S. Sezer

Publications253
h-index33
Citations5,367
Highly Influential Citations290
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Are we ready for SDN? Implementation challenges for software-defined networks
TLDR
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.
Evolution of ransomware
TLDR
This study examines the pathway from the first clumsy ransomware attempts to the present day sophisticated ransomware attack campaigns and argues that this low-impact extortion, using highly automated methods, has proven very rewarding for the criminals.
Deep Android Malware Detection
TLDR
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.
Sdn Security: A Survey
TLDR
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.
A Multimodal Deep Learning Method for Android Malware Detection Using Various Features
TLDR
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.
STPA-SafeSec: Safety and security analysis for cyber-physical systems
OperationCheckpoint: SDN Application Control
TLDR
This paper presents an approach to secure the northbound interface by introducing a permissions system that ensures that controller operations are available to trusted applications only and implementation of this permissions system with Operation Checkpoint adds negligible overhead and illustrates successful defense against unauthorized control function access attempts.
High accuracy android malware detection using ensemble learning
TLDR
Experimental results and analysis presented shows that the proposed method which uses a large feature space to leverage the power of ensemble learning is capable of 97.3-99% detection accuracy with very low false positive rates.
A Survey of Security in Software Defined Networks
TLDR
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.
DL-Droid: Deep learning based android malware detection using real devices
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