Anomaly detection is an important problem that has been researched within diverse
research areas and application domains. Many anomaly detection techniques have been …

In a contemporary data center, Linux applications often generate a large quantity of real-time
system call traces, which are not suitable for traditional host-based intrusion detection …

Outliers are also referred to as abnormalities, discordants, deviants, or anomalies in the data
mining and statistics literature. In most applications, the data is created by one or more …

In the statistics community, outlier detection for time series data has been studied for
decades. Recently, with advances in hardware and software technology, there has been a …

This chapter will provide a detailed survey of frequent pattern mining algorithms. A wide
variety of algorithms will be covered starting from Apriori. Many algorithms such as Eclat …

Security is of paramount importance in this new era of on-demand Cloud Computing.
Researchers have provided a survey on several intrusion detection techniques for detecting …

This survey attempts to provide a comprehensive and structured overview of the existing
research for the problem of detecting anomalies in discrete/symbolic sequences. The …

Malicious software–so called malware–poses a major threat to the security of computer
systems. The amount and diversity of its variants render classic security defenses ineffective …

A serious security threat today is malicious executables, especially new, unseen malicious
executables often arriving as email attachments. These new malicious executables are …

A new approach, based on the k-Nearest Neighbor (kNN) classifier, is used to classify
program behavior as normal or intrusive. Program behavior, in turn, is represented by …