Unreliability and a dynamic nature are frequently present in the field of WSN, making anomaly detection necessary. Although events are often functions of more than one attribute and the energy in sensors is lim
In order to solve these three problems, we propose an efficient log anomaly detection method based on an improved kNN algorithm with an automatically labeled sample set. This method first proposes a log parsing method based on N-gram and frequent pattern mining (FPM) method, which reduces the ...
Anomaly detectionEncoding path modelKNN-DistortMetro traffic flowAnomaly detection is an important problem that has been well researched in diverse application domains. However, to the best of our knowledge, the anomaly detection for metro traffic flow has not been investigated before. In this paper,...
This paper proposes a method to identify flooding attacks in real-time, based on anomaly detection by genetic weighted KNN (-nearest-neighbor) classifiers. A genetic algorithm is used to train an optimal weight vector for features; meanwhile, an unsupervised clustering algorithm is applied to ...
Anomaly detectionwireless sensor networkdata bufferconfidence intervaldifferenceIn recent years use of a Wireless Sensor Network (WSN) has become a leading area of research in various applications. Because of WSN limitation of its own features that low ability of calculation, small volume of storage,...
鈥 This paper describes a hybrid design for intrusion detection that combines anomaly detection with misuse detection. The proposed method includes an ensemble feature selecting classifier and a data mining Classifier. The former consists of four classifiers using different sets of features and each ...
The main objective of this article is to propose a novel anomaly detection method based on adaptive Mahalanobis-squared distance and one-class kNN rule called AMSD-kNN for SHM under varying environmental conditions. The central idea behind the proposed method is to find sufficient nearest neighbors ...
This study proposes to use a new autoencoder method and compare it to the K-Nearest Neighbors Algorithm in order to improve the accuracy of medical imaging anomaly identification. Materials and Methods: With an alpha of 0.8, G-power of 0.80, and beta of 0.2, the SPSS program was used to...
EVALUATIONS OF THE EFFECTIVENESS OF ANOMALY BASED INTRUSION DETECTION SYSTEMS BASED ON AN ADAPTIVE KNN ALGORITHMThe aim of the present paper is to present some evaluations of the effectiveness of IDS based on the k-Nearest Neighbor algorithm with Jaro and Jaro-Wincler distances, applied as metrics...
Anomaly detectionData streamsLSHTime sensitiveAnomaly detection is an important data mining method aiming to discover outliers that show significant diversion from their expected behavior. A widely used criteria for determining outliers is based...