DEPARTMENT OF COMPUTER SCIENCE,

This project focuses on the design and implementation of a lightweight honeypot system aimed at detecting and logging unauthorized access attempts on a local network. With cyberattacks
becoming increasingly common and sophisticated, many small organizations lack the tools to properly monitor their networks. Honeypots offer a simple yet effective way to study intrusion behaviour by acting as decoy systems that attract potential attackers. The system developed in this project was built using Python due to its flexibility and strong support for network programming through libraries such as socket, datetime, and logging. It works by listening on a specific network port, recording each incoming connection, and logging details such as the intruder’s IP address, timestamp, and input commands into a text file named honey.txt. Using the prototyping methodology, the system was built and tested within a controlled local
environment using Telnet and Nmap to simulate intrusion attempts. The results confirmed that the honeypot could efficiently detect connections, capture relevant data, and operate smoothly with minimal resource use. Overall, this project shows that lightweight honeypots can provide valuable insights into network security and help raise awareness of potential threats, especially for small organizations and students. Future improvements could include multi-port monitoring, real-time alert features, and database-based log management to enhance system efficiency and scalability.

Sales order accounting system development is essential for modern retail businesses seeking to eliminate the inefficiencies of manual sales processing, inventory tracking, and financial reporting. This project presents the design and implementation of a fully automated, client-side sales order accounting system built with HTML5, CSS3, vanilla JavaScript, and localStorage. The system integrates seamless sales order creation with unique identifiers and real-time input validation, dynamic inventory management featuring instant stock updates and proactive low-stock alerts, and comprehensive financial reporting modules that generate daily sales summaries, profit-and-loss statements, and exportable transaction logs. It also supports basic marketing functions by enabling secure export of customer purchase records for personalized email or SMS promotions. The application runs entirely offline, supports cross-session data persistence, and features a clean, intuitive, mobile-responsive interface, making it suitable for small businesses with limited technical infrastructure. Although Shoprite is referenced as an example of a large retailer already using computerized systems, this solution is specifically designed to provide small businesses with an affordable, scalable blueprint for digital transformation. Rigorous testing showed 98% accuracy in transaction processing, a 70% reduction in order-processing time, and a 4.7/5 satisfaction rating from test users, demonstrating its reliability, efficiency, and practical value in real retail environments.