Yuichi Ohsita

TL;DR: The results show that the method can improve the accuracy of the traffic matrix estimation and achieve an adequate VNT as is the case with the reconfiguration using the actual traffic matrices.

TL;DR: This paper introduces a mechanism for detecting SYN flood traffic more accurately by taking into consideration the the time variation of arrival traffic, and shows that the arrival rate of normal TCP SYN packets can be modeled by a normal distribution.

TL;DR: This method models user behavior as sequences of user events including operation of home IoT devices and other monitored activities, and generates multiple event sequences by removing some events and learning the frequently observed sequences.

TL;DR: Through the simulation using actual traffic traces on a backbone network of Internet2, it is shown that traffic engineering using the traffic information predicted by the proposed prediction procedure can set up routes that accommodate traffic variation for several or more hours with efficient load balancing.

TL;DR: The EDCNN is a new CNN to reduce the negative effect of benign URLs redirected from compromised websites included in malicious URL sequences and lowers the operation cost of malware infection by reducing 47% of false alerts compared with a CNN when users access compromised websites but do not obtain exploit code due to browser fingerprinting.