In today’s digital age, the authenticity and security of certificates play a pivotal role in various domains, including education, employment, and legal documentation. Also, in a fiercely competitive job market, the proliferation of fraudulent academic certificates poses a significant challenge. Traditional paper-based certificates are prone to forgery and tampering, leading to a growing demand for robust and efficient certificate authentication systems. Also, Traditional certificate authentication methods are time-consuming, leading to delays in verifying credentials. This project presents a novel solution – the “Certificate Authentication System using QR Code” – developed utilizing PYTHON and MySQL technologies to enhance the verification and security of certificates. The primary objective of this project is to design and implement a user-friendly, efficient, and secure system that enables the issuance and authentication of digital certificates through QR codes. This innovative approach harnesses the power of PYTHON for application development and MySQL for database management to create a seamless and reliable certificate verification process. Our system offers a versatile solution, providing options for generating Bonafide Certificates, Transfer Certificates, and Course Completion Certificates. By adopting QR code technology, the system streamlines the certificate verification process, empowering organizations to swiftly and accurately verify the authenticity of certificates. This innovative approach not only addresses the challenge of certificate fraud but also enhances the efficiency and reliability of the verification process, ensuring that employers have a dependable means to authenticate certificates and make informed hiring decisions. It represents a significant step towards the digitization and security enhancement of certificate management systems, contributing to a more trustworthy and reliable certification ecosystem.
With the rapid growth of digital communication and image sharing, securing visual data from unauthorized access has become a significant concern. Traditional encryption methods are not always suitable for image-based data due to size, format, and processing constraints. This project proposes a Visual Cryptography-Based Image Security System that uses the concept of visual cryptography to split an image into multiple secure shares such that the original image can be revealed only when the correct shares are combined. Each individual share reveals no information on its own, ensuring maximum security. The system is ideal for securing confidential images like identity proofs, medical scans, biometric data, etc.
In digital banking systems, traditional password-based authentication is increasingly vulnerable to phishing, brute-force, and credential theft. To enhance transaction security, this project proposes a Multi-Factor Authentication (MFA) system that incorporates three verification layers: One-Time Password (OTP) sent to the user’s registered email. Security Question Verification (chosen at account setup). Random Puzzle Authentication (3×3 grid puzzle), where the correct solution is generated and the answer is sent to the user’s registered mail for confirmation. The system is developed using Python Flask as the backend, with secure email handling (SMTP), database integration for user credentials, and cryptographic hashing for sensitive information. This layered approach ensures that even if one factor is compromised, unauthorized access is still prevented.
The rapid adoption of Internet of Things (IoT) devices has created new opportunities but also introduced severe security threats. Due to limited computational resources and weak authentication mechanisms, IoT devices are vulnerable to botnet attacks, where compromised devices are hijacked to launch Distributed Denial of Service (DDoS) or other malicious activities. Traditional signature-based detection systems are inadequate in identifying novel and evolving botnet traffic. This project proposes an IoT Botnet Detection System using Deep Learning, which leverages neural networks to analyze network traffic patterns and identify malicious activities. The system is capable of detecting botnet attacks with high accuracy by learning hidden traffic patterns from IoT datasets such as UNSW-NB15, Bot-IoT, or N-BaIoT. The project provides a robust framework for real-time monitoring, anomaly detection, and prevention of large-scale IoT botnet attacks.
There are number of users who purchase products online and make payment through e- banking. There are e- banking websites who ask user to provide sensitive data such as username, password or credit card details etc. often for malicious reasons. This type of e-banking websites is known as phishing website. In order to detect and predict e-banking phishing website, we proposed an intelligent, flexible and effective system that is based on using classification Data mining algorithm. We implemented classification algorithm and techniques to extract the phishing data sets criteria to classify their legitimacy. The e-banking phishing website can be detected based on some important characteristics like URL and Domain Identity, and security and encryption criteria in the final phishing detection rate. Once user makes transaction through online when he makes payment through e-banking website our system will use data mining algorithm to detect whether the e-banking website is phishing website or not.
