Accurate ship detection from radar imagery needs a proper statistical characterization of both target and sea scattering. Even if a statistical model for the target class is not available, effective discrimination between “sea” and “nonsea” classes can be achieved making use of a good model for the sea scattering distribution (i.e. anomaly detection). Hence, the retrieval of the statistical cha...

This paper discusses a statistical anomaly-detection method for model-independent searches new physics in collision events produced at the Large Hadron Collider (LHC). The requires calculations of Z-scores large number Lorenz-invariant variables to identify that deviate from those expected Standard Model (SM).

Even though mainly statistical methods have been used in anomaly network intrusion detection, to detect various attack types, machine learning based anomaly detection was introduced. Machine learning based anomaly detection started from research applying traditional learning algorithms of artificial intelligence to intrusion detection. However, detection rates of these methods are not satisfact...

OBJECTIVE Broadly, this research aims to improve the outbreak detection performance and, therefore, the cost effectiveness of automated syndromic surveillance systems by building novel, recombinant temporal aberration detection algorithms from components of previously developed detectors. METHODS This study decomposes existing temporal aberration detection algorithms into two sequential stage...

Anomaly detection is important for the correct functioning of wireless sensor networks. Recent studies have shown that node mobility along with spatial correlation of the monitored phenomenon in sensor networks can lead to observation data that have long range dependency, which could significantly increase the difficulty of anomaly detection. In this article, we develop an anomaly detection sch...

Network anomaly detection has been a hot research topic for many years. Most detection systems proposed so far employ a supervised strategy to accomplish the task, using either signature-based detection methods or supervised-learning techniques. However, both approaches present major limitations: the former fails to detect unknown anomalies, the latter requires training and labeled traffic, whi...

Abstract: Anomaly detection is important for the correct functioning of wireless sensor networks. Recent studies have shown that node mobility along with spatial correlation of the monitored phenomenon in sensor networks can lead to observation data that have long range dependency, which could significantly increase the difficulty of anomaly detection. In this paper, we develop an anomaly detec...

Network and information security is of increasing concern as intruders utilize more advanced technologies, and attacks are occurring much more frequently. A simple intrusion can cause an enterprise financial disaster, a threat to national safety, or loss of human life. Network-based and computer-based intrusion detection systems (IDS's) started appearing some twenty years ago. Now, there are va...

T his article describes a family of unsupervised approaches to video anomaly detection based on statistical activity analysis. Approaches based on activity analysis provide intriguing possibilities for region-of-interest (ROI) processing since relevant activities and their locations are detected prior to higher-level processing such as object tracking, tagging, and classification. This strategy...