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Discriminant Metric Learning Approach for Face Verification
( Ju-chin Chen ),( Pei-hsun Wu ),( Jenn-jier James Lien ) 한국인터넷정보학회 2015 KSII Transactions on Internet and Information Syst Vol.9 No.2
In this study, we propose a distance metric learning approach called discriminant metric learning (DML) for face verification, which addresses a binary-class problem for classifying whether or not two input images are of the same subject. The critical issue for solving this problem is determining the method to be used for measuring the distance between two images. Among various methods, the large margin nearest neighbor (LMNN) method is a state-of-the-art algorithm. However, to compensate the LMNN`s entangled data distribution due to high levels of appearance variations in unconstrained environments, DML`s goal is to penalize violations of the negative pair distance relationship, i.e., the images with different labels, while being integrated with LMNN to model the distance relation between positive pairs, i.e., the images with the same label. The likelihoods of the input images, estimated using DML and LMNN metrics, are then weighted and combined for further analysis. Additionally, rather than using the k-nearest neighbor (k-NN) classification mechanism, we propose a verification mechanism that measures the correlation of the class label distribution of neighbors to reduce the false negative rate of positive pairs. From the experimental results, we see that DML can modify the relation of negative pairs in the original LMNN space and compensate for LMNN`s performance on faces with large variances, such as pose and expression.
Mitigating SYN flooding Attack and ARP Spoofing in SDN Data Plane
Ting-Yu Lin,Jhen-Ping Wu,Pei-Hsuan Hung,Ching-Hsuan Shao,Yu-Ting Wang,Yun-Zhan Cai,Meng-Hsun Tsai 한국통신학회 2020 한국통신학회 APNOMS Vol.2020 No.09
As the number of network devices increases rapidly, it becomes more and more difficult to defend network attacks. Large-scaled attacks, such as SYN flooding, may lead to heavy burden to the switches as well as the controller in a software defined network (SDN). In this paper, we investigate the SYN flooding and Address Resolution Protocol (ARP) spoofing attacks in SDN, and then propose mechanisms to address these two attacks. We also present a new scheme to detect SYN flooding by using only a few forwarding rules. Moreover, we utilize the Programming Protocol-independent Packet Processors (P4) technique to mitigate the burden of the controller.