Visible to the public Biblio

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Al Hussien, Nedaa, Barka, Ezedin, Abdel-Hafez, Mohammed, Shuaib, Khaled.  2016.  Secure Spectrum Sensing in Cognitive-Radio-Based Smart Grid Using Role-Based Delegation. Proceedings of the 2016 8th International Conference on Information Management and Engineering. :25–29.

As smart grid becomes more popular and emergent, the need for reliable communication technology becomes crucial to ensure the proper and efficient operation of the grid. Therefore, cognitive radio has been recently utilized to provide a scalable and reliable communication infrastructure for smart grid. However, accurate spectrum sensing is the core of this infrastructure. In this paper, we propose an architecture, utilizing Role-Based Delegation to manage spectrum sensing within the cognitive-radio-based communication infrastructure for smart grid and ensure its reliability and security.

Khayat, Mohamad, Barka, Ezedin, Sallabi, Farag.  2019.  SDN\_Based Secure Healthcare Monitoring System(SDN-SHMS). 2019 28th International Conference on Computer Communication and Networks (ICCCN). :1–7.
Healthcare experts and researchers have been promoting the need for IoT-based remote health monitoring systems that take care of the health of elderly people. However, such systems may generate large amounts of data, which makes the security and privacy of such data to become imperative. This paper studies the security and privacy concerns of the existing Healthcare Monitoring System (HMS) and proposes a reference architecture (security integration framework) for managing IoT-based healthcare monitoring systems that ensures security, privacy, and reliable service delivery for patients and elderly people to reduce and avoid health related risks. Our proposed framework will be in the form of state-of-the-art Security Platform, for HMS, using the emerging Software Defined Network (SDN) networking paradigm. Our proposed integration framework eliminates the dependency on specific Software or vendor for different security systems, and allows for the benefits from the functional and secure applications, and services provided by the SDN platform.
Mohammed, Saif Saad, Hussain, Rasheed, Senko, Oleg, Bimaganbetov, Bagdat, Lee, JooYoung, Hussain, Fatima, Kerrache, Chaker Abdelaziz, Barka, Ezedin, Alam Bhuiyan, Md Zakirul.  2018.  A New Machine Learning-based Collaborative DDoS Mitigation Mechanism in Software-Defined Network. 2018 14th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob). :1–8.
Software Defined Network (SDN) is a revolutionary idea to realize software-driven network with the separation of control and data planes. In essence, SDN addresses the problems faced by the traditional network architecture; however, it may as well expose the network to new attacks. Among other attacks, distributed denial of service (DDoS) attacks are hard to contain in such software-based networks. Existing DDoS mitigation techniques either lack in performance or jeopardize the accuracy of the attack detection. To fill the voids, we propose in this paper a machine learning-based DDoS mitigation technique for SDN. First, we create a model for DDoS detection in SDN using NSL-KDD dataset and then after training the model on this dataset, we use real DDoS attacks to assess our proposed model. Obtained results show that the proposed technique equates favorably to the current techniques with increased performance and accuracy.