rise in temperature occurred after soil temperature was measured in different time series. in this article, ldf (logarithmic derivative filter) innovative method is applied to detect anomalies. this method tests soil temperature time series for 12 earthquakes in iran with magnitudes of either five or greater than five. results from this method were collected. based on the results of ldf method ...

In this paper, we introduce a hierarchical anomaly network intrusion detection system, which is capable of detecting network–based attacks using statistical preprocessing models and neural network classification. The sample network used has a three-tier hierarchy, where the lower tier detectors report to the higher tiers. The statistical preprocessor converts network traffic sample information ...

In this paper, we introduce the Java Anomaly Detection (JAD) library which provides a convenient way for the statistical analysis of network traffic for anomalies and intrusions. JAD provides a fully documented generic Java class library with Application Programming Interfaces (APIs) to handle data and heuristic methods for network anomaly detection. The library uses GNU R in the background and...

Data mining is the technique of discovering patterns among data to analyze patterns or decision making predictions. Anomaly detection is the technique of identifying occurrences that deviate immensely from the large amount of data samples. Advances in computing generates large amount of data from different sources, which is very difficult to apply machine learning techniques due to existence of...

In this paper we analyze the use of different types of statistical tests for the correlation of anomaly detection alerts. We show that the Granger Causality Test, one of the few proposals that can be extended to the anomaly detection domain, strongly depends on good choices of a parameter which proves to be both sensitive and difficult to estimate. We propose a different approach based on a set...

In recent years network anomaly detection has become an important area for both commercial interests as well as academic research. Applications of anomaly detection typically stem from the perspectives of network monitoring and network security. In network monitoring, a service provider is often interested in capturing such network characteristics as heavy flows that use a link with a given cap...

Since the advent of intrusion detection system (IDS) in the early 1980s, IDS has been suffering many problems until now. The detection of novel attacks and lower rate of false alarms must be realized in successful IDS. Misuse detection compares data against predefined patterns usually collected by an IDS signature database. It is hard for misuse detection to detect even slightly variation of kn...

Existing data mining approaches to complex systems anomaly detection use uni-variate and/or multi-variate statistical hypothesis testing to assign anomaly scores to data streams associated with system components. The former approach assumes statistical independence of individual components, while the latter assumes substantial global systemic correlation. As a compromise between these two epist...

anomaly detection (ad) has recently become an important application of target detection in hyperspectral images. the reed-xialoi (rx) is the most widely used ad algorithm that suffers from “small sample size” problem. the best solution for this problem is to use dimensionality reduction (dr) techniques as a pre-processing step for rx detector. using this method not only improves the detection p...