Principal Component Analysis based anomaly detection approaches have been extensively studied recently. However, none of these approaches address the problem of anomaly localization. In this paper, we proposed a novel approach based on PCA to perform anomaly detection and localization in sensor network simultaneously. By enforcing the joint sparsity across the Principal Component in the abnorma...

Anomaly detection is a pattern recognition task whose goal is to report the occurrence of abnormal or unknown behavior in a given system being monitored. In this paper we propose a general procedure for the computation of decision thresholds for anomaly detection in mobile communication networks. The proposed method is based on Kohonen’s Self-Organizing Map (SOM) and the computation of nonparam...

Windows NT has become the dominant desktop platform. To date, host-based intrusion detection research has focused on Unixavored platforms. As a result, we have a large gap between the platform people use in practice and the platforms on which intrusion detection research is active. In this paper, we examine the feasibility of applying host-based intrusion detection to the Windows NT platform. S...

We present a method that improves the results of network intrusion detection by integration of several anomaly detection algorithms through trust and reputation models. Our algorithm is based on existing network behavior analysis approaches that are embodied into several detection agents. We divide the processing into three distinct phases: anomaly detection, trust model update and collective t...

Energy efficiency is one of the most important current challenges, and its impact at a global level considerable. To solve it critical that consumers are able to control their energy consumption. In this paper, we propose using time series window-based entropy detect anomalies in electricity consumption household when pattern behavior exhibits change. We compare accuracy approach with two machi...

In recent years, machine learning-based cyber intrusion detection methods have gained increasing popularity. The number and complexity of new attacks continue to rise; therefore, effective intelligent solutions are necessary. Unsupervised learning techniques particularly appealing systems since they can detect known unknown types as well zero-day attacks. the current paper, we present an unsupe...

In order to solve the problem that complex pig house environment leads difficulty and low accuracy of abnormal detection group pigs. The video 9 adult fattening pigs were collected, key frames obtained by frame differential method as training set, YOLOX model for was constructed. results show average on test set is 98.0%. research can provide a reference anomalies in breeding farms.