scholarly journals Anomaly Detection in NFV Using Tree-Based Unsupervised Learning Method

2019 ◽  
Author(s):  
Girish L
Keyword(s):  
Anomaly Detection ◽  
Data Center ◽  
Learning Algorithm ◽  
Traffic Monitoring ◽  
Anomalous Behavior ◽  
Normal Operation ◽  
Machine Learning Algorithm ◽  
Memory Network ◽  
Anomaly Score ◽  
Isolation Forest

With the increased adoption of virtualized NFs in data center, it is crucial toaddress some of the challenges such as performance and availability of the applications invirtualized network environment. The normal operation of the network can be analyzed withrespect to the usage of various resources like, CPU, memory, network and disk. Inefficientusage or over usage of these resources leads to anomalous behavior. Anomalies are oftenpreceded by faults. It is important to detect anomalies before they occur. The detectedanomalies can be used for corrective and optimization actions. This paper presents that;unsupervised machine learning algorithm performs better compared to supervised machinelearning algorithms in detecting anomalies. Here we use isolation forest algorithm on timeseries dataset, which is collected using monitoring agent collectd. Stress is induced to thecomputer using traffic monitoring generator and stress-ng. The results show that isolationforest algorithm gives better performance in anomaly detection with good anomaly score.

scholarly journals Spectral-Spatial Anomaly Detection of Hyperspectral Data Based on Improved Isolation Forest

2021 ◽  
Author(s):  
Xiangyu Song ◽  
Sunil Aryal ◽  
Kai Ming Ting ◽  
zhen Liu ◽  
Bin He
Keyword(s):  
Anomaly Detection ◽  
Spatial Information ◽  
Hyperspectral Image ◽  
Gabor Filter ◽  
Poor Performance ◽  
Hyperspectral Data ◽  
Relative Mass ◽  
Anomaly Score ◽  
Spectral Anomaly ◽  
Isolation Forest

Anomaly detection in hyperspectral image is affected by redundant bands and the limited utilization capacity of spectral-spatial information. In this article, we propose a novel Improved Isolation Forest (IIF) algorithm based on the assumption that anomaly pixels are more susceptible to isolation than the background pixels. The proposed IIF is a modified version of the Isolation Forest (iForest) algorithm, which addresses the poor performance of iForest in detecting local anomalies and anomaly detection in high-dimensional data. Further, we propose a spectral-spatial anomaly detector based on IIF (SSIIFD) to make full use of global and local information, as well as spectral and spatial information. To be specific, first, we apply the Gabor filter to extract spatial features, which are then employed as input to the Relative Mass Isolation Forest (ReMass-iForest) detector to obtain the spatial anomaly score. Next, original images are divided into several homogeneous regions via the Entropy Rate Segmentation (ERS) algorithm, and the preprocessed images are then employed as input to the proposed IIF detector to obtain the spectral anomaly score. Finally, we fuse the spatial and spectral anomaly scores by combining them linearly to predict anomaly pixels. The experimental results on four real hyperspectral data sets demonstrate that the proposed detector outperforms other state-of-the-art methods.

scholarly journals Effective and efficient network anomaly detection system using machine learning algorithm

2019 ◽  
Vol 8 (1) ◽  
pp. 46-51 ◽  
Author(s):  
Mukrimah Nawir ◽  
Amiza Amir ◽  
Naimah Yaakob ◽  
Ong Bi Lynn
Keyword(s):  
Machine Learning ◽  
Anomaly Detection ◽  
Processing Time ◽  
Learning Algorithm ◽  
Detection System ◽  
Malicious Code ◽  
Supervised Machine Learning ◽  
Machine Learning Algorithm ◽  
Network Anomaly Detection ◽  
Anomaly Detection System

Network anomaly detection system enables to monitor computer network that behaves differently from the network protocol and it is many implemented in various domains. Yet, the problem arises where different application domains have different defining anomalies in their environment. These make a difficulty to choose the best algorithms that suit and fulfill the requirements of certain domains and it is not straightforward. Additionally, the issue of centralization that cause fatal destruction of network system when powerful malicious code injects in the system. Therefore, in this paper we want to conduct experiment using supervised Machine Learning (ML) for network anomaly detection system that low communication cost and network bandwidth minimized by using UNSW-NB15 dataset to compare their performance in term of their accuracy (effective) and processing time (efficient) for a classifier to build a model. Supervised machine learning taking account the important features by labelling it from the datasets. The best machine learning algorithm for network dataset is AODE with a comparable accuracy is 97.26% and time taken approximately 7 seconds. Also, distributed algorithm solves the issue of centralization with the accuracy and processing time still a considerable compared to a centralized algorithm even though a little drop of the accuracy and a bit longer time needed.

scholarly journals Spectral-Spatial Anomaly Detection of Hyperspectral Data Based on Improved Isolation Forest

2021 ◽  
Author(s):  
Xiangyu Song
Keyword(s):  
Anomaly Detection ◽  
Spatial Information ◽  
Hyperspectral Image ◽  
Gabor Filter ◽  
Poor Performance ◽  
Hyperspectral Data ◽  
Relative Mass ◽  
Anomaly Score ◽  
Spectral Anomaly ◽  
Isolation Forest

Anomaly detection in hyperspectral image is affected by redundant bands and the limited utilization capacity of spectral-spatial information. In this article, we propose a novel Improved Isolation Forest (IIF) algorithm based on the assumption that anomaly pixels are more susceptible to isolation than the background pixels. The proposed IIF is a modified version of the Isolation Forest (iForest) algorithm, which addresses the poor performance of iForest in detecting local anomalies and anomaly detection in high-dimensional data. Further, we propose a spectral-spatial anomaly detector based on IIF (SSIIFD) to make full use of global and local information, as well as spectral and spatial information. To be specific, first, we apply the Gabor filter to extract spatial features, which are then employed as input to the Relative Mass Isolation Forest (ReMass-iForest) detector to obtain the spatial anomaly score. Next, original images are divided into several homogeneous regions via the Entropy Rate Segmentation (ERS) algorithm, and the preprocessed images are then employed as input to the proposed IIF detector to obtain the spectral anomaly score. Finally, we fuse the spatial and spectral anomaly scores by combining them linearly to predict anomaly pixels. The experimental results on four real hyperspectral data sets demonstrate that the proposed detector outperforms other state-of-the-art methods.

scholarly journals Spectral-Spatial Anomaly Detection of Hyperspectral Data Based on Improved Isolation Forest

2021 ◽  
Author(s):  
Xiangyu Song ◽  
Sunil Aryal ◽  
Kai Ming Ting ◽  
zhen Liu ◽  
Bin He
Keyword(s):  
Anomaly Detection ◽  
Spatial Information ◽  
Hyperspectral Image ◽  
Gabor Filter ◽  
Poor Performance ◽  
Hyperspectral Data ◽  
Relative Mass ◽  
Anomaly Score ◽  
Spectral Anomaly ◽  
Isolation Forest

Anomaly detection in hyperspectral image is affected by redundant bands and the limited utilization capacity of spectral-spatial information. In this article, we propose a novel Improved Isolation Forest (IIF) algorithm based on the assumption that anomaly pixels are more susceptible to isolation than the background pixels. The proposed IIF is a modified version of the Isolation Forest (iForest) algorithm, which addresses the poor performance of iForest in detecting local anomalies and anomaly detection in high-dimensional data. Further, we propose a spectral-spatial anomaly detector based on IIF (SSIIFD) to make full use of global and local information, as well as spectral and spatial information. To be specific, first, we apply the Gabor filter to extract spatial features, which are then employed as input to the Relative Mass Isolation Forest (ReMass-iForest) detector to obtain the spatial anomaly score. Next, original images are divided into several homogeneous regions via the Entropy Rate Segmentation (ERS) algorithm, and the preprocessed images are then employed as input to the proposed IIF detector to obtain the spectral anomaly score. Finally, we fuse the spatial and spectral anomaly scores by combining them linearly to predict anomaly pixels. The experimental results on four real hyperspectral data sets demonstrate that the proposed detector outperforms other state-of-the-art methods.

scholarly journals Transaction Fraud Detection (Anomaly detection) using Machine Learning

2021 ◽  
pp. 361-363
Author(s):  
Prof. Viresh Vanarote ◽  
Omkar Gaykar ◽  
Sarfaraz Saudagar ◽  
Naresh Bulbule ◽  
Tushar Funde
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Anomaly Detection ◽  
Online Shopping ◽  
Learning Algorithm ◽  
Fraud Detection ◽  
Machine Learning Algorithm ◽  
Behavior Profiles

Now a day’s online shopping crazy thing for peoples. As well as many people uses Online transaction for many purposes. Transaction fraud is growing seriously. Therefore, the study on fraud detection is interesting and significant and we can say necessary. An important way of detecting fraud is to extract the behavior profiles (BPs) of users based on their historical transaction records, and then to verify if an incoming transaction is a fraud or not in view of Their BPs. Also we apply SVM, Adaboost, and Neural Network machine learning algorithm to see which one is giving the best result.

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