Model-Based Outlier Detection System with Statistical Preprocessing
نویسندگان
چکیده
منابع مشابه
An Outlier Detection-Based Alert Reduction Model
Intrusion Detection Systems (IDSs) are widely deployed with increasing of unauthorized activities and attacks. However they often overload security managers by triggering thousands of alerts per day. And up to 99% of these alerts are false positives (i.e. alerts that are triggered incorrectly by benign events). This makes it extremely difficult for managers to correctly analyze security state a...
متن کاملFP-outlier: Frequent pattern based outlier detection
An outlier in a dataset is an observation or a point that is considerably dissimilar to or inconsistent with the remainder of the data. Detection of such outliers is important for many applications and has recently attracted much attention in the data mining research community. In this paper, we present a new method to detect outliers by discovering frequent patterns (or frequent itemsets) from...
متن کاملRobust Statistical Methods for Automated Outlier Detection
The computational challenge of automating outlier, or blunder point, detection in radio metric data requires the use of nonstandard statistical methods because the outliers have a deleterious effect upon standard least squares methods. The particular nonstandard methods most applicable to the task are the robust statistical techniques that have undergone intense development since the 1960s. The...
متن کاملStatistical Outlier Detection in Large Multivariate Datasets
This work focuses on detecting outliers within large and very large datasets using a computationally efficient procedure. The algorithm uses Tukey’s biweight function applied on the dataset to filter out the effects of extreme values for obtaining appropriate location and scale estimates. Robust Mahalanobis distances for all data points are calculated using these location and scale estimates. A...
متن کاملRank-Based Outlier Detection
We propose a new approach for outlier detection, based on a new ranking measure that focuses on the question of whether a point is “important” for its nearest neighbors; using our notations low cumulative rank implies the point is central. For instance, a point centrally located in a cluster has relatively low cumulative sum of ranks because it is among the nearest neighbors of its own nearest ...
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: Journal of Modern Applied Statistical Methods
سال: 2016
ISSN: 1538-9472
DOI: 10.22237/jmasm/1462077480