Browsing by Author "Alzoubi, Haitham M."
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Item A Roadmap for SMEs to Adopt an AI Based Cyber Threat Intelligence(Springer Science and Business Media Deutschland GmbH, 2023) Varma, Abhilash J.; Taleb, Nasser; Said, Raed A.; Ghazal, Taher M.; Ahmad, Munir; Alzoubi, Haitham M.; Alshurideh, MuhammadCybersecurity has started to become the most significant concern among organizations as the number of threats and criminal activities in the past decade has increased exponentially. Cybercriminals and their attacking techniques have become increasingly sophisticated over the past couple of years. Conventional security measures will no longer be able to detect and mitigate the propagation of such advanced attacking trends. More and more hackers have started focusing on Small and medium-sized enterprises (SMEs) taking advantage of their limited resources. Therefore, SMEs will have to quickly adopt Artificial Intelligence (AI) based cybersecurity system in their infrastructure to defend themselves effectively and efficiently. It is currently forecasted that by 2021, 75% of all organizations will use AI and Machine learning (ML) applications in their security architecture to protect against all cyber threats. In this paper, the researchers identify the various challenges faced by SMEs in adopting an AI based cybersecurity due to their knowledge gap and lack of expertise. The researcher intends to provide a good background on AI, Cyber Threat Intelligence (CTI) and highlight some of the significant benefits provided by an AI based CTI system. A simple roadmap is developed using a qualitative research methodology to help SMEs effectively implement an AI based Cyber Threat Intelligent system in their infrastructure. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.Item An Integrated Cloud and Blockchain Enabled Platforms for Biomedical Research(Springer Science and Business Media Deutschland GmbH, 2023) Ghazal, Taher M.; Hasan, Mohammad Kamrul; Abdullah, Siti Norul Huda Sheikh; Bakar, Khairul Azmi Abu; Taleb, Nasser; Al-Dmour, Nidal A.; Yafi, Eiad; Chauhan, Ritu; Alzoubi, Haitham M.; Alshurideh, MuhammadIn the current pandemic scenario, healthcare data tends to be an important asset among organizations. The major challenge is to handle the data effectively while maintaining the privacy and security of the data. In a real-world, context healthcare data proves to be heterogeneous. Hence, managing such significance to big data has ardently laid numerous challenges among researchers and scientists around the globe. Cloud environment and blockchain technology can be discussed as usable platforms which can deliver a comprehensive centralized data privacy system. In the current approach study, we have integrated both technologies to provide usability in medical systems. Further, we have also proposed and implemented a blockchain application with an integrated cloud-based environment regarding heterogeneous medical databases. The study is proposed in 2 phases to maintain the privacy and the accessibility of the data. The double-spending problem is also presented, as mentioned above, using Blockchain’s consensus process. Each network node independently verifies the validity of individual transactions and entire blocks. As a result, there is no need to put faith in a single entity or other nodes. As a result, third parties are no longer required for network actions or blockchain management. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.Item Analysis of Issues Affecting IoT, AI, and Blockchain Convergence(Springer Science and Business Media Deutschland GmbH, 2023) Taleb, Nasser; Al-Dmour, Nidal A.; Issa, Ghassan F.; Abdellatif, Tamer Mohamed; Alzoubi, Haitham M.; Alshurideh, Muhammad; Salahat, MohammedThe purpose of this project was to appraise the integration or convergence issues influencing the mutual functioning of blockchain, AI, and IoT. The study argued that the recent developments in the field of IoT and blockchain prediction have involved the integration of innumerable classification schemes to establish a hybrid model. The introduction of the hybrid technique relies on the prediction performance that strives to override the limitations of any available architectural scheme. This study offers a comprehensive exploratory appraisal of the issues influencing the successful integration of IoT and blockchain in regards to functionality and effectiveness of security, trust, and flawless communication issues. The exploratory research methodology was used in analyzing the issues affecting the integration of blockchain, artificial intelligence (AI), and the internet of things (IoT). The findings indicated that the integration challenges influencing the effective operations of blockchain, AI, and IoT as a single system involve security, scalability, accountability, and trust of communications. The study recommends that successful and effective integration will enhance the development of new business models as well as the digital transformation of market corporations. Accordingly, new approaches to convergence should ensure that executives address the new technology demands to obtain significant gains in efficiency. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.Item Breast Cancer Prediction Using Machine Learning and Image Processing Optimization(Springer Science and Business Media Deutschland GmbH, 2023) Al-Dmour, Nidal A.; Said, Raed A.; Alzoubi, Haitham M.; Alshurideh, Muhammad; Ali, LiaqatItem Development of Data Mining Expert System Using Naïve Bayes(Springer Science and Business Media Deutschland GmbH, 2023) Salahat, Mohammed; Al-Dmour, Nidal A.; Said, Raed A.; Alzoubi, Haitham M.; Alshurideh, MuhammadThe consumer spectrum consists of a wide range, including the affluent, middle-income, and low-income. This consumer shows different behaviors or motivations towards choosing clothes. We want to develop a framework for a Sale Recommendation System. These expert System can be helpful for sale persons, fashion designer, promoter, brand manager as well as sponsor of Recommendation System. The study implemented the Data Science approach and techniques to see how reliable Recommendation Systems are and in our selected dataset we have applied different modelling techniques such as KNN, SVM, Bayes Naïve and Decision Tree and fond the NB as the most suitable and practical method of modelling in regard to the accuracy, recall and runtime. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.Item Development of Data Mining Framework Cardiovascular Disease Prediction(Springer Science and Business Media Deutschland GmbH, 2023) Said, Raed A.; Al-Dmour, Nidal A.; Salahat, Mohammed; Issa, Ghassan F.; Alzoubi, Haitham M.; Alshurideh, MuhammadOne of the highest shares of data-driven technology of health sector happens for private insurance stakeholders. It is therefore clear that private insurance companies can only survive being competitive in covering different medical stages such as surgery, intervention and other clinical trials in a high-risk environment. Estimation of expected costs and coverage is also important for both patient and insurer. In this case study we as a Data Mining and Artificial Business consultant want to explore different techniques of data mining to find out business risks for patients. We have asked the insurer to provide us a sizable medical history to watch those features. We would like to predict if given biographical profile of the patient along with exam results can predict CVD so he can cover his costs with this Insurer. On the other hand, in case of higher error of misclassified CVD what kind of decision should be taken by risk holder and insurer. Which one of these attributes causing this cost and what other stakeholders like target group of patients can be suffered from the loss? The ultimate goal is to develop a model that can predict the gap between those patients’ perception of their disease and their real disease. This can further help stakeholders to develop specific insurance policy. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.Item Information Systems Solutions for the Database Problems(Springer Science and Business Media Deutschland GmbH, 2023) Al-Dmour, Nidal A.; Ali, Liaqat; Salahat, Mohammed; Alzoubi, Haitham M.; Alshurideh, Muhammad; Chabani, ZakariyaItem IT Governance and Control: Mitigation and Disaster Preparedness of Organizations in the UAE(Springer Science and Business Media Deutschland GmbH, 2023) Al Blooshi, Ismail Ali; Alamim, Abdulazez Salem; Said, Raed A.; Taleb, Nasser; Ghazal, Taher M.; Ahmad, Munir; Alzoubi, Haitham M.; Alshurideh, MuhammadItem Linear Discrimination Analysis Using Image Processing Optimization(Springer Science and Business Media Deutschland GmbH, 2023) Said, Raed A.; Al-Dmour, Nidal A.; Ali, Liaqat; Alzoubi, Haitham M.; Alshurideh, Muhammad; Salahat, MohammedWhen we talk about Machinery Vision and Deep Learning, we often talk about algorithms. In fact, mathematical models with computer knowledge are the basis of how we deal with graphical data to process the Image and make decision. Machine learning can play an important role in determining agricultural plant type in order to optimize the harvesting steps in an automated way. How to process and introduce the products to the market often requires detailed information about the stages of planting and harvesting. In addition, by using this method, sophisticated research can be designed in plant genetics and effect of environmental variables on the end product. The ultimate goal of this work is to use Linear Discrimination Analysis for the Image Processing and classification of harvested wheat grain which are belonged to different types of grain namely Rosa, Kama and Canadian. The above discovery has proved with the statistics to have with more than 94% of accuracy. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.Item Machine Learning-Based Intrusion Detection Approaches for Secured Internet of Things(Springer Science and Business Media Deutschland GmbH, 2023) Ghazal, Taher M.; Hasan, Mohammad Kamrul; Abdullah, Siti Norul Huda Sheikh; Bakar, Khairul Azmi Abu; Al-Dmour, Nidal A.; Said, Raed A.; Abdellatif, Tamer Mohamed; Moubayed, Abdallah; Alzoubi, Haitham M.; Alshurideh, Muhammad; Alomoush, WaleedNowadays, protecting communication and information for Internet of Things (IOT) has emerged as a critical challenge. Existing systems use firewalls to ensure that they are safe from any unexpected occurrences that may disrupt the desired systems and applications. Intrusion detection systems (IDSs) are an acceptable second line of defence for IOT applications. IDS play a crucial role ensuring that it enhances the IOT security level maintaining sophisticated framework. Attackers have continuously been attempting to determine novel ways to circumnavigate security frameworks that prevent the structures. This paper reviews the security advances, threats and countermeasures for the IOT applications. A state of art review has accomplished using the references from 2009 to 2020 to encompass the real demography of the IOT security research data. This work also highlights the deep learning-based intrusion detection approaches for Internet of Things (IOT) security. With the systematic literature review approach, the review suggests that implementing existing security measures, such as encryption, authentication, access control, network and application security for IoT systems and their intrinsic amenability is ineffective for the IOT systems. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.