Yazar "Mahmood, Mohammed Thakir" seçeneğine göre listele

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    A survey on privacy and policy aspects of blockchain technology
    (IEEE, 2022) Mahmood, Mohammed Thakir; Uçan, Osman Nuri; Ibrahim, Abdullahi Abdu
    From financial transactions to digital voting systems, identity management, and asset monitoring, blockchain technology is increasingly being developed for use in a wide range of applications. The problem of security and privacy in the blockchain ecosystem, which is now a hot topic in the blockchain community, is discussed in this study. The survey’s goal was to investigate this issue by considering several sorts of assaults on the blockchain network in relation to the algorithms offered. Following a preliminary literature assessment, it appears that some attention has been paid to the first use case; however the second use case, to the best of my knowledge, deserves more attention when blockchain is used to investigate it. However, due to the subsequent government mandated secrecy around the implementation of DES, and the distrust of the academic community because of this, a movement was spawned that put a premium on individual privacy and decentralized control. This movement brought together the top minds in encryption and spawned the technology we know of as blockchain today. This survey paper also explores the genesis of encryption, its early adoption, and the government meddling which eventually spawned a movement which gave birth to the ideas behind blockchain. It also closes with a demonstration of blockchain technology used in a novel way to refactor the traditional design paradigms of databases.
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    Detection of vehicle with Infrared images in road traffic using YOLO computational mechanism
    (IOP Publishing Ltd, 2020) Mahmood, Mohammed Thakir; Ahmed, Saadaldeen Rashid Ahmed; Ahmed, Mohammad Rashid Ahmed
    Vehicle counting is an important process in the estimation of road traffic density to evaluate the traffic conditions in intelligent transportation systems. With increased use of cameras in urban centers and transportation systems, surveillance videos have become central sources of data. Vehicle detection is one of the essential uses of object detection in intelligent transport systems. Object detection aims at extracting certain vehicle-related information from videos and pictures containing vehicles. This form of information collection in intelligent systems is faced with low detection accuracy, inaccuracy in vehicle type detection, slow processing speeds. In this research, we propose a vehicle detection system from infrared images using YOLO (You Look Only Once) computational mechanism. The YOLO mechanism can apply different machine or deep learning algorithms for accurate vehicle type detection. In this study we propose an infrared based technique to combine with YOLO for vehicle detection in traffic. This method will be compared with a machine learning technique of K-means++ clustering algorithm, a deep learning mechanism of multitarget detection and infrared imagery using convolutional neutral network © Published under licence by IOP Publishing Ltd.
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    Securing 5G network using low power wireless personal area network
    (Institute of Electrical and Electronics Engineers Inc., 2020) Al-Sarray, Zaid Ali; Mahmood, Mohammed Thakir; Ibrahim, Abdullahi Abdu
    The purpose of this work was to get acquainted with wireless communication technologies and the information security challenges they create from the viewpoint of 5G and its preceding technologies. 5th Generation (5G) is becoming a global phenomenon and it is currently being implemented in dozens of countries around the globe with it comes new information security challenges. Potential solutions for the challenges are also offered. The outcome of this research is an overview of information security challenges in 5G using Low-power wireless personal area Network (LPWAN) and in the technologies preceding 5G. Possible information security solutions are presented in this work for the new technologies coming with 5G. This work showed that the new technologies coming with 5G, such as the virtualization of hardware and services as well as the utilization of cloud computing, create completely new areas of attack for networks. With this knowledge, Labelled and Freely Available Dataset from Open-Source Repository will be used and it is possible to prevent attacks targeting networks by implementing necessary information security elements. For the training, testing and validation of our dataset which is an IoT and cyber-security based dataset, a well-known MATLAB R2019a software was used for this purpose. The proposed reinforcement learning algorithm for Securing 5G network is designed for mesh topology from the ground up by the model of the network itself using low power personal area networks. We model the network operating in a finite area with a finite number of nodes distributed inside the area randomly in this algorithm. Hence, we defined the service area of the target network by assuming the finiteness of the network in the model. © 2020 IEEE.