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6. Secure MAC protocols for cognitive radio networks

Author

Alhakami, Wajdi

Subjects
621.384, secure MAC, protocols, radio networks, cognitive radio
Abstract

With the rapid increase in wireless devices, an effective improvement in the demand of efficient spectrum utilisation for gaining better connectivity is needed. Cognitive Radio (CR) is an emerging technology that exploits the inefficient utilisation of the unused spectrum dynamically. Since spectrum sharing is responsible for coordinating channels’ access for Cognitive Users (CUs), the Common Control Channel (CCC) is one of the existing methods used to exchange the control information between CUs. However, the unique characteristics and parameters of Cognitive Radio Networks (CRNs) present several possible threats targeting spectrum sensing, spectrum management, spectrum sharing, and spectrum mobility leading to the deterioration of the network performance. Thus, protection and detection security mechanisms are essential to maintaining the CRNs. This thesis presents a novel decentralised CR MAC protocol that successfully utilises the unused portion of the licensed band. The protocol achieves improved performance; communication time and throughput when compared to two benchmark protocols. Less communication time and higher throughput are accomplished by the protocol due to performing fast switching to the selected available data channel for initiating data transmission. The proposed protocol is then extended to two different versions based on two authentication approaches applied to it; one using Digital Signature and another is based on Shared-Key. The two proposed secure protocols address the security requirements in CRNs leading to subsequent secure communication among CUs. The protocols function effectively in providing defence against several attacks related to the MAC layer such as; Spectrum Sensing Data Manipulation/Falsification, Data Tempering and Modification, Jamming attacks, Eavesdropping, Forgery and Fake control information attacks, MAC address spoofing, and unauthorised access attacks. The associated security algorithms ensure the successful secure communication between CUs in a cooperative approach. Moreover, the security protocols are investigated and analysed in terms of security flows by launching unauthorised access and modification attacks on the transmitted information. The testing results demonstrated that two protocols perform successful detection of threats and ensure secure communication in CRNs.

Published
2016

11. Inpainting forgery detection using hybrid generative/discriminative approach based on bounded generalized Gaussian mixture model.

Author

Alharbi, Abdullah, Alhakami, Wajdi, Bourouis, Sami, Najar, Fatma, and Bouguila, Nizar

Subjects
GAUSSIAN mixture models, FORGERY, INPAINTING, SUPPORT vector machines, DIGITAL images
Abstract

We propose in this paper a novel reliable detection method to recognize forged inpainting images. Detecting potential forgeries and authenticating the content of digital images is extremely challenging and important for many applications. The proposed approach involves developing new probabilistic support vector machines (SVMs) kernels from a flexible generative statistical model named "bounded generalized Gaussian mixture model". The developed learning framework has the advantage to combine properly the benefits of both discriminative and generative models and to include prior knowledge about the nature of data. It can effectively recognize if an image is a tampered one and also to identify both forged and authentic images. The obtained results confirmed that the developed framework has good performance under numerous inpainted images. [ABSTRACT FROM AUTHOR]

Published
2024
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25. SVM‐based generative adverserial networks for federated learning and edge computing attack model and outpoising.

Author

Manoharan, Poongodi, Walia, Ranjan, Iwendi, Celestine, Ahanger, Tariq Ahamed, Suganthi, S. T., Kamruzzaman, M. M., Bourouis, Sami, Alhakami, Wajdi, and Hamdi, Mounir

Subjects
MACHINE learning, DEEP learning, EDGE computing, BILEVEL programming, GENERATIVE adversarial networks, POISONS
Abstract

Machine learning are vulnerable to the threats. The Intruders can utilize the malicious nature of the nodes to attack the training dataset to worsen the process and manipulate the learning and make the over all system with less efficiency and performance. The optimized poison attack procedures are already introduced to estimate the overall bad scenario, design the intrusion as bi‐level optimization and it is considered computational complexity is high and demanding, in contrary the applicability is limited such models deep neural networks. In this research papers, we have proposed, novel proposed system, poisoning attacks against the Machine learning training dataset, including the genuine data points that reduce the accuracy of the classifier in the process of training. The proposed system have 3 components of Generative Adverserial networks (GAN) generator, discriminator, and the target classifier. The proposed system allows to detect the vulnerability easy and it can be found as similar as realistic attacks to detect the area where the underlying data distribution have more possibility of poising attack which cause vulnerability to the network. Our experimentation, proves the claim our that the proposed model is effective on compromising the classifiers uses the machine learning algorithms and also deep learning networks. [ABSTRACT FROM AUTHOR]

Published
2023
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33. A Generic Internet of Things (IoT) Middleware for Smart City Applications.

Author

Ali, Zulfiqar, Mahmood, Azhar, Khatoon, Shaheen, Alhakami, Wajdi, Ullah, Syed Sajid, Iqbal, Jawaid, and Hussain, Saddam

Abstract

The Internet of Things (IoT) is one of the key components of the ICT infrastructure of smart cities due to its great potential for intelligent management of infrastructures and facilities and the enhanced delivery of services in support of sustainable cities. Smart cities typically rely on IoT, where a wide variety of devices communicate with each other and collaborate across heterogeneous and distributed computing environments to provide information and services to urban entities and urbanites. However, leveraging the IoT within software applications raises tremendous challenges, such as data acquisition, device heterogeneity, service management, security and privacy, interoperability, scalability, flexibility, data processing, and visualization. Middleware for IoT has been recognized as the system that can provide the necessary infrastructure of services and has become increasingly important for IoT over the last few years. This study aims to review and synthesize the relevant literature to identify and discuss the core challenges of existing IoT middleware. Furthermore, it augments the information landscape of IoT middleware with big data applications to achieve the required level of services supporting sustainable cities. In doing so, it proposes a novel IoT middleware for smart city applications, namely Generic Middleware for Smart City Applications (GMSCA), which brings together many studies to further capture and invigorate the application demand for sustainable solutions which IoT and big data can offer. The proposed middleware is implemented, and its feasibility is assessed by developing three applications addressing various scenarios. Finally, the GMSCA is tested by conducting load balance and performance tests. The results prove the excellent functioning and usability of the GMSCA. [ABSTRACT FROM AUTHOR]

Published
2023
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39. A New SLM-UFMC Model for Universal Filtered Multi-Carrier to Reduce Cubic Metric and Peak to Average Power Ratio in 5G Technology

Author

Shawqi, Farooq Sijal, primary, Audah, Lukman, additional, Mostafa, Salama A., additional, Gunasekaran, Saraswathy Shamini, additional, Baz, Abdullah, additional, Hammoodi, Ahmed Talaat, additional, Alhakami, Hosam, additional, Hassan, Mustafa Hamid, additional, Jubair, Mohammed Ahmed, additional, and Alhakami, Wajdi, additional

Published
2020
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44. Benchmarking Methodology for Selection of Optimal COVID-19 Diagnostic Model Based on Entropy and TOPSIS Methods

Author

Mohammed, Mazin Abed, primary, Abdulkareem, Karrar Hameed, additional, Al-Waisy, Alaa S., additional, Mostafa, Salama A., additional, Al-Fahdawi, Shumoos, additional, Dinar, Ahmed Musa, additional, Alhakami, Wajdi, additional, Baz, Abdullah, additional, Al-Mhiqani, Mohammed Nasser, additional, Alhakami, Hosam, additional, Arbaiy, Nureize, additional, Maashi, Mashael S., additional, Mutlag, Ammar Awad, additional, Garcia-Zapirain, Begona, additional, and De La Torre Diez, Isabel, additional

Published
2020
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