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Browsing International Business by Author "Abdellatif, Tamer Mohamed"
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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 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.