Browsing by Author "Abdellatif, Tamer Mohamed"
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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 Cyber Security Strategies While Safeguarding Information Systems in Public/Private Sectors(Springer Science and Business Media Deutschland GmbH, 2022) Al Mehairi, Alya; Zgheib, Rita; Abdellatif, Tamer Mohamed; Conchon, EmmanuelMany private and public organizations in the UAE and around the world are facing challenges in protecting their information and systems from external cyber-attacks due to the increase in the usage of computer networks within worldwide businesses. The objective of this research study is to explore the strategies that are implemented by the public and private sectors in the UAE to safeguard their data and information systems from cyber-attacks. The findings of the study indicated that public organizations in the UAE do have effective strategies in place to safeguard their information and systems against any cyber-attacks. These strategies include providing adequate training to their employees to create awareness among them and developing robust cyber security strategies in line with the UAE National Cyber Security strategy framework. Public and some private organizations are vigilant in assessing, identifying, and mitigating cyber security risks and threats through well-designed organizational strategies. The research also concludes that protecting the information system can reduce cyber threats and can lead to improved business practices. The findings of this study will lay the foundations for other private and public sectors to use them in their organizational practices, which will help them to decrease the data breaches, and protect their company and customers’ confidential data, thereby reducing the cost and risk of cyber-attacks. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.Item DDoS Intrusion Detection with Ensemble Stream Mining for IoT Smart Sensing Devices(Springer Science and Business Media Deutschland GmbH, 2023) Ghazal, Taher M.; Al-Dmour, Nidal A.; Said, Raed A.; Omidvar, Alireza; Khan, Urooj Yousuf; Soomro, Tariq Rahim; Soomro, Tariq Rahim; Alshurideh, Muhammad; Abdellatif, Tamer Mohamed; Moubayed, Abdullah; Ali, LiaqatSecurity threats in the Smart City Systems are becoming a challenge. These Smart City Systems, generating Big Data, are a revolutionizing application of the Internet of Things(IoT). Data Stream Mining, which is an efficient way of handling Big Data, is now of great concern. The acquired information is computationally expensive to process in terms of efficiency and runtime. Detection of suspicious activities on decentralized servers, generating and computing massive data streams requires time. Moreover, several stakeholders should be engaged to train the heterogenous malware data streams in the level of service application. Small experiments can be performed on the functionality of Batch ML on IoT datasets with available heap size resources. Among these candidate datasets, a little contribution has been already represented on the Mirai Attack. This research aims at the study of Data Stream Mining algorithms. Owing to the accuracy and interferences of the measurement, these algorithms are able to handle the non-hierarchical and unbalanced datasets similar to the Mirai Attacks. No single method can solely improve these critical standpoints. Thus, an Ensemble technique should be implemented. According to our study, a pool of meta or selective classifiers that interact based on the temporal Data Mining swiftly can outperform others. The maintainability and security concerns of such applications can be best fulfilled in meta-heuristics with the one-time scanning network approach for the recognition of the most frequent attacking pattern with the on-the-fly scheme. These are implemented in Create, Read, Update and Delete (CRUD) operations of the Big Data Systems. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.Item Impact and Research Challenges of Penetrating Testing and Vulnerability Assessment on Network Threat(Institute of Electrical and Electronics Engineers Inc., 2023) Fatima, Areej; Khan, Tahir Abbas; Abdellatif, Tamer Mohamed; Zulfiqar, Sidra; Asif, Muhammad; Safi, Waseem; Hamadi, Hussam Al; Al-Kassem, Amer HaniItem 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.Item Understanding Dark Web: A Systematic Literature Review(Institute of Electrical and Electronics Engineers Inc., 2022) Abdellatif, Tamer Mohamed; Said, Raed A.; Ghazal, Taher M.Item Vehicle's Big Brother: IoT-Based Monitoring System for In-car Safety(Institute of Electrical and Electronics Engineers Inc., 2022) Satrya, G. Bayu; Ferdinand, Yulio; Elnaffar, Said; Putri Alamsyah N.C.; Abdellatif, Tamer Mohamed