Understanding of human dynamics has drawn attention to various areas. Due to the wide spread of positioning technologies that use GPS or public Wi-Fi, location information can be obtained with high spatial-temporal resolution as well as at low cost. By collecting set of individual location information in real time, monitoring of human dynamics is recently considered possible and is expected to ...

Anomaly detection has recently become an important problem in many industrial and financial applications. Very often, the databases from which anomalies have to be found are located at multiple local sites and cannot be merged due to privacy reasons or communication overhead. In this paper, a novel general framework for distributed anomaly detection is proposed. The proposed method consists of ...

Anomaly Detection Systems aim to construct accurate network traffic models with the objective to discover yet unknown malicious network traffic patterns. In this paper, we study the use of the same methods in order to create a covert channel which is not discovered by Anomaly Detection Systems and can be used to exfiltrate (malicous) traffic from a network. The channel is created by imitating c...

Computer systems are vulnerable to abuse by insiders and to penetration by outsiders. The amount of monitoring data generated in computer networks is enormous. Tools are needed to ease the work of system operators. Anomaly detection attempts to recognise abnormal behaviour to detect intrusions. A prototype Anomaly Detection System has been constructed. The system provides means for automatic an...

Anomaly detection, detection of deviations from what is considered normal, is an important complement to misuse detection based on attack signatures. Anomaly detection in real-time places hard requirements on the algorithms used, making many proposed data mining techniques less suitable. ADWICE (Anomaly Detection With fast Incremental Clustering) uses the first phase of the existing BIRCH clust...

This paper discusses several issues of evaluation and comparison of anomaly detection algorithms, namely lack of publicly available implementations and annotated data sets. Another problem of many methods is a detection delay caused by operating on data binned to a long time intervals. The paper presents a library under development which aims to tackle the comparison and evaluation issues. Furt...

Signature based intrusion detection systems cannot detect new attacks. These systems are the most used and developed ones. Current anomaly based intrusion detection systems are also unable to detect all kinds of new attacks because they are designed to restricted applications on limited environment. Current hackers are using new attacks where neither preventive techniques mainly based on access...

practical applications every day. It has been used for fraud detection and intrusion detection for a long time, but in later years the usage has exploded to all kind of domains, like surveillance, industrial system monitoring, epidemiology, and so on. For an overview of different anomaly-detection methods and applications, see, for example, Chandola, Banerjee, and Kumar (2009). The approach tak...

Now day’s Outlier Detection is used in various fields such as Credit Card Fraud Detection, Cyber-Intrusion Detection, Medical Anomaly Detection, and Data Mining etc. So to detect anomaly objects from various types of dataset Outlier Detection techniques are used, that detects and remove the anomaly objects from the dataset. Outliers are the containments that divert from the other objects. Outli...