arXiv subjects

Kazuya Okada

Publications and source records attributed to Kazuya Okada.

3 recordsLinked to original sources

arXiv2026

AC Field-driven orientational crossover and energy dissipation in suspended magnetic nanoparticles

By combining the Landau--Lifshitz--Gilbert equation with Brownian rotational dynamics of magnetic nanoparticles (MNPs), we theoretically investigate the role of particle rotation through easy-axis reorientation in magnetic fluid hyperthermia (MFH). Our results reveal a field-driven crossover in the stationary orientation of the easy axes, from predominantly perpendicular to predominantly parallel or antiparallel to the applied field as the field amplitude increases. Although the precise crossover field depends on particle size and excitation frequency, it occurs at approximately $0.5H_k$, where $H_k$ is the uniaxial anisotropy field. These orientational regimes are directly linked to the underlying microscopic dynamics and the associated MFH performance through the occurrence of switching and non-switching hysteresis cycles, predominantly associated with Néel magnetization reversal and Brownian particle rotation, respectively. The relative importance of these dissipation mechanisms also depends on frequency: at $f=1$ MHz, Brownian heating dominates at low field amplitudes, whereas Néel heating dominates at high fields. By contrast, at $f=100$ kHz, both contributions remain comparable over most of the investigated field range.

cond-mat.mtrl-sci↗

arXiv2021

Classification of URL bitstreams using Bag of Bytes

Protecting users from accessing malicious web sites is one of the important management tasks for network operators. There are many open-source and commercial products to control web sites users can access. The most traditional approach is blacklist-based filtering. This mechanism is simple but not scalable, though there are some enhanced approaches utilizing fuzzy matching technologies. Other approaches try to use machine learning (ML) techniques by extracting features from URL strings. This approach can cover a wider area of Internet web sites, but finding good features requires deep knowledge of trends of web site design. Recently, another approach using deep learning (DL) has appeared. The DL approach will help to extract features automatically by investigating a lot of existing sample data. Using this technique, we can build a flexible filtering decision module by keep teaching the neural network module about recent trends, without any specific expert knowledge of the URL domain. In this paper, we apply a mechanical approach to generate feature vectors from URL strings. We implemented our approach and tested with realistic URL access history data taken from a research organization and data from the famous archive site of phishing site information, PhishTank.com. Our approach achieved 2~3% better accuracy compared to the existing DL-based approach.

cs.NI↗

arXiv2021

Classifying DNS Servers based on Response Message Matrix using Machine Learning

Improperly configured domain name system (DNS) servers are sometimes used as packet reflectors as part of a DoS or DDoS attack. Detecting packets created as a result of this activity is logically possible by monitoring the DNS request and response traffic. Any response that does not have a corresponding request can be considered a reflected message; checking and tracking every DNS packet, however, is a non-trivial operation. In this paper, we propose a detection mechanism for DNS servers used as reflectors by using a DNS server feature matrix built from a small number of packets and a machine learning algorithm. The F1 score of bad DNS server detection was more than 0.9 when the test and training data are generated within the same day, and more than 0.7 for the data not used for the training and testing phase of the same day.

cs.NI↗