Face Recognition Attendance System

Face recognition systems are used in practically every industry in this digital age. One of the most widely utilized biometrics is face recognition. It can be used for security, authentication, and identity, among other things. Despite its low accuracy relative to iris and fingerprint identification, it is extensively utilized because it is a contactless and non-invasive technique. Face recognition systems can also be used to track attendance in schools, colleges, and companies. Because the existing manual attendance system is time consuming and difficult to maintain, this system intends to create a class attendance system that employs the concept of face recognition. There’s also the possibility of proxy attendance. As a result, the demand for this system grows. Database development, face detection, face recognition, and attendance updating are the four steps of this system. The photos of the kids in class are used to generate the database. Faces are discovered and recognized from the classroom's live streaming footage. At the end of the session, the attendance will be mailed to the appropriate faculty. Keywords: Smart Attendance System, NFC, RFID, OpenCV, NumPy

Research on Security Access of Power Internet of Things Terminals Based on Edge Computing Technology

In view of the current lack of unified security authentication and control for the power Internet of Things terminal equipment, at the perception level of the power Internet of Things, the perception layer terminal access control, front-end authentication technology realization and terminal equipment abnormal behavior detection methods are proposed. This method enhances the communication security between power equipment and edge nodes, and ensures the safe and stable operation of the power Internet of Things.

Build Encrypted Interconnection Networks by application of IP Security and Mac Address Filtering Authentication Methods: بناء شبكات ربط مشفرة من خلال تطبيق أساليب مصادقة أمان IP وتصفية عنوان Mac

To improve the security in data networks we use of IP Security and MAC Address Filtering authentication methods on network devices is very useful to be able to protect, verify and filter company data especially if data contain sensitive information like credit cards while using public data network. IP Security authentication provides integrity between connections, then Filtering MAC Address can help the router task to be able recognize users on the network, So that expected the combination between IP Security and Mac Address Filtering will provide security for every transfer and receive data from Headquarter to branch office, then the company doesn't have to worry about data package being robbed or manipulated by the unauthorized parties.

The Security of a Blockchain-Based File System in a Software Defined Network Framework

Developing distributed form of file security systems using Blockchain technology. Based on the idea of cloud storage as it is a leading storage technology for huge data storage. Blockchain is one of the trending technology for decentralized data storage systems that ensures privacy, confidentiality, data security, authentication, and integrity. As SDN network provides support to have various nodes in the network for the secure transaction of data from source to destination. Blockchain helps in keeping track of block data by constructing the gateway to make it immutable. BCFS refers to Blockchain-Based File System Security in SDN. In the designed system, a Web-Based Interface is developed an authorized entity can upload file data the user’s file is projected to encryption process and the block data is shared among the various nodes in the network. Along with Unique Document ID, encrypted random key, and hash data. This hash data value holds the file path and preserves in the blockchain into their corresponding block data folders. Detection of node failure across the network an automatic short path is chosen by the network and detection of an attack based on entropy value.

Face Recognition Attendance System using Raspberry Pi

Face recognition is used for security, authentication, identification, and has got many advantages over conventional methods. It is being used in many sectors since it is contactless and non-invasive. Billions of images have been uploaded on social media networks and are crawled by search engines over many years. These images may include many different faces. The increase in computing capability and collected data has helped in creating more powerful neural network models. [1] This project thesis aims to create an attendance system which uses face recognition biometric authentication as the currently used manual attendance system is cumbersome to maintain and time consuming. Face recognition prohibits the chance of students marking attendance for their peers (proxy attendance). Keywords: Face Recognition, Face embeddings, Face Detection, Image Processing, Raspberry Pi automation.

An improved NFC device authentication protocol

Aimed at the security authentication problem between Near Field Communication (NFC) devices, this paper uses the technology of asymmetric encryption algorithm, symmetric encryption algorithm, hash function, timestamp and survival period to improve the confidentiality, performance and security of the protocol. The symmetric encryption algorithm encrypts the transmission content, while the asymmetric encryption algorithm encrypts the shared key. The whole authentication process is secure, and the key distribution is secure. The improved NFC device authentication protocol can effectively resist the brute force attack, man-in-the-middle attack and replay attack in the authentication process, it can reduce the number of message transmission in the authentication process, improve the transmission efficiency, enhance the confidentiality, integrity, non-repudiation and improve the security of NFC device authentication.

Enhanced Security Authentication Based on Convolutional-LSTM Networks

The performance of classical security authentication models can be severely affected by imperfect channel estimation as well as time-varying communication links. The commonly used approach of statistical decisions for the physical layer authenticator faces significant challenges in a dynamically changing, non-stationary environment. To address this problem, this paper introduces a deep learning-based authentication approach to learn and track the variations of channel characteristics, and thus improving the adaptability and convergence of the physical layer authentication. Specifically, an intelligent detection framework based on a Convolutional-Long Short-Term Memory (Convolutional-LSTM) network is designed to deal with channel differences without knowing the statistical properties of the channel. Both the robustness and the detection performance of the learning authentication scheme are analyzed, and extensive simulations and experiments show that the detection accuracy in time-varying environments is significantly improved.