A Review of Encryption Algorithms for Enhancing Data Security in Cloud Computing

Cloud computing is one of the most rapidly evolving technologies today; it provides numerous advantages that increase its affordability and dependability for use in the company. This paper goes over the concepts of cloud computing, such as its characteristics, deployment model, and service model, discusses the various benefits of cloud computing, and highlights the most pressing issues and security concerns in cloud storage. As a consequence, that leads to a review of distinctive cryptography algorithms that meet the security requirements (CIA: confidentiality, integrity, and availability) that are used to secure communications in cloud computing situations. It also displays many algorithms depending on the previous studies, such as Blowfish, RSA, DES, AES, MD5, Feistel, SP, HIGHT, LED, Cybpher, PRESENT, RC6, Diamond2, mCrypton, SLIM, Klein, PUFFIN-2, SEA, CLEFIA, LBlock, TWINE, 2, ANU, ANU-II, NLBSIT, Piccolo, BORON, RECTANGLE, LICI, QTL, LOGIC, TRIVIUM, Fruit-v2, Fruit-80, A4, the Enocoro family, and Grain family, to make a comparison among them using many measurements. It found that modern and lightweight algorithms are more suitable for use in this field. Furthermore, the purpose of this paper is to make some suggestions for improving the safety and security of cloud computing technologies

Adaptive Optimization of Deep Learning Models on AES based Large Side Channel Attack Data

Deep learning-based side-channel analysis is an efficient and suitable technique for profiling side-channel attacks. In order to obtain the better performance, it is highly necessary to analyze an in-depth training stage in which the optimization of relevant hyperparameters should be a vital process. During the training phase, hyperparameters that are connected to the architecture of the neural network are often selected; however, hyperparameters that impact the training process are to be effectively analyzed. This was represented by an optimized hyperparammeter that consists of considerable impact on attacking behaviour, which is the primary focus of our research. Our research has shown that even while the popular optimizers Adam and RMSprop are capable of delivering satisfactory outcomes, they are also tend to being overfit. Hence, it is necessary to use condensed training periods, simple profiling models, and explicit regularization in order to avoid this problem. On the other hand, the performance of optimizers of the SGD type is only satisfactory when momentum is used which results in slower convergence and less overfit. In conclusion, the research results provide a better use of Adagrad in the cases of longer training datasets or big profiling models

Medical Image Improvement Using a Proposed Algorithm

The clarity of medical images is important nowadays because it will be based on the diagnosis of the patient\u27s situation diagnosis, the phase of the disease, and give an appropriate treatment to him. The objective of the present study is to clarify the edges of the colored medical images by boosting thickness to dispose of the soft edges and some places that do not appear when the edge is determined. It can convert from RGB to HSV and then display the resulting image in the color space (HSV)set the edges of this image and add it to the image in space (HSV) then convert the resulting image from the color space (HSV) to RGB).  Use this algorithm to optimize images by Matlab2020a. This proposed method can be applied to all types of medical images such as (MRI, Ultrasound, X-ray, ... etc.) colored and gray, with any size and any part of the human body. The results give high resolution in the resulting images if showed an increase in the consistency of the resulting images

Application of Machine Learning Techniques for Countering Side-Channel Attacks in Cryptographic Systems

The use of machine learning algorithms in order to, not only, detect the adversarial intend behind side-channel attacks on cryptographic systems, but also to resist Differential Power Analysis (DPA) attacks. In particular, with the help of the DPA Challenge Dataset containing power traces of AES encryption operations, we propose a detailed step-by-step approach that includes data acquisition, preprocessing, feature extraction, and model assessment. The pre-processing includes noise reduction, normalization and segmental processing of the collected data for which basic statistical and frequency domain analysis can be used for extraction of relevant features. Support Vector Machines (SVMs) are then trained and tested in order to classify and in turn predict attack scenarios as per the subsequently derived features. As the outcome of the result pages show, the SVM model successfully classifies attack and non-attack traces at a rate of 88% on the validation set, which underlines the usage of machine learning to boost cryptographic security. Investigation of the feature relevance demonstrates that frequency-domain features, namely FFT coefficients are most impactful. The findings of this research prove that machine learning can be useful in preventing side-channel attacks apart from providing valuable information on enhancing the understanding of different defenses in cryptographic systems as well as future development of this domain

Improving Communication Performance Through Fiber Amplifier EDFA

Due to the development of fast and extensive data communication methods, typical erbium-doped fiber amplifiers (EDFA) have recently gained much interest. The main advantage of EDFA is its ability to build a system with a broad band. However, it is evident from documented works that using EDFA results in a gain increase. Nevertheless, the expense, complexity, and subpar effectiveness of these procedures made them ineffective. In this work, the performance of an 8-channel communication system for different distances of 80, 120, and 180 km is enhanced and maximized by applying a simulation model of long distance based on EDFA to reduce the effects of dispersion correction. The effect of the EDFA on three channels was investigated along the three distances by Q Factor and BER. The proposed system achieved good results in enhancing signals due to EDFA. The results of the extraction demonstrate the system\u27s capacity to send large data rates to 180 km with a bit error rate of less than 1×10-14. EDFA shows the best performance in gain differences. The simulation setup and implementation of the work aim to improve communication performance and propose a suitable solution to enhance the Bit Error Rate (BER)

ChatGPT and the Crisis of Academic Honesty

ChatGPT is one of the conversational AI tools that changed and still change the human life in different aspects wherein the academic and the scientific aspects seem to be affected more seriously. This paper, therefore, is an attempt to delve into some minutes of this influence in these two sensitive aspects. And this can be achieved through pinpoint its aggressions that are mainly caused by its capacities and limitations as far as scientific communication and studies are concerned. On its surface face, this AL seems to enrich scientific texts with clarity and coherence, to have a role in generation of new ideas and research methods, along with the dangers it creates to the honesty of academic efforts. The study, therefore, seems grim and pessimistic in this respect contrary to the immense praise and acceptance it receives at the universal level. It tries to call for a stricter human censorship over academic and scientific works to draw them away, as much as possible, from the injuries of this AI application. And to support this caution, it shows some of ChatGPT features and restrictions that turn it dangerous in the academic fields. And to be objective and accurate, the study refers to some of its positive aspects like its ability to help in creating coherence and clarity to scientific articles, research methods, and use in fighting plagiarism. As thus, the study highlights an urgent need for a necessary methods, restrictions, and strategies that can ensure a safe use of ChatGPT\u27s advantages and avoiding its weaknesses whenever it is used in scientific writing

Design of Deep Learning Techniques for Side-Channel Attacks on Masked 128-bit AES Implementations

Researchers are exploring the use of convolutional neural networks (CNNs) in side-channel attacks to understand the weaknesses in cryptographic implementation. CNNs can learn hierarchical characteristics automatically from electromagnetic radiation or power usage during cryptographic processes. Researchers train CNNs on side-channel data to extract meaningful representations and deduce secret keys. Deep learning algorithms are helpful in evaluating the security of embedded systems, and CNNs are a feasible paradigm for profiling side-channel analysis attacks. In this paper, it has been introduced a VGG (Visual Geometry Group)-Net architecture, which is a typical deep convolutional neural network design with numerous layers. It uses the ASCAD dataset to conduct experiments. They found that VGG-Net architecture Side Channel Attacks (SCA) provides better results than the previously optimized CNN model by significantly reducing the number of side-channel traces required for successful attacks on desynchronized datasets. The researchers also discovered that synchronous traces serve as the pre-training source for VGG-Net architecture, functioning successfully in terms of jittering with minimal fine-adjusting after trainin

Internet of Things: Architecture, Technologies, Applications, and Challenges

    A subset of cutting-edge information technology is the Internet of things (IoT). IoT refers to a network of physical objects with sensors attached that are linked to the Internet via LAN and WAN networking techniques. It is now commonly used to sense the environment and gather data in a variety of settings, including smart cities, healthcare, intelligent transportation, smart homes, and other structures. The IoT network architecture, core technology, and significant applications were outlined in this overview. The sensing layer, transport layer, and application layer are separated in the IoT network architecture. The essential technologies are embedded systems, network connectivity, sensor, and radio frequency identification (RFID) technology. IoT implementation in logistics still faces challenges despite the potential advantages. The utilization of technology in the IoT context is a topic with many open studies, which are also examined in this paper

A Robust Privacy Preserving Authentication Scheme for IOT Environment by 5G Technology: A Robust Privacy Preserving Authentication Scheme for IOT Environment by 5G Technology

 In recent years, secure communication between the interconnected components of the internet of things has become an important and worrying issue due to some attacks on the IoT. The Internet of Things (IOT) is the integration of things with the world of the Internet, where this integration takes place by adding devices or programs to be smart and, as a result, they will be able to communicate with one another and participate in all elements of life quite efficiently. Accordingly, we\u27ve developed an authentication protocol for the IoT ecosystem; it\u27s primary function is to ensure the safety of data exchange between the many devices that make up the IoT. Our proposed protocol is based on the elliptic curve cipher (ECC) algorithm, which greatly aids in protecting IoT components from physical assault. Our informal protocol analysis demonstrates that our solution not only protects users\u27 privacy by concealing their devices\u27 identities but also thwarts impersonation, counterattacks, and tracking and suggestion attacks directed at IoT devices. Security characteristics of the proposed protocol are also explicitly examined with the help of the ( SCYTHER) program. In addition, the effectiveness of the suggested protocol is evaluated by determining both its excess costs and its communication costs. Therefore, it appears that the protocol is vastly superior than the many other equivalent protocols by assessing its performance and security.In recent years, secure communication between the interconnected components of the internet of things has become an important and worrying issue due to some attacks on the IoT. The Internet of Things (IOT) is the integration of things with the world of the Internet, where this integration takes place by adding devices or programs to be smart and, as a result, they will be able to communicate with one another and participate in all elements of life quite efficiently. Accordingly, we\u27ve developed an authentication protocol for the IoT ecosystem; it\u27s primary function is to ensure the safety of data exchange between the many devices that make up the IoT. Our proposed protocol is based on the elliptic curve cipher (ECC) algorithm, which greatly aids in protecting IoT components from physical assault. Our informal protocol analysis demonstrates that our solution not only protects users\u27 privacy by concealing their devices\u27 identities but also thwarts impersonation, counterattacks, and tracking and suggestion attacks directed at IoT devices. Security characteristics of the proposed protocol are also explicitly examined with the help of the ( SCYTHER) program. In addition, the effectiveness of the suggested protocol is evaluated by determining both its excess costs and its communication costs. Therefore, it appears that the protocol is vastly superior than the many other equivalent protocols by assessing its performance and security

An Explainable Content-Based Course Recommender Using Job Skills

The large number of courses offered in universities and online studies made it difficult for students to choose the courses that suit their interests and career goals, which led students to lose many opportunities to be employed in the job they wanted. To keep pace with the rapid development of technology, and instead of relying on the job title as was previously done, the employers began to identify the skills required for a job. The competencies of the candidates are then examined and evaluated according to those requirements. Thus, it has become necessary for students to take courses that suit their future professional interests, ensuring that they are employed in the job they desire and supporting their long-term career success. Fortunately, the emergence of skills-based employment has provided an opportunity for universities and colleges to create a clearer path to the courses offered to allow students to take courses that match their future career interests. In this study, we used K-Mean clustering algorithm, TF-idf approach, and content-based filtering algorithm to provide relevant courses for students based on the required job with an explanation of why these courses are recommended. Our result illustrates that our method offers many advantages compared with other recommender systems. our system converts a simple course recommendation into a tool for discovering skills.  Since many recommendation systems work as black boxes, we designed our system to recommend the relevant course with explaining why these courses are recommended, which will add a factor of transparency to our system and confirms the reliability of the system to the students