Anomaly Detection in Ethernet Networks Using Self Organising Maps

The network is a highly vulnerable venture for any organization that needs to have a set of computers for their work and needs to communicate among them. Any large organization that sets up a network needs a basic Ethernet or wireless framework for transferring data. Nevertheless the security concern of the organization creeps in and the computers storing the highly sensitive data need to be safeguarded. The threat to the network comes from the internal network as well as the external network. The amount of monitoring data generated in computer networks is enormous. Tools are needed to ease the work of system operators. Anomaly detection attempts to recognize abnormal behavior to detect intrusions. We have concentrated to design a prototype UNIX Anomaly Detection System. Neural Networks are tolerant of imprecise data and uncertain information. We worked to devise a tool for detecting such intrusions into the network. The tool uses the machine learning approaches ad clustering techniques like Self Organizing Map and compares it with the k-means approach. Our system is described for applying hierarchical unsupervised neural network to intrusion detection system. The network connection is characterized by six parameters and specified as a six dimensional vectors. The self organizing map creates a two dimensional lattice of neurons for network for each network service. During real time analysis, network features are fed to the neural network approaches and a winner is selected by finding a neuron that is closest in distance to it. The network is then classified as an intrusion if the distance is more than a preset threshold. The evaluation of this approach will be based on data sets provided by the Defense Advanced Research Projects Agency (DARPA) IDS evaluation in 1999

Structure, magnetic morphology and magnetization correlations in pulsed laser deposited CoFe2O4 (111) thin films

The present paper reports a complete study on the correlation between structure, morphology, and magnetic properties of (111)-oriented cobalt ferrite (CoFe2O4) thin films with varying film thickness. The CoFe2O4 (CFO) thin films were deposited on Pt-coated Si substrate by pulsed laser deposition (PLD) at 550 °C. The x-ray diffraction (XRD) data confirms the (111)-oriented growth of the cobalt ferrite films. The in-plane morphology of the films in the field emission scanning electron micrographs ensure the Stranski–Krastanov growth mechanism, and the atomic force micrographs confirms the effect of lattice relaxation on the morphology of the films with varying thickness. The possible cation distributions for the samples were determined from the Raman spectroscopy, which revealed the crystal structure-magnetic property correlations in cobalt ferrite films. The magnetic hysteresis (M−H) loops show a significant spin reorientation by showing the variation between the in-plane (IP) and out-of-plane (OP) magnetization. The presence of a kink on the OP M−H loops and its variation with film thickness clearly establishes the existence of competing magnetic anisotropies in the films. The high coercivity (HC) values observed for OP magnetization of cobalt ferrite films with thicknesses 115 nm and 125 nm may be explored for possible room-temperature (RT) device applications

Tailoring magnetic domains in Gd-Fe thin films

This paper presents the global modification of magnetic domains and magnetic properties in amorphous Gd19Fe81 thin films with rapid thermal processing at two distinct temperatures (250oC and 450oC), and with different time intervals viz., 2, 5, 10 and 20 minutes. 100 nm thick as-prepared films display nano-scale meandering stripe domains with high magnetic phase contrast which is the signature of perpendicular magnetic anisotropy. The films processed at 250oC for various time intervals show successive reduction in magnetic phase contrast and domain size. The domain pattern completely disappeared, and topography dominated mixed magnetic phase has been obtained for the films processed at 450oC for time intervals greater than 2 minutes. The magnetization measurements indicate the reduction in perpendicular magnetic anisotropy with increase in saturation magnetization for all the rapid thermal processed films. The experimental outputs have been used to simulate the domain pattern. Reduction in uniaxial anisotropy along with the increase in saturation magnetization successfully explain the experimental trend of decrease in domain size and magnetic contrast

Review on Point Dipole Approximation in Magnetic Force Microscopy

Image interpretation in magnetic force microscopy (MFM) requires details information about the internal microstructure the ferromangetic tip used for probing the surface microfield of a sample. Since these information are generally not experimentally available, image interpretation is more speculative than rigorously quantitative at the present time. This theoretical analysis confirms by a simple criterion that MFM image interpretation can be perform in terms of point dipole probing provided that some experiment constraints are satisfied. The validity of the criterion is demonstrated for various experimentally relevant example. Starting from tip transfer function (TTF), a minimum detectable wavelength will be used as a measure for the lateral resolution of the instrument. This minimum detectable wavelength will determine the detector noise level in the instrument’s configuration

Synthesis and characterization of Structural and magnetic properties of electrodeposited Cobalt Iron thin film

In this project, I fabricated thin films of cobalt iron alloy of different thickness by electrodeposition technique and studied the domain and dynamics of the domain walls by taking measurements from magnetic force microscopy (MFM). I measured the roughness by taking measurements from Atomic force microscopy. I characterized the films with XRD which showed the crystal structure of the film. The SEM images of Cobalt iron film exhibited nano crystallized structure and the variation of granular size as a function of the potential at which the film deposited

Synthesis and Analysis of Novel Thermo-Acoustic and Mechanical Behaviour of Rattan Reinforced Composite for Value Added Applications

The idea of low cost green thermoacoustic composites urge special attention as an alternative candidate for synthetic composites in designing of sound proof and highly flexural components for pollution free electric vehicle and other automotive industries. Short randomly oriented rattan fiber has significant contribution in development of high strength to weight ratio composite with high frequency dispersion capability. The present work employs the hand layup method for synthesis followed by ultrasonic cavitation for surface treatment with compatible methanol blended acrylic acid. The Scanning-Electron-Microscopic (SEM) and Fourier Transform Infrared (FTIR) spectroscopic characterization confirmed the reorientation of different functional groups which facilitate the sound absorption and mechanical properties. The composite with admissible tensile strength 47.5 MPa and high flexural property 121.89 MPa along with 30 HV hardness value has quite distinguishable mechanical characteristics for the fabricated composites. The unique characteristic sound absorption feature of the rattan fiber composite supports the acoustic behaviour with sound absorption coefficient (SAC) of 97% classifies as Class-A type as per ASTM C423-17 standard. A continuous mass loss of 69% from 390.89°C to 475°C is well supported by thermo gravimetric analysis. The regression analysis provides the optimum mechanical performance for which the composites execute its accuracy. Low sound transmissibility of the composite enhances its acoustic performance. The thermo-acoustic insulating properties such as thermal conductivity and acoustic behaviour of the materials are well described converting the rattan fiber for sustainable and eco-friendly applications

Direct observation of frozen moments in the NiFe/FeMn exchange bias system

We detect the presence of frozen magnetic moments in an exchange biased NiFe ferromagnet at the NiFe/FeMn ferromagnet/antiferromagnet interface by magnetic circular dichroism in x-ray absorption and resonant reflectivity experiments. Frozen moments are detected by means of the element-specific hysteresis loops. A weak dichroic absorption with unidirectional anisotropy can be linked to frozen magnetic moments in the ferromagnet. A more pronounced exchange bias for increasing the thickness of the FeMn layer correlates with an increase in orbital moment for interface Ni atoms carrying a frozen moment. These atoms compose about a single monolayer, but only a fraction of the atoms contributes by means of a strongly enhanced orbital moment to the macroscopic exchange bias phenomenon. The microscopic spin-orbit energy associated with these few interface frozen moment atoms appears to be sufficient to account for the macroscopic exchange bias energ

Direct observation of frozen moments in the NiFe/FeMn exchange bias system

We detect the presence of frozen magnetic moments in an exchange biased NiFe ferromagnet at the NiFe/FeMn ferromagnet/antiferromagnet interface by magnetic circular dichroism in x-ray absorption and resonant reflectivity experiments. Frozen moments are detected by means of the element-specific hysteresis loops. A weak dichroic absorption with unidirectional anisotropy can be linked to frozen magnetic moments in the ferromagnet. A more pronounced exchange bias for increasing the thickness of the FeMn layer correlates with an increase in orbital moment for interface Ni atoms carrying a frozen moment. These atoms compose about a single monolayer, but only a fraction of the atoms contributes by means of a strongly enhanced orbital moment to the macroscopic exchange bias phenomenon. The microscopic spin-orbit energy associated with these few interface frozen moment atoms appears to be sufficient to account for the macroscopic exchange bias energ

Magnetic and dielectric response in yttrium (Y)-manganese (Mn) substituted multiferroic Bi1−xYxFe1−yMnyO3 ( x = y = 0 ; x = 0.03 , 0.06 , 0.12 , y = 0.05) ceramics

The effect of Y and Mn substitution on the structural, magnetic, and dielectric properties of Bi1−xYxFe1−yMnyO3 (x=y=0;x=0.03,0.06,0.12,y=0.05) is investigated. X-ray diffraction and Raman spectroscopy analysis revealed that all the compounds were stabilized in the Rhombohedral structure (Space group: R3c) below Y(6%)-Mn co-doping. A minimal contribution from the Pbam phase was also observed at higher Y (12%). Co-doped compounds exhibit canted ferromagnetic behavior with a significant increase in the saturation magnetization (Ms) of 1.28 emu/g when (Y, Mn)=(0.12,0.05). Dielectric measurements at different temperatures ranging between 30∘C and 210∘C in a wide frequency range of 1 Hz–1 MHz were investigated. Impedance study is useful to understand the correlation of grain and grain boundary with electrical properties. The grain and grain boundary contribution to electrical parameters was presented with equivalent R-C circuit. Furthermore, electrical properties such as complex electrical modulus and electrical conductivity analysis clearly emphasize the substitution of Y and Mn, leading to a significant change in BiFeO3 ceramic

Diabetes Prediction: A Study of Various Classification based Data Mining Techniques

Data Mining is an integral part of KDD (Knowledge Discovery in Databases) process. It deals with discovering unknown patterns and knowledge hidden in data. Classification is a pivotal data mining technique with a very wide range of applications. Now a day’s diabetic has become a major disease which has almost crippled people across the globe. It is a medical condition that causes the metabolism to become dysfunctional and increases the blood sugar level in the body and it becomes a major concern for medical practitioner and people at large. An early diagnosis is the starting point for living well with diabetes. Classification Analysis on diabetic dataset is a part of this diagnosis process which can help to detect a diabetic patient from non-diabetic. In this paper classification algorithms are applied on the Pima Indian Diabetic Database which is collected from UCI Machine Learning Laboratory. Various classification algorithms which are Naïve Bayes Classifier, Logistic Regression, Decision Tree Classifier, Random Forest Classifier, Support Vector Classifier and XGBoost Classifier are analyzed and compared based on the accuracy delivered by the models