Methods from machine learning are used in the design of secure Industrial Control Systems.
Such methods focus on two major areas: detection of intrusions at the network level using …

Modern network intrusion detection systems must be able to handle large and fast changing
data, often also taking into account real-time requirements. Ensemble-based data mining …

In this paper, a new hybrid intrusion detection method that hierarchically integrates a misuse
detection model and an anomaly detection model in a decomposition structure is proposed …

Most of the security threats in various communications networks are posed by the illegitimate
entities that enter or intrude within the network perimeter, which could commonly be termed …

The development and applications of mobile communication technologies in intelligent
autonomous transportation systems have led to an extraordinary rise in the mount of …

During the last decade, various machine learning and data mining techniques have been
applied to Intrusion Detection Systems (IDSs) which have played an important role in …

Intrusion has become a growing concern today. With the advent of new technologies each
day and widespread of computers, security has become a very important issue. Attacks like …

A high false alarm rate of anomaly-based, on-line, high throughput intrusion detection
systems (IDS) is a serious concern, often rendering these IDSs impractical for use in real …

As Network traffic rises and attacks become more widespread and complicated, we must
come across Innovative ways to enrich Intrusion Detection Systems in Cloud Computing …

Environmental sensors are exploited in smart homes for many purposes. Sensor data
inherently carries behavioral information, possibly useful to infer wellness and health-related …