S
Sakir Sezer
Researcher at Queen's University Belfast
Publications - 242
Citations - 7280
Sakir Sezer is an academic researcher from Queen's University Belfast. The author has contributed to research in topics: Malware & Smart grid. The author has an hindex of 33, co-authored 236 publications receiving 5655 citations. Previous affiliations of Sakir Sezer include Queen's University.
Papers
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Journal ArticleDOI
Are we ready for SDN? Implementation challenges for software-defined networks
Sakir Sezer,Sandra Scott-Hayward,Pushpinder Kaur Chouhan,B. Fraser,D. Lake,J. Finnegan,Nicolaas J. Viljoen,M. Miller,N. Rao +8 more
TL;DR: The question of how to achieve a successful carrier grade network with software-defined networking is raised and specific focus is placed on the challenges of network performance, scalability, security, and interoperability with the proposal of potential solution directions.
Proceedings ArticleDOI
Sdn Security: A Survey
TL;DR: This paper presents a comprehensive survey of the research relating to security in software-defined networking that has been carried out to date, and both the security enhancements to be derived from using the SDN framework and the security challenges introduced by the framework are discussed.
Journal ArticleDOI
A Survey of Security in Software Defined Networks
TL;DR: The challenges to securing the network from the persistent attacker are discussed, and the holistic approach to the security architecture that is required for SDN is described.
Proceedings ArticleDOI
Deep Android Malware Detection
Niall McLaughlin,Jesus Martinez del Rincon,BooJoong Kang,Suleiman Y. Yerima,Paul Miller,Sakir Sezer,Yeganeh Safaei,Erik Trickel,Ziming Zhao,Adam Doupé,Gail-Joon Ahn +10 more
TL;DR: A novel android malware detection system that uses a deep convolutional neural network (CNN) to perform static analysis of the raw opcode sequence from a disassembled program, removing the need for hand-engineered malware features.
Journal ArticleDOI
A Multimodal Deep Learning Method for Android Malware Detection Using Various Features
TL;DR: This paper is the first study of the multimodal deep learning to be used in the android malware detection, and compared the performance of the framework with those of other existing methods including deep learning-based methods.