical networks with quantum elements appears to be the next logical step. In this study, we propose modifications to the HTTP protocol's data packet structure, adjustments to end-to-end encryption methods, and optimized bandwidth distribution between quantum and classical channels for high-traffic network routes.
Uc-181 Prison Minecraft Game Mode Plug-In, Ryan S Venable, Carson R Hunter, David Do
Uc-181 Prison Minecraft Game Mode Plug-In, Ryan S Venable, Carson R Hunter, David Do
C-Day Computing Showcase
A project designed for Kennesaw State University's owned Minecraft server. The project centers around creating a Minecraft plug-in, a software product that is easy to activate in any Minecraft server. This plug-in changes the standard rules of Minecraft to become a classic game mode called Prison where players are taken to a special map and tasked with collecting resources in specialized mines or by fighting each other for them to earn in game currency for the purpose of buying their way to more privileged positions in the prison, gaining access to new areas and features. Prison was designed to work …
Ur-172 A Comparative Study Of Llm Effectiveness In Mental Health Assistance, Kris Prasad
Ur-172 A Comparative Study Of Llm Effectiveness In Mental Health Assistance, Kris Prasad
C-Day Computing Showcase
This study evaluates the effectiveness of LLMs in supporting mental health applications by analyzing their performance in understanding and categorizing user (mental health-related) inputs. We collected data from various mental health apps on the Google Play Store, including user reviews and app descriptions, and filtered content using a targeted mental health keyword bank. Sentiment analysis and keyword similarity scores were generated for reviews using RoBERTa-based models, this showed us how each review aligned with the mental health keywords advertised by the app and how users felt about the app. We prompted four modern LLMs: GPT-4o, Claude 3.5 Sonnet, Gemma 2, …
Gmc-130 School Bus Monitoring Simulation W/ Apache Kafka, John Blake, Hudson Topping, Eun Sik Kim, Tiep Bui, Julio Lois
Gmc-130 School Bus Monitoring Simulation W/ Apache Kafka, John Blake, Hudson Topping, Eun Sik Kim, Tiep Bui, Julio Lois
C-Day Computing Showcase
Our project is a proof-of-concept of event-streaming bus telemetry data using Apache Kafka (Kafka), as requested by our client, Gwinnett County Public Schools (GCPS). The Kafka event stream is more efficient than GCPS’s current process of pulling bus telemetry data: calling APIs every 5 seconds. Moving to Kafka will provide GCPS near real-time insights into bus locations and speeds, giving visibility to whether buses drive safely and punctually. Our simulation produces synthetic bus data to be passed through Kafka and consumed. It features two UIs for system monitoring and data visualization.
Gmc-168 Hybrid Approach Of Data Mining And Deep Learning For Network Intrusion Classification In Big Data, Md Shamsul Alam, Yash Patel, Yaswanth Srinivas Gurram
Gmc-168 Hybrid Approach Of Data Mining And Deep Learning For Network Intrusion Classification In Big Data, Md Shamsul Alam, Yash Patel, Yaswanth Srinivas Gurram
C-Day Computing Showcase
The growing complexity and volume of network traffic pose significant challenges to traditional intrusion detection systems (IDS), often leading to inefficiencies in detecting unauthorized access and malicious activities. To identify different types of network attacks, many intrusion detection systems (IDSs) have been proposed using artificial intelligence or machine learning, but the results are still not satisfactory for most of these systems. Recently in some research, deep learning models have shown promising performance in big data analysis. However, a combined data mining and deep learning approach in big data for the detection of intrusions has not been scrutinized. This research aims …
Gmc-191 Optimizing K-Means Clustering For Customer Analytics: A Multi-Faceted Enhancement Approach, Carlos R Mota Jr, Bindu Neelam, Dedeepya Bonigala
Gmc-191 Optimizing K-Means Clustering For Customer Analytics: A Multi-Faceted Enhancement Approach, Carlos R Mota Jr, Bindu Neelam, Dedeepya Bonigala
C-Day Computing Showcase
This paper presents a detailed analysis of the al- gorithmic complexity of the K-means clustering algorithm, a foundational method in unsupervised machine learning. Although the problem of finding the optimal solution is NP-hard, K-means is widely used for efficiently partitioning data into clusters by minimizing within-cluster variance. We explore four main ideas for improvement:1) parallel points generation and processing for speeding up convergence, 2) penalty scoring for avoiding clusters with high variability within them, 3) utilization of other distance measurements such as Manhattan distance for providing better clustering in structures of objects of different nature and 4) probability addition in …
Gmc-241 Gta Request / Hiring Management, Vineetha Madasu, Pavan Kalyan Mandapaneni, Tharun Derangula, Thushara Thotakura, Raghurama Reddy Nambuvaripalli Reddappa
Gmc-241 Gta Request / Hiring Management, Vineetha Madasu, Pavan Kalyan Mandapaneni, Tharun Derangula, Thushara Thotakura, Raghurama Reddy Nambuvaripalli Reddappa
C-Day Computing Showcase
The Graduate Teaching Assistant (GTA) Management System is designed to address inefficiencies in the GTA hiring and assignment process at the departmental level. This full-stack web application utilizes modern Python frameworks such as Flask for backend development, providing a robust and scalable foundation for data handling and business logic. The frontend is developed using HTML, CSS, and JavaScript, ensuring an intuitive and responsive user experience. Key features include user access, automated GTA request generation based on course catalog data, and real-time tracking of hiring progress. By integrating data import capabilities from sources like Owl Express via Excel, the system handles …
Gmr-124 Emotion-Based Synthetic Feature Binary Classification Of Human Vs Llm Generated Text/Essay, Tong Xu, Rene P Lisasi, Patrick Wu
Gmr-124 Emotion-Based Synthetic Feature Binary Classification Of Human Vs Llm Generated Text/Essay, Tong Xu, Rene P Lisasi, Patrick Wu
C-Day Computing Showcase
Sentimental analysis is a popular method to classify text into various emotional tones and intentions. In the meanwhile, the emergence of large language models (LLMs) has become ever more capable, their potential to cause harm through information fabrication, misleading propagation, or mere lack of capability has also increased. Therefore, our project is designed to discover any patterns that could potentially uncover texts origins of human and LLM during sentimental analysis. Our dataset covers over 57,000 lengthier essay samples (70% human vs 30% LLM), we use the state-of-art pre-trained DistilRoBERTa-base, a powerful pre-trained language model that is a more condensed and …
Gmr-165 An Empirical Study Of Prompt-Based Non-Functional Requirements Classification, Allen Kim
Gmr-165 An Empirical Study Of Prompt-Based Non-Functional Requirements Classification, Allen Kim
C-Day Computing Showcase
In modern software development, Non-Functional Requirements (NFR) are essential to satisfy users’ needs. Distinguishing different categories of NFR is tedious, error-prone, and time consuming due to the complexity of software systems. In our project, we conducted a comprehensive study to evaluate the performance of prompt-based NFR classification by designing various handcraft templates and soft templates on the pre-trained language model (i.e., BERT). Our experimental results show that handcraft templates can achieve best effectiveness (e.g., 83.52% in terms of F1 score) but with unstable performance for different templates.
Gmr-216 Ai/Ml-Based Water Quality Monitoring Mobile App For Predicting E.Coli In Surface Waters, Keerthi Priya Karumanchi
Gmr-216 Ai/Ml-Based Water Quality Monitoring Mobile App For Predicting E.Coli In Surface Waters, Keerthi Priya Karumanchi
C-Day Computing Showcase
E.coli contamination in surface waters has proven to be a significant public health concern, requiring innovative monitoring solutions. This paper presents the design of an AI-driven mobile application to predict whether E.coli bacteria are present at levels exceeding acceptable thresholds in surface waters. The methodology employs sensor devices to collect water quality data parameters, such as water temperature, pH, dissolved oxygen, and turbidity. A dataset is generated based on these parameters, and machine learning (ML) algorithms are applied to evaluate accuracy, precision, recall, and processing time. Additionally, our ML algorithms establish a correlation matrix among water quality parameters to identify …
Gmr-7175 Enhancing Alzheimer’S Diagnosis Through Spontaneous Speech Recognition: A Deep Learning Approach With Data Augmentation, Venkata Sai Bhargav Mutala
Gmr-7175 Enhancing Alzheimer’S Diagnosis Through Spontaneous Speech Recognition: A Deep Learning Approach With Data Augmentation, Venkata Sai Bhargav Mutala
C-Day Computing Showcase
Alzheimer’s disease (AD) is a growing public health issue due to its progressive nature and rising prevalence. This study explores a neural network model trained on speech data from the ADReSS2020 Challenge dataset to distinguish AD patients from healthy individuals, using log-Mel spectrogram features. To improve accuracy, five data augmentation methods, including pitch and time shifting, were used. The results highlight deep learning, combined with data augumentation, as a promising, scalable, and noninvasive approach for early AD diagnosis
Gmr-8195 Machine Learning-Enhanced Hpcc Systems For Alzheimer's Disease Detection: A Scalable Solution For Early Diagnosis, Zularbine Kamal
Gmr-8195 Machine Learning-Enhanced Hpcc Systems For Alzheimer's Disease Detection: A Scalable Solution For Early Diagnosis, Zularbine Kamal
C-Day Computing Showcase
Alzheimer's disease is an incurable brain disorder that gradually deteriorates memory and cognitive abilities, leading to symptoms such as memory loss, confusion, difficulty in thinking, and changes in language, behavior, and personality. Early diagnosis that is effective, innovative, and cost-efficient can help mitigate damage to nerve cells. Detecting these symptoms through voice responses and analyzing the corresponding transcripts offers a promising approach. This poster demonstrates how an open-source platform can be utilized for text classification to identify Alzheimer's disease, employing fast, inexpensive, and non-invasive methods to complement other diagnostic techniques
Gpr-148 A Study Of Different Real-Time Robotic Applications, Yongshuai Wu
Gpr-148 A Study Of Different Real-Time Robotic Applications, Yongshuai Wu
C-Day Computing Showcase
Real-time operating systems (RTOS) are widely used in various robotic applications such as path planning and obstacle avoidance, which require real-time communication and interaction with the environment, posing significant challenges for RTOS design.In this paper, we will first explore different robotic control and decision-making applications based on RTOS. Then, we will study the implementations of several widely employed RTOS frameworks. Finally, we will analyze how different RTOS implementations impact overall system performance and discuss the advantages and limitations of these RTOS frameworks based on previous research.
Gpr-155 Integration Of Quantum Natural Language Processing (Qnlp) With Neo4j Llm Knowledge Graphs For Enhanced Nlp Tasks, Suman Bharti
Gpr-155 Integration Of Quantum Natural Language Processing (Qnlp) With Neo4j Llm Knowledge Graphs For Enhanced Nlp Tasks, Suman Bharti
C-Day Computing Showcase
This study investigates the integration of Quantum Natural Language Processing (QNLP) with Neo4j LLM Knowledge Graphs (KGs) to enhance natural language understanding tasks. By leveraging quantum circuit simulations, we aim to improve the probabilistic interpretation of relationships between entities. Our preliminary findings suggest that QNLP offers deeper insights compared to traditional NLP methods, particularly in modeling complex entity relationships. This approach also addresses significant limitations in Neo4j-based Large Language Model (LLM) Graph Databases, such as handling high dimensional relationships and capturing semantic nuances. The integration of QNLP into Neo4j refines relationship modeling and enhances performance in tasks like entity extraction …
Gpr-161 Meta-Reinforcement Learning With Discrete World Models For Adaptive Load Balancing, Cameron J Redovian
Gpr-161 Meta-Reinforcement Learning With Discrete World Models For Adaptive Load Balancing, Cameron J Redovian
C-Day Computing Showcase
We present a novel integration of the RL^2 meta-reinforcement learning algorithm with discrete world models, employing the DreamerV3 architecture, to enhance load balancing in operating systems. This integration allows for rapid adaptation to dynamic workload distributions with minimal retraining. In experiments using the Park load balancing environment, our approach outperformed the traditional AC3 algorithm in both standard and adaptive trials. Additionally, it exhibited strong resilience to catastrophic forgetting, maintaining high performance despite continuous variations in workload distribution and size. These results demonstrate the effectiveness of combining recurrent policy networks with discrete world models, offering a significant advancement in meta-learning capabilities …
Gpr-2212 Explainable Multi-Label Classification Framework For Behavioral Health Based On Domain Concepts, Francis E Nweke, Abm Adnan Azmee
Gpr-2212 Explainable Multi-Label Classification Framework For Behavioral Health Based On Domain Concepts, Francis E Nweke, Abm Adnan Azmee
C-Day Computing Showcase
Behavioral health, which covers mental health, lifestyle choices, addictions, and crises, poses serious issues in the community. Thus, appropriately analyzing and classifying behavioral health data is crucial for making informed healthcare decisions. Traditional deep learning and natural language processing approaches struggle to effectively identify behavioral health issues because the data is unstructured, complex, and lacks sufficient context. Furthermore, subject matter experts must be consulted to ensure effective identification. In this work, we proposed a deep learning-based framework consisting of several modules: A) domain concept encoder converts the keywords and their evidence types to vectors, which were predefined by a subject …
Gpr-2238 Tech Guru: A Domain Specific Llm For Tech. Industry, Francis E Nweke, Long Vu, Nitin Jha
Gpr-2238 Tech Guru: A Domain Specific Llm For Tech. Industry, Francis E Nweke, Long Vu, Nitin Jha
C-Day Computing Showcase
This project focuses on developing a domain-specific chatbot tailored for the tech industry. The chatbot utilizes articles sourced from blogs written by developers and engineers at leading companies such as Google and NVIDIA. Titles and content from these articles are extracted to form a question-answer dataset, with the titles acting as questions and the article content serving as answers. To refine the questions, we implemented a custom method to format the dataset to follow Alpaca format. The resulting question-answer pairs are then used to fine-tune a language model, adapting it to the specialized domain of the tech industry. Following this, …
Uc-129 Angel Among Us Pet Rescue - Website Enhancement With Chatbot, Koen Victorica, John Huffstutler, Chaz Dooley, Elizabeth A Kovaltchouk
Uc-129 Angel Among Us Pet Rescue - Website Enhancement With Chatbot, Koen Victorica, John Huffstutler, Chaz Dooley, Elizabeth A Kovaltchouk
C-Day Computing Showcase
This project integrates an AI-powered chatbot into the Angels Among Us Pet Rescue website, enhancing user experience by efficiently addressing common queries. The chatbot uses large language model (LLM) technology, which in this project is ChatGPT, to understand and respond to user questions dynamically. A content management system (CMS) supports easy updates to the chatbot’s responses, allowing Angels Among Us staff to manage FAQ entries without technical intervention. The chatbot integrates seamlessly with the existing website, maintaining the organization’s aesthetic, accessibility, and compatibility across devices. This enhancement improves user engagement and streamlines support, enabling the nonprofit to focus more on …
Uc-141 It Capstone Project 17 - Ksu Esports Tournament Bot, Patricia G Helfrick, Niranjanaa Jayakumar, Daniel J Schroeder, Trinity F Miller, Jackson M Stogsdill
Uc-141 It Capstone Project 17 - Ksu Esports Tournament Bot, Patricia G Helfrick, Niranjanaa Jayakumar, Daniel J Schroeder, Trinity F Miller, Jackson M Stogsdill
C-Day Computing Showcase
In this project, our team has automated tournament tasks in the KSU eSports Discord server, with a focus on the League of Legends tournaments. Our team has implemented a matchmaking algorithm that forms teams consisting of players placed within one tier of each other, so teams are evenly matched. Our team has also created a database that stores player statistics and has been integrated with the Discord bot. Furthermore, our team has integrated the developer API with the Discord bot, which pulls player data from the API when players join the server, and the team has been working to improve …
Uc-144 Attack Surface Management And Analysis, Danard S Mclemore, Nick A Tanner, Keshaun Berry, Nelson Thairu, Niang Ciin
Uc-144 Attack Surface Management And Analysis, Danard S Mclemore, Nick A Tanner, Keshaun Berry, Nelson Thairu, Niang Ciin
C-Day Computing Showcase
Recent advancements in AI have made knowledge more accessible, but this also introduces risks, as vulnerabilities can now be quickly found and exploited. To address this, we developed a comprehensive, cloud-native attack surface monitoring suite in Google Cloud. Integrating open-source intelligence tools like OWASP Amass and Project Discovery, along with custom Python-based processing, we gather extensive security data—covering subdomain enumeration, open ports, HTTP responses, and DNS configurations. This data is stored in BigQuery, processed, and visualized in Looker Studio for easy client interpretation. A containerized, scalable backend with a Flask-based API ensures seamless tool integration and adaptability. BigQuery ML further …
Uc-156 Swap - A Solo Developed Fps Game, Noah G Schultz
Uc-156 Swap - A Solo Developed Fps Game, Noah G Schultz
C-Day Computing Showcase
SWAP is an FPS game that blends tactical thinking with quick reflexes and player expression. Your dog Chomper has been kidnapped by the Big Dogs Mafia, and you must infiltrate their undercover locations to bring Chomper back home safe and sound. Along the way, the player will be asked to think on the fly, grabbing anything they can get their hands on to use as a weapon. From pistols and shotguns to forks, screwdrivers and keyboards, everything that the player can pick up is a deadly weapon.
Uc-166 The Eternal Guest - A 2d Hack-And-Slash Game, Luke H Gamage, Zion Johnson, Benjamin J Hedges, Bryanna N Walker, Avis A. Ewing
Uc-166 The Eternal Guest - A 2d Hack-And-Slash Game, Luke H Gamage, Zion Johnson, Benjamin J Hedges, Bryanna N Walker, Avis A. Ewing
C-Day Computing Showcase
The Eternal Guest is a narrative-driven, hack-and-slash combat and exploration game where you make meaningful friendships, battle enemies, and regain lost memories as you traverse a strange, non-euclidian hotel. Use a wide array of weapons and abilities alongside the knowledge you gain from other guests to attempt to escape the bloodlust of a homicidal vampire. The Eternal Guest emphasizes 2D, top-down, melee combat in combination with ranged abilities, offering an exciting dynamic to gameplay. Our game also presents a unique spin on randomized exploration through its unique D.R.E.A.D. system, creating a sense of unease and uncertainty when exploring. This unique …
Uc-173 Leveraging Large Language Models To Empower Caretakers Of People With Dementia, Mercy Olaniran, Emily A Centeno
Uc-173 Leveraging Large Language Models To Empower Caretakers Of People With Dementia, Mercy Olaniran, Emily A Centeno
C-Day Computing Showcase
Behavioral symptoms of Alzheimer's Disease and Related Dementias (ADRD) are detrimental to the quality of life for individuals with ADRD and their caregivers. Symptoms such as wandering, agitation, and confusion can often overwhelm caregivers leading to stress, depression, or burnout which can lead to a decrease in the quality of care. These challenges often result in increased hospitalizations and care costs, creating a need for a solution to support informal caregivers. This project proposes the development of an AI-based Dementia Care Voice Assistant application to meet the needs of caregivers. Using large language models, the application will provide real-time and …
Uc-176 Cybriant: Attack Surface Management, Diwakar Rai, Nicholas Agyen-Frempong, David Laurent, Daniel Gutierrez, Jose R Mendoza
Uc-176 Cybriant: Attack Surface Management, Diwakar Rai, Nicholas Agyen-Frempong, David Laurent, Daniel Gutierrez, Jose R Mendoza
C-Day Computing Showcase
As businesses and organizations expand their operation digitally, so too do the vectors for attack expand. In partnership with Cybriant, this application develops an Attack Surface Composite Score by breaking down various attack common vectors. DKIM records, Open Port Scanning, and other metrics are compiled with the aid of Google Cloud Run jobs, deposited into Google BigQuery for analysis, and packaged and generated using (Grafana/Kibana) as the front-end for our software stack. Our resulting application presents rapid, easy-to-understand breakdowns of various cybersecurity metrics and their impact.