Fe-Dy Nanogranular Thin Films: Investigation of Structural, Microstructural and Magnetic Properties

A series of Few(100) Dy-x(x) thin films with the concentration range x = 6 to 35 were fabricated by dc magnetron sputtering process. X-ray diffraction and TEM studies revealed that films have separate Fe and Dy nanograins and that there is no intermixing of Fe and Dy thus forming nanogranular films. This unmixed behaviour in our thin films is very different from the bulk Fe-Dy alloys where several stoichiometric compounds can be formed. Magnetic properties of the films have been systematically studied. The contribution to the total magnetization is due to the Fe grains and the Dy grains are paramagnetic down to 4 K. The saturation magnetization of all the samples is significantly lower than that of bulk Fe due to the existence of superparamagnetic Fe grains. Upon increasing x, the in-plane magnetic anisotropy is found to decrease and the samples become isotropic. The zero field cooled and field cooled magnetization measurements also confirmed the presence of the superparamagnetic Fe grains

Exchange Bias Induced by the Spin Glass-Like Surface Spins in Sputter Deposited Fe3O4 Thin Films

The exchange bias in the reactive sputtered polycrystalline Fe3O4 thin films of thicknesses 50 and 150 nm is studied. X-ray diffraction, laser Raman, and selected area electron diffraction studies confirm the formation of the Fe3O4 single phase. The high-resolution transmission electron microscope images show the presence of well-defined crystallites. The presence of the exchange bias effect is mainly due to the existence of a significant exchange coupling between the core spins and the spin glass-like surface spins of the grains. The temperature dependence of the magnitude of the exchange bias field HEB shows two exponential regimes of which the lower temperature regime corresponds to the spin freezing effect below 50 K. The first magnetization curves measured after zero field cooling show S-shape below the spin freezing temperature. The presence of superparamagnetism and spin freezing has been investigated through the field cooled (FC) and zero FC magnetization measurements. Temperature dependence of coercivity also indicates the spin freezing effect. Hence, the observed large exchange bias of the samples at lower temperatures is due to the freezing of the surface spins

Machine Learning Inspired Phishing Detection (PD) for Efficient Classification and Secure Storage Distribution (SSD) for Cloud-IoT Application

Cloud-IoT data security and privacy have become a major problem due to its sensitivity, which curbs multiple cloud applications. In addition, if the encrypted data lives in one place, in many fields, such as the financial industry and government agencies, the man-in-the-middle-attack (MMA) and phishing attack (PA) may have chances of realising the extraction. The phishing goal is evaluated and predicted by most previous machine learning models through a discrete or continuous result. The current models lag in accurately determining both attacks because of this approach. We developed a three-step phishing detection (PD) framework inspired by machine learning and a secure storage distribution (SSD) for cloud to improve model accuracy and storage security. The partition-based selection of features is designed for phishing detection (PD) with a hybrid classifier approach and hyper-parameter classifier tuning. Initially, the entire data set is partitioned by entropy and is hybridised for each performing model partition. In order to reduce the complexity, the next entropy is applied to decrease the dimension of each partition. Finally, to improve precision, the performing model is optimised with hyper-parameter tuning. The partition-based feature choice with the hybrid classifier approach outperforms with 97.86% accuracy for both attack detection from the experimental and comparative results of SVM, LM, NN and RF. Atlast, SSD performance is evaluated against other storage models where SSD outperforms other models