College of Saint Elizabeth

Phishing remains one of the most effective initial access vectors despite continuous advancements in platform-level security. This paper presents an empirical evaluation of modern anti-phishing defenses by comparing adversary-in-the-middle (AITM) phis

Learning Data Structures and Algorithms (DSA) can often be noted to be an important challenge, primarily due to the abstract nature of program execution. To address this pedagogical challenge, the current paper presents RunSketch, an interactive educational application that will help with visualizing code in real-time, highlighting syntax, tracking variables, and providing AI-based narration. RunSketch uses code execution to provide feedback on the code to users, and provides explanatory information in the form of written text and spoken output using Google Text to Speech API. Its front-end architecture is based on React.js, D3.js and Monaco Editor, the back-end is written in Python using Flask and uses the sys.settrace() method to trace execution. RunSketch significantly enhances understanding and interaction of learners, due to the combination of visual and audio feedback systems, thus, becoming an important innovation in the sphere of teaching programming.

Medical IoT devices for health monitoring require constant power and connectivity, yet they face challenges in complex indoor environments like hospitals due to signal blockages and energy constraints. This paper proposes a deep learning-based approach for determining the optimal position and phase shift of a Reconfigurable Intelligent Surface (RIS) to enhance both energy harvesting and communication throughput. Simulations using OpenEMS and Octave model signal behavior in a hospital-like scenario. A regression-based neural network is trained using TensorFlow to predict the optimal RIS configuration. Results demonstrate a nonlinear, positively correlated relationship between RIS parameters and performance metrics, enabling intelligent optimization of RIS for real-time health IoT applications.

Human-Machine Interaction (HMI) is a branch of study that deals with man and the machine for the best way to interact and have a perfect interface that is efficient in its performance. This paper aims to discuss the historical development of HMI, core aspects, the technologies behind HMI, and the general uses of HMI. Originating from command-based interfaces, HMI evolved with GUI interfaces and then adopted many related technologies such as AI, augmented reality, and sensor systems. All these innovations have enabled various revolutionary uses in health sector, vehicle systems, learning institutions, robots, and games. The paper focuses on the use of multiple inputs including voice, touch, and gestures and issues of usability, security and adaptability. BCIs and affective computing are innovations that augment interaction paradigms by incorporating feelings and easy usability. Future developments in HMI require focusing on the following issues: the ethical problems, the possibility of increasing the adaptability of the system, and the integration of interdisciplinary approaches. HMI is of great importance to develop revolutionary technologies for industries and human life, to help build secure and efficient technologies with higher qualities that are capable of addressing human requirements in challenging situations.

Since the information technology revolution, cryptography has been essential for protecting sensitive information and communication. A plethora of algorithms have been developed to safeguard data and protect data integrity, with the goal of improving data security and protection. Modern cryptography is based on a variety of encryption techniques, namely Hybrid cryptography and multiple cryptography. Hybrid cryptography combines asymmetric key and symmetric key encryption to combine the strengths of both. Multiple cryptography is the process of encrypting a previously encrypted message one or more times, each time using the same or a different symmetric or asymmetric algorithm. The study proposes an approach that seeks to incorporate both hybrid and multiple cryptography as a way to combine all of their benefits while also minimizing the drawbacks that each technique has on its own to provide an additional layer of security. Although these techniques have all contributed to data protection, not all of them have been successful in providing a high level of security as they have shown a propensity for being targeted and exploited over time. This research project implements a cryptosystem based on Hybrid Multiple Cryptography (HMC) which is a novel scheme for data encryption that offers a greater level of security for sending data in an Android messaging application, in order to showcase the system's security capabilities in real time.

Our project aims to provide secure file storage for users on the cloud (specifically on AWS S3) by utilizing a hybrid cryptography approach. This involves encrypting files using both AES and RSA algorithms, with the user receiving the encryption key through email. To further enhance security, the key will be hidden behind an image or within a PDF document using steganography techniques. Additionally, the files stored in the S3 bucket will also be encrypted, but instead of encrypting the contents of the file, we will encrypt the file format itself. The file format can be decrypted using the GHE decryption key and software, which will be made available to end-users. Overall, our project aims to provide a robust and secure solution for cloud-based file storage.