EKMC: Ensemble of kNN using MetaCost for Efficient Anomaly Detection
نویسندگان
چکیده
منابع مشابه
A Novel Ensemble Approach for Anomaly Detection in Wireless Sensor Networks Using Time-overlapped Sliding Windows
One of the most important issues concerning the sensor data in the Wireless Sensor Networks (WSNs) is the unexpected data which are acquired from the sensors. Today, there are numerous approaches for detecting anomalies in the WSNs, most of which are based on machine learning methods. In this research, we present a heuristic method based on the concept of “ensemble of classifiers” of data minin...
متن کاملEnsemble of Feature Chains for Anomaly Detection
Along with recent technological advances more and more new threats and advanced cyber-attacks appear unexpectedly. Developing methods which allow for identification and defense against such unknown threats is of great importance. In this paper we propose new ensemble method (which improves over the known cross-feature analysis, CFA, technique) allowing solving anomaly detection problem in semi-...
متن کاملA Bayesian Ensemble for Unsupervised Anomaly Detection
Methods for unsupervised anomaly detection suffer from the fact that the data is unlabeled, making it difficult to assess the optimality of detection algorithms. Ensemble learning has shown exceptional results in classification and clustering problems, but has not seen as much research in the context of outlier detection. Existing methods focus on combining output scores of individual detectors...
متن کاملAnomaly Detection Framework Using Rule Extraction for Efficient Intrusion Detection
Huge datasets in cyber security, such as network traffic logs, can be analyzed using machine learning and data mining methods. However, the amount of collected data is increasing, which makes analysis more difficult. Many machine learning methods have not been designed for big datasets, and consequently are slow and difficult to understand. We address the issue of efficient network traffic clas...
متن کاملUnsupervised Ensemble Anomaly Detection Using Time-Periodic Packet Sampling
We propose an anomaly detection method for finding patterns in network traffic that do not conform to legitimate (i.e., normal) behavior. The proposed method trains a baseline model describing the normal behavior of network traffic without using manually labeled traffic data. The trained baseline model is used as the basis for comparison with the audit network traffic. This anomaly detection wo...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Advances in Science, Technology and Engineering Systems Journal
سال: 2019
ISSN: 2415-6698,2415-6698
DOI: 10.25046/aj040552