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Thursday, April 24 (between 11:00 a.m. and 12:00 a.m.) an operation is planned on the database.
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Invited Speakers
Professor ASIMI Ahmed is a full professor at the Faculty of Science, Agadir, Morocco. He received his Ph.D degree in Number theory from Department of Mathematics, Faculty of Science, University Mohammed V, Agdal in 2001, Morocco. He is reviewer at the International Journal of Network Security (IJNS). His main areas of research interests include Number theory, Code theory, Cloud and MultiCloud optimization, Blockchain, Computer Cryptology( Homomorphic Cryptography), IoT, and Network and Cyper Security. Title: Homomorphic security and its applications: IS, CC, BC and IoT Abstract: In an increasingly interconnected and data-driven world, protecting sensitive information has become a major challenge. The rise of intelligent systems, cloud computing, blockchain, and the Internet of Things (IoT) technologies requires new security approaches that can protect data not only in transit and storage, but also during processing. It is in this context that homomorphic security, and in particular homomorphic encryption, stands out as a revolutionary advance. Cloud computing has become the infrastructure of choice for large-scale data storage and processing. However, this outsourcing raises concerns about the confidentiality of sensitive data, exposed to untrusted third parties. Traditionally, to perform operations on data in the cloud, it is necessary to decrypt it, which makes it vulnerable to hacking. Homomorphic encryption offers an elegant solution to perform computations on encrypted data without exposing it. Blockchain is praised for its ability to provide transparent and secure transactions in decentralized environments. However, this transparency sometimes conflicts with privacy needs, particularly for financial transactions and personal data. Homomorphic encryption would introduce a layer of confidentiality on public blockchains, allowing private computations on data while maintaining the verifiability of transactions. This technology could strengthen trust in sectors such as smart contracts, where stakeholders want to protect their data while executing automated transactions. Intelligent systems, such as artificial intelligence and machine learning algorithms, rely on vast amounts of data to operate effectively. In fields such as healthcare, finance, or national security, this data is often sensitive and its leakage could have dramatic consequences. With homomorphic encryption, it becomes possible to train artificial intelligence models directly on encrypted data, ensuring complete confidentiality throughout the process. The IoT is a network of countless connected objects, ranging from industrial sensors to medical devices to smart cars. These objects collect, transmit and process data continuously, often without a suitable security infrastructure. Homomorphic encryption would provide a robust solution to protect the data collected at the source. This would help counter the growing risks of cyberattacks on connected objects, where each vulnerability can potentially be exploited on a large scale.
Professor Mohamed BAKHOUYA is a professor of computer science at the International University of Rabat. He obtained his HDR from UHA-France in 2013 and his PhD from UTBM-France in 2005. He has more than ten years experiences in participating and working in sponsored ICT projects. He was EiC of IJARAS journal and also serves as a guest editor of a number of international journals, e.g.,ACM Trans. on Autonomous and Adaptive Systems, Product Development Journal, Concurrency and Computation: Practice and Experience, FGCS, and MICRO. He has published several papers in international journals, books, and conferences. His research interests include various aspects related to the design, validation, and implementation of distributed and adaptive systems, architectures, and protocols. Title: Blockchain-based IoT Platforms for smart Farming Systems Abstract: Recent advances in pervasive technologies, such as wireless ad hoc networks and wearable sensor devices, allow the connection of everyday things to the Internet, commonly denoted as Internet of Things (IoT). IoT is seen as an enabler to the development of intelligent and context-aware services and applications. These services could dynamically react to the environment changes and users’ preferences. The main aim is to make users’ life more comfortable according to their locations, current requirements, and on-going activities. However, handling dynamic and frequent context changes is a difficult task without a real-time event/data acquisition and processing platform. Big data, WSN, and IoT technologies have been recently proposed for timely gathering and analysing information (i.e., data, events) streams. In this talk, we shed more light on the potential of these technologies for continuous and real-time data monitoring and processing in different real-case applications (e.g, Healthcare, energy efficient building, smart grid).
Dr. Hamed Taherdoost is faculty member of University Canada West. He holds PhD of Computer Science and Master of Information Security. He has over 20 years of experience in both industry and academic sectors. He has worked at international companies from Cyprus, the UK, Malta, Iran, Malaysia, and Canada and has been highly involved in development of several projects in different industries including oil and gas, healthcare, transportation, and information technology, holding positions as varied as Project Manager, R&D Manager, Tech Lead, and CTO. He has spent the last nine years helping start-ups to grow by implementing new projects and business lines. Apart from his experience in industry, he also has some achievements in academia. He has been a lecturer in three different parts of the world, Southeast Asia, the Middle East, and North America. Besides, he has organized and chaired numerous workshops, conferences, and conference sessions respectively, and has delivered speeches as chief guest and keynote speaker. Moreover, he is the editorial, reviewer, and advisory board member of some authentic peer-reviewed journals publishing with Taylor & Francis, Springer, Emerald, Elsevier, MDPI, EAI, & IGI Publishing, and Inderscience. Hamed has been an active multidisciplinary researcher and R&D specialist involved in several academic and industrial research projects. He has been working with researchers from various disciplines and has been actively engaged in different research studies. His research achievements also include winning several best paper awards and outstanding reviewer awards. His views on science and technology have been published in top-ranked scientific publishers such as Elsevier, Springer, Emerald, IEEE, IGI Global, Inderscience, Taylor and Francis and Dr. Hamed has published over 160 scientific articles in authentic peer-reviewed international journals and conference proceedings (h-index = 31; i10-index = 52; May 2022), ten book chapters as well as eight books in the field of technology and research methodology. He was the finalist for the Innovation in Teaching of Research Methodology Excellence Awards at ECRM, UK in 2022 and was nominated as the finalists in Southeast Asian Startup Awards in 2020 by Global Startup Awards and has been listed on the Stanford-Elsevier list of World’s top 2% of scientist by August 2021. He is a Certified Cyber Security Professional and Certified Graduate Technologist. He is senior member of IEEE, IAEEEE, IASED, and IEDRC and WGM of IFIP TC11 - Assurance and Information Security Management, and member of CSIAC), ACT-IAC, and AASHE. Currently, he is involved in several multidisciplinary research projects, including studying innovation in information technology, blockchain and cybersecurity, and technology acceptance. Title: Threat Intelligence and Machine Learning: A Powerful Combination for Cybersecurity Abstract: Machine learning and threat intelligence combined provide a potent cybersecurity tool. While unstructured data can be analyzed with machine learning, threat intelligence entails gathering and evaluating data to foresee new assaults. Additionally, risk exposure assessment, alert management, raw data analysis, and cyber threat intelligence can all benefit from machine learning. It is imperative that each customer concentrates on the threat landscape that pertains to them, as the majority of the threat landscape is unimportant to most firms. By automatically generating a customized threat profile and making it easier for analysts to enrich that threat profile by providing them with AI-based natural language processing capabilities, the threat environment may be made more personalized. The question of whether open-source intelligence can be successfully incorporated into a practical method that reliably categorizes cyber threat intelligence can also be answered using machine learning. Machine learning and rule-based algorithms are used in the processing pipeline of the threat intelligence machine to convert unstructured data from open, technical sources into organized, useful intelligence. To strengthen cybersecurity, machine learning can also be utilized to visualize trends in CTI data. In summary, this speech discusses how threat intelligence and machine learning together can offer a strong basis for artificial intelligence (AI) solutions that can safeguard companies from online attacks.
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