Information and Computer Science‎

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https://hdl.handle.net/20.500.11888/10130

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Recent Submissions

Now showing 1 - 5 of 166
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    FIRE AND SMOKE DETECTION SYSTEM
    (2025-02-03) Mohammad Ammar Jararrah; Amr Adnan Badran
    The Fire and Smoke Detection System is an AI-powered solution designed for real-time detection of fire and smoke in video streams. Utilizing a YOLO-based deep learning model, the system processes video feeds from cameras, ensuring high accuracy in identifying potential fire hazards. The system comprises two main components: a Python server, responsible for video analysis and detection, and a .NET MAUI client, which provides users with real-time alerts and live monitoring capabilities. With features such as automatic network discovery, QR code-based remote access, and efficient communication between client and server, the system offers a seamless experience for both local and remote fire detection. This project aims to enhance fire safety by providing an accessible and efficient monitoring tool for homes, businesses, and industrial environments
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    CV Finder: Intelligent Recruitment tool
    (2025-02-02) Ayat Dardouk; Kinana Abueideh
    Abstract This project is a website platform to assist companies in simplifying the process of job hiring. The website allows companies to post job openings quickly with detailed job descriptions and required skills. The main function is to intelligently review the CVs of applicants with respect to the job requirements. Using natural language processing coupled with keyword matching, the platform finds a "similarity ratio" for each applicant, showing how well they fit the job. It helps companies easily rank and prioritize applicants based on their qualifications, saving a lot of time and effort in the first screening stage. The platform leverages advanced technologies like Natural Language Processing (NLP) and machine learning to intelligently extract information from CVs and compute match scores with job requirements. The implementation includes custom algorithms for PDF text extraction, model training using scikit-learn, and integration with APIs for automation and email notifications. Moreover, the system includes email services that allow companies to send emails to shortlisted candidates with invitations for interviews or additional instructions. This documentation describes our graduation project in seven chapters: • Chapter One, Abstract. • Chapter two Acknowledgment • Chapter Three you will find a brief introduction to the project • Chapter Four discusses the application methodology, the main work done in developing the application • Chapter Five, you will find a detailed description of the provided services, the software, and the APIs used in the project. • Chapter Six the Conclusion • Chapter Seven the future work will be found at the end of the document.
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    Quantum-Safe Hashing Algorithm
    (2025-02-03) Dana Abushawish
    In the era of quantum computing, ensuring cryptographic security requires innovative approaches. This project introduces a post-quantum cryptographic hashing algorithm designed to resist quantum-based attacks, particularly Grover’s algorithm. The proposed method utilizes two random oracles to generate entropy dynamically, following Boltzmann’s entropy formula and the concept of microstates. By leveraging this dynamic entropy mechanism, the algorithm produces a quantum- safe hash value that is computationally infeasible to reverse or find its preimage. Experimental evaluations demonstrate the robustness of the algorithm in providing enhanced security against quantum threats, offering a a significant step forward in post-quantum cryptographic research.
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    Engineering Projects Tracking System(EPTS)
    (2025-02-03) Leena Dweikat; Shahd Balalem
    The Engineering Projecct tracking System. is an innovative platform designed to enhance the efficiency of managing engineering tasks within projects. This system simplifies the process of creating new projects, assigning tasks to engineers, and monitoring their progress using a smart and intuitive approach. By leveraging features like a color-coded task management system and an integrated smart assistant, the platform ensures that tasks are distributed based on engineers' availability and expertise, enabling project managers to streamline workflows effectively. This project aims to transform traditional task management into a seamless and efficient process, reducing the workload of project managers and improving the overall productivity of engineering teams. The system provides real-time updates on task statuses, helping managers track progress and address issues promptly. By prioritizing simplicity, clarity, and efficiency, the platform offers a user-friendly interface tailored to meet the needs of engineering professionals. The Engineering Projecct tracking System. is more than just a tool; it is a comprehensive solution designed to assist project managers in organizing and completing projects on time while ensuring effective collaboration.
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    Reverse Engineering and Automated Defense Strategies for Detecting and Preventing Rat Malware
    (2025-02-03) Linda Bsharat; Lara Shahrori; Lana Khdair
    This research presents a multi-faceted approach to detecting and preventing Remote Access Trojan (RAT) malware using reverse engineering and automated defense mechanisms. The study employs static and dynamic analysis techniques to deconstruct RAT malware, revealing its structure, persistence mechanisms, and command-and-control (C2) communication. Advanced cybersecurity tools such as Wireshark, Process Monitor, and Regshot are utilized to analyze system modifications and network traffic. To enhance real-time detection, automated analysis through APIs like VirusTotal is integrated, enabling the extraction of malicious indicators. Custom Snort intrusion detection system (IDS) rules are generated and deployed dynamically within a pfSense firewall, ensuring active blocking of RAT-related network traffic. This approach demonstrates the effectiveness of combining manual expertise with automation in fortifying network security. The study's methodology provides a scalable defense strategy against evolving malware threats, with potential applications in broader cybersecurity frameworks.