Deep learning (DL) is a new option that has just been made available for side-channel analysis. DL approaches for profiled side-channel attacks (SCA) have dominated research till now. In this attack, the attacker has complete control over the profiling device and can collect many traces for a range of critical parameters to characterise device leakage before the attack. In this study, we apply DL algorithms to non-profiled data. An attacker can only retrieve a limited number of side-channel traces from a closed device with an unknown key value in non-profiled mode. The authors conducted this research. Key estimations and deep learning measurements can reveal the secret key. We prove that this is doable. This technology is excellent for non-profits. DL and neural networks can benefit these organisations. Neural networks can provide a new technique to verify the safety of hardware cryptographic algorithms. It was recently suggested. This study creates a non-profiled SCA utilising convolutional neural networks (CNNs) on an AVR microcontroller with 8 bits of memory and the AES-128 cryptographic algorithm. We used aligned power traces with several samples to demonstrate how challenging CNN-based SCA is in practise. This will help us reach our goals. Here is another technique to create a solid CNN data set. In particular, CNN-based SCA experiment data and noise effects are examined. These experiments employ power traces with Gaussian noise. The CNN-based SCA works well with our data set for non-profiled attacks. Gaussian noise on power traces causes many more issues. These results show that our method can recover more bytes successfully from SCA compared to other methods in correlation power analysis (CPA) and DL-SCA without regularisation.

During the years 2018-2024, considerable advancements have been achieved in the use of deep learning for side channel attacks. The security of cryptographic algorithm implementations is put at risk by this. The aim is to conceptually keep an eye out for specific types of information loss, like power usage, on a chip that is doing encryption. Next, one trains a model to identify the encryption key by using expertise of the underpinning encryption algorithm. The encryption key is then recovered by applying the model to traces that were obtained from a victim chip. Deep learning is being used in many different fields in the past several years. Convolutional neural networks and recurrent neural networks, for instance, have demonstrated efficacy in text generation and object detection in images, respectively. Deep learning has been effective throughout the side-channel analysis field. Until 2024, there was no deep learning layers made especially for SCAs. In this article, a systematic review on hybrid deep learning models for enhancing encryption techniques against side channel attacks is presented here.

Power side channel analysis signal analysis is automated using deep learning. Signal processing and cryptanalytic techniques are necessary components of power side channel analysis. Chip leakages can be found using a classification approach called deep learning. In addition to this, we do this so that the deep learning network can automatically tackle signal processing difficulties such as re-alignment and noise reduction. We were able to break minimally protected Advanced Encryption Standard (AES), as well as masking-countermeasure AES and protected elliptic-curve cryptography (ECC). These results demonstrate that the attacker knowledge required for side channel analysis, which had previously placed a significant emphasis on human abilities, is decreasing. This research will appeal to individuals with a technical background who have an interest in deep learning, side channel analysis, and security.

Because of COVID-19, healthcare became the first interesting domain at the world. Here, comes the role of researchers to do what they can to guide people. Nowadays, the most wanted field is beauty industry. It achieved large market. And the estimation is toward the growing. Researchers can give advice to prevent unhealthy causes in this field. They can apply sentiment analysis methods to make decision whether a Beauty center is healthy or unhealthy. This work develops an improved method of sentiment analysis to classify the beauty centers in Iraq into healthy and unhealthy classes. Researchers used comments of beauty centers’ Facebooks to perform the assessment. The methodologies encompass the two approaches lexicon-based and machine-learning-based. Three machine learning mechanisms had been applied; rough set theory, naïve bayes, and k-nearest neighbors. It will be shown that rough set theory is the best compared with the others two. Rough set theory achieved 95.2%, while Naïve Bayes achieved 87.5% and k-nearest neighbors achieved 78%.

Today, social media sites like Twitter provide effective platforms to share opinions and thoughts in public with millions of other users. These opinions shared on such sites influence a large number of people who may easily retweet them and accelerate their spread. Unfortunately, some of these opinions were expressed by extremists who promoted hateful content. Since Arabic is one of the most spoken languages, it is crucial to automate the process of monitoring Arabic content published on social sites. Therefore, this study aims to propose a hybrid technique to detect extremism in Arabic social media texts and articles to monitor the situation of published extremist content. The proposed technique combines the lexicon-based approach with the rough set theory approach. The rough set theory is employed with two approximation strategies: lower approximation and accuracy approximation. The hybrid technique used the rough set theory as a classifier and the lexicon-based as a vector. Furthermore, this study built three types of corpuses (V1, V2, and V3) collected from Twitter. The experimental findings show that among the proposed hybrid methods, the accuracy approximation was superior to the lower approximation with seed vector. It was also revealed that hybrid methods outperformed machine learning techniques in terms of efficiency. Moreover, the study recommends using an accuracy approximation method with seed vector to identify the polarity of the text.

Identification and access have been a concept that has evolved over time as the need to constantly identify people and grant them access to sensitive and classified data and information became very important. The effect is felt in most organizations, especially multinational companies that deal in highly classified research that has to do with pharmaceuticals, technology, power as well as the human biology coupled with security. The most common form of implementation of biometrics is facial recognition, fingerprints, iris recognition, a retina scanner, and voice recognition into so many applications and scenarios. The integration of this biometrics has had a rising effect and impact of everyday life and has practically changed some daily routines. This paper will examine future integrations of biometrics and it will in time affect everyday life and routine.