Journal of Cybersecurity and Information Management

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2690-6775ISSN (Online) 2769-7851ISSN (Print)

Managing a Secure JSON Web Token Implementation By Handling Cryptographic Key Management for JWT Signature in REST API: : A survey

Nihal Salah

  JSON Web Token (JWT) is a compact and self-contained mechanism, digitally authenticated and trusted, for transmitting data between various parties. They are mainly used for implementing stateless authentication mechanisms. The Open Authorization (OAuth 2.0) implementations are using JWTs for their access tokens. OAuth 2.0 and JWT are used token frameworks or standards for authorizing access to REST APIs because of their statelessness and the signature implementation. The most important cryptographic algorithms were tested namely a symmetric algorithm HS256 (HMAC with SHA-256) and an asymmetric algorithm RS256 (RSA Signature with SHA-256) used to construct JWT for signing token based on several parameters of the speed of generating tokens, the size of tokens, time data transfer tokens and security of tokens against attacks.In this research,we propose an approach used for handling cryptographic key management for signing RS256 tokens to ensure the security of the application's architecture. JWT offer a variety of options to manage keys, the server always needs to verify the validity of the key before trusting it for verify that a JWT implementation is secure.The experimental results show It's better to use the RS256 signature method for handling cryptographic key management for signing tokens to manage a secure JWT Implementation

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Vol. 6 Issue. 1 PP. PP. 5-17, (2021)

Image Classification Based On CNN: A Survey

Ahmed A. Elngar , Mohamed Arafa , Amar Fathy , Basma Moustafa , Omar Mahmoud , Mohamed Shaban , Nehal Fawzy

Computer vision is one of the fields of computer science that is one of the most powerful and persuasive types of artificial intelligence. It is similar to the human vision system, as it enables computers to recognize and process objects in pictures and videos in the same way as humans do. Computer vision technology has rapidly evolved in many fields and contributed to solving many problems, as computer vision contributed to self-driving cars, and cars were able to understand their surroundings. The cameras record video from different angles around the car, then a computer vision system gets images from the video, and then processes the images in real-time to find roadside ends, detect other cars, and read traffic lights, pedestrians, and objects. Computer vision also contributed to facial recognition; this technology enables computers to match images of people’s faces to their identities. which these algorithms detect facial features in images and then compare them with databases. Computer vision also play important role in Healthcare, in which algorithms can help automate tasks such as detecting Breast cancer, finding symptoms in x-ray, cancerous moles in skin images, and MRI scans. Computer vision also contributed to many fields such as image classification, object discovery, motion recognition, subject tracking, and medicine. The rapid development of artificial intelligence is making machine learning more important in his field of research. Use algorithms to find out every bit of data and predict the outcome. This has become an important key to unlocking the door to AI. If we had looked to deep learning concept, we find deep learning is a subset of machine learning, algorithms inspired by structure and function of the human brain called artificial neural networks, learn from large amounts of data. Deep learning algorithm perform a task repeatedly, each time tweak it a little to improve the outcome. So, the development of computer vision was due to deep learning. Now we'll take a tour around the convolution neural networks, let us say that convolutional neural networks are one of the most powerful supervised deep learning models (abbreviated as CNN or ConvNet). This name "convolutional" is a token from a mathematical linear operation between matrixes called convolution. CNN structure can be used in a variety of real-world problems including, computer vision, image recognition, natural language processing (NLP), anomaly detection, video analysis, drug discovery, recommender systems, health risk assessment, and time-series forecasting. If we look at convolutional neural networks, we see that CNN are similar to normal neural networks, the only difference between CNN and ANN is that CNNs are used in the field of pattern recognition within images mainly. This allows us to encode the features of an image into the structure, making the network more suitable for image-focused tasks, with reducing the parameters required to set-up the model. One of the advantages of CNN that it has an excellent performance in machine learning problems. So, we will use CNN as a classifier for image classification. So, the objective of this paper is that we will talk in detail about image classification in the following sections.  

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Vol. 6 Issue. 1 PP. PP. 18-50, (2021)

Explaining feature detection Mechanisms: A Survey

Ahmed A. Elngar , Mohamed Arafa , Mustafa Marouf , Mahmoud Ahmed , Nehal Fawzy

  Feature detection, description and matching are essential components of various computer vision applications; thus, they have received a considerable attention in the last decades. Several feature detectors and descriptors have been proposed in the literature with a variety of deļ¬nitions for what kind of points in an image is potentially interesting (i.e., a distinctive attribute). This chapter introduces basic notation and mathematical concepts for detecting and describing image features. Then, it discusses properties of perfect features and gives an overview of various existing detection and description methods. Furthermore, it explains some approaches to feature matching. Finally, the chapter discusses the most used techniques for performance evaluation of detection algorithms.  

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Vol. 6 Issue. 1 PP. 51-64, (2021)