Distributed Denial of Service (DDoS) attacks continue to adversely affect internet-based
services and applications. Various approaches have been proposed to detect different types …

Principal component analysis (PCA) has received increasing attention as a method to
distinguish network traffic anomalies from normal data instances based on its orthogonal …

In this paper we describe how Bayesian Principal Anomaly Detection (BPAD) can be used
for detecting long and short term trends and anomalies in geographically tagged alarm data …

Traffic patterns in backbone IP networks often deviate from the norm, to lead to events
commonly termed as anomalies. Different algorithms have been proposed in literature to …

It is imperative for Internet-based services to monitor service performance closely and detect
anomalies as soon as possible. Despite this, deploying anomaly detectors to a particular …

Machine Learning (ML) has proven itself to be one of the most powerful tools to detect and
prevent cyber-attacks. In this study, ML is used for anomaly detection in an Internet of Things …

We propose a new algorithm based on machine learning techniques for automatic intruder
detection in surveillance networ s. The algorithm is theoretically founded on the concept of …

Visual surveillance networks are installed in many sensitive places in the present world.
Human security officers are required to continuously stare at large numbers of monitors …

Anomaly detection refers to the problem of finding patterns in data that do not conform to
expected behavior. It is very important to timely detect parameter anomalies in real-world …

Unwanted traffic is a major concern in the Internet today. Unwanted traffic includes Denial of
Service attacks, worms, and spam. Identifying and mitigating unwanted traffic costs …