This paper looks into the problem of detecting network anomalies by analyzing NetFlow
records. While many previous works have used statistical models and machine learning …

Modeling network traffic is an endeavor actively carried on since early digital
communications, supporting a number of practical applications, that range from network …

Outlier detection is an extensive research area, which has been intensively studied in
several domains such as biological sciences, medical diagnosis, surveillance, and traffic …

In this paper we propose a scalable, unsupervised approach for detecting anomalies in the
Internet of Things (IoT). Complex devices are connected daily and eagerly generate vast …

Intrusion detection systems (IDSs) play an important role in identifying malicious attacks and
threats in networking systems. As fundamental tools of IDSs, learning based classification …

The fast-growing Internet-of-Things (IoT) market has opened up a large threat landscape,
given the wide deployment of IoT devices in both consumer and commercial spaces. Attacks …

Over the last decade, vehicular ad-hoc networks (VANETs) have received a greater attention
in academia and industry due to their influence in intelligent transportation systems …

With the advent of the digital information age, new data services such as virtual reality,
industrial Internet, and cloud computing have proliferated in recent years. As a result, it …

To ensure the security and stabilization of the communication networks, anomaly detection
is the first line of defense. However, their learning process suffers two major issues:(1) …

IoT market is rapidly changing the cyber threat landscape. The challenges to security and
privacy arise not only because IoT devices are large in number, but also because IoT …