AlKadhum Journal of Science
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Application of Machine Learning Techniques for Countering Side-Channel Attacks in Cryptographic Systems
No full textThe 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
Design of Deep Learning Techniques for Side-Channel Attacks on Masked 128-bit AES Implementations
No full textResearchers 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
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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
No full text 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
Adaptive Optimization of Deep Learning Models on AES based Large Side Channel Attack Data
No full textDeep 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
The Role of Soft Power in Dominating Public Sector and Increasing Unemployment Rates in the Economy of Iraq: An Analytic Vision
No full textThe problem of the research is that soft power mobilized most of the local labor force towards employment in the public sector without focusing on creativity in creating private business opportunities for a promising future of self-reliance with the absence of strategic care for these contributions through financing and incubating them in private incubators. Research hypothesis: soft power leads to a slackness of the public sector and an increase in unemployment rates, which contributes to wasting human capital and local energies, as well as pressures in the public budget for more employment. The aim of the research: to shed light on the importance of the role of soft power in its passing political rather than economic goals through increased employment in the public sector, neglect of the private sector, Deterioration of real economic indicators and high unemployment rates. The research found that the public sector was slack in light of soft power. It was a call to request more employment with political gains regardless of economic efficiency, which contributed to the migration of large numbers of local youth energies, while neglecting the role of business incubators in nurturing private leadership of youth energies in order to increase. The research recommended giving exceptional importance to the role of business incubators in refining the talents of the workforce and relying on self-development of local youth energies. which contributed to the migration of large numbers of local youth energies, while neglecting the role of business incubators in nurturing private leadership of youth energies in order to increase recruitment. The research recommended giving exceptional importance to the role of business incubators in refining the talents of the workforce and relying on self-development of local youth energies. which contributed to the migration of large numbers of local youth energies, while neglecting the role of business incubators in nurturing private leadership of youth energies in order to increase recruitment. This research recommended giving exceptional importance to the role of business incubators in refining the talents of the workforce and relying on self-development of local youth energies
Beyond Polarity: The Potential Applications and Impacts of Sentiment Analysis and Emotion Detection
No full textOpinion mining and emotion detection are two important techniques in natural language processing that have gained significant attention in recent years. Opinion mining is the process of identifying and extracting subjective information from text, such as opinions, attitudes, and emotions, while emotion detection is the process of identifying and extracting emotions from text. These techniques have a wide range of applications in various domains, including social media analysis, customer feedback analysis, and product reviews. This paper provides an overview of opinion mining and emotion detection techniques in natural language processing. We discuss the various approaches and methods used in opinion mining and emotion detection, including machine learning, deep learning, and natural language processing techniques. We also explore the challenges and limitations of these techniques, including the subjectivity of language, cultural differences, and the lack of labeled data. Furthermore, we examine the current state of the art in opinion mining and emotion detection, highlighting recent research and developments in these areas. We also discuss the potential applications of these techniques in various domains, including marketing, healthcare, and social media analysis. Overall, this paper provides a comprehensive overview of opinion mining and emotion detection in natural language processing. It provides insights into the methods, challenges, and potential applications of these techniques, and highlights the importance of these techniques in understanding and analyzing subjective information in text
Enhancement the Security by Creating Ontology-Based Trust Management Using Semantic Web Tools
No full textأكثر نماذج السياسة التقليدية لا تأخذ في الاعتبار الطبيعة الديناميكية بل ركزت
أنظمة التوزيع في معالجة قضايا مثل القدرة على التكيف والتوسعة والتفكير في السياسات الأمنية. السبب الرئيسي لمشكلات المرونة وقابلية التوسع في بيئات الإنترنت والشبكات الديناميكية هو عدم وجود تحكم مركزي في البيئات، وعدم تحديد المستخدمين مسبقًا. ونتيجة لذلك، تصبح قضايا الأمن والثقة حاسمة في الأنظمة المختلفة؛ إن تعزيز أمن هذه البيئات يتطلب إضافة الثقة إلى البنى التحتية الأمنية الحالية. وقد أخذت القليل من نماذج الثقة في الاعتبار العلاقة الدلالية للعناصر السائدة، على الرغم من أنه تم اقتراح العديد من النماذج لمعالجة قضايا الثقة في البيئات الديناميكية؛ وخاصة تلك النماذج التي تتعلق بفئات الثقة. نقوم في عملنا بحل المشكلات الناتجة عن الأمن وتتبع ديناميكيات أجهزة الاتصال المشاركة في الشبكات الديناميكية الموزعة. من خلال استخدام الانطواوجيا لإدارة الثقة والذي يتم من خلاله تحديد المفردات المستخدمة لوصف وتمثيل مجال المعرفة. للتمثيل، استخدمنا أدوات الويب الدلالية لتمثيل مجال البيئة الديناميكيةThe most traditional policy models which do not consider dynamic nature of distribute systems and the limitation in addressing issues like adaptability, extensibility, and reasoning over security policies. The main cause of the flexibility and scalability issues in the environments of the Internet and dynamic networks is that there is no central control over the environments, and users are not predetermined. As a result, security and trust issues become critical in the various systems; enhancing the security of these environments would require adding trust to the existing security infrastructures. Few trust models have taken into account the semantic relationship for pervasive elements, despite the fact that numerous models have been proposed to address trust issues in dynamic environments; especially those who related to trust categories. In our work, we solve issues resulted from security and tracking the dynamics of participating communication devices in dynamic distributed networks. Through using ontology for trust management which it is define vocabularies used to described and represented an area of knowledge. For representation, we used semantic web\u27s tools to represent the domain of the dynamic environment and we improv that the reasoning succusses in inference the trusted device and user exactly where we do query.
Applications of the Internet of Nano Things in Health Care
No full textThe Internet of Nano Things (IoNT) is a relatively new addition to the Internet of Things (IoT). The IoNT refers to the connectivity of nanodevices and nanosensors, and next-generation standards based on the IoT have been established to connect many nanomachines with current communications systems via the Internet. In communication networks, nanoscale applications provide a new edge. Because of the novel features resulting from nanotechnology\u27s benefits, IoNT opened the door to a slew of new applications in a range of fields. This study provides an overview of the IoNT technique, including its implementation, objectives, general implications, and most significant problems, as well as the differences between IoT and IoNT. The architecture of the IoNT in smart health care, as well as the most essential technologies used in nano communication networks, have been recognized, along with an assessment of each technology\u27s merits
Harmonizing Some Solutions for E-learning and Online Learning: A Case Study of Imam Kadhum College
No full textThis paper presents descriptive information for E-Learning, its design, and development as well as the application, integration, and management as it relates to Imam Kadhum College. One of the most significant things E-Learning applications have done is remove distance as a barrier to learning. E-Learning applications in form of online or virtual classrooms have been embraced especially in environments that have the facility to accommodate them. However, recent trends have caused a lot of reputable institutions such as Imam Kadhum College to re-examine their respective model, the mode in which education is disseminated to their student population as Online Classrooms increased competition among institutions all over the world. This paper aims to examine E-Learning, the possible effects it already has on learning and future implications it might have on the way education is disseminated in a learning environment such as Imam Kadhum College. The sample size taken into consideration is the population of Imam Kadhum (a) College for Islamic Science, Iraq. A couple of E-Learning Systems and software online such as Edx.org will also be examined in this research and will be used as a reference syste