R
Reza Javidan
Researcher at Shiraz University of Technology
Publications - 122
Citations - 1784
Reza Javidan is an academic researcher from Shiraz University of Technology. The author has contributed to research in topics: Wireless sensor network & Computer science. The author has an hindex of 16, co-authored 112 publications receiving 1114 citations. Previous affiliations of Reza Javidan include Islamic Azad University & Malek-Ashtar University of Technology.
Papers
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A Two-Layer Dimension Reduction and Two-Tier Classification Model for Anomaly-Based Intrusion Detection in IoT Backbone Networks
TL;DR: A novel model for intrusion detection based on two-layer dimension reduction and two-tier classification module, designed to detect malicious activities such as User to Root (U2R) and Remote to Local (R2L) attacks is presented.
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SLICOTS: An SDN-Based Lightweight Countermeasure for TCP SYN Flooding Attacks
TL;DR: SLICOTS takes the advantage of dynamic programmability nature of SDN to detect and prevent attacks, and reduces the response time overhead up to some 50%, while ensuring the same level of protection.
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Similarity-based Android Malware Detection Using Hamming Distance of Static Binary Features
Rahim Taheri,Meysam Ghahramani,Reza Javidan,Mohammad Shojafar,Mohammad Shojafar,Zahra Pooranian,Mauro Conti +6 more
TL;DR: Four malware detection methods using Hamming distance to find similarity between samples which are first nearest neighbor (FNN), all nearest neighbors (ANN), weighted all nearestNeighbors (WANN), and k-medoid based nearestNeighborhood (KMNN) are developed.
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A Hybrid Data Mining Approach for Intrusion Detection on Imbalanced NSL-KDD Dataset
TL;DR: This hybrid approach is a combination of synthetic minority oversampling technique (SMOTE) and cluster center and nearest neighbor (CANN) and improves the accuracy of detecting U2R and R2L attacks in comparison to the baseline paper by 94% and 50%, respectively.
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Medical image registration using deep neural networks: A comprehensive review
TL;DR: A comprehensive review on the state-of-the-art literature known as medical image registration using deep neural networks is presented, which allows a deep understanding and insight for the readers active in the field who are investigating the state of theart and seeking to contribute the future literature.