ToLeRating UR-STD

A new emerging paradigm of Uncertain Risk of Suspicion, Threat and Danger, observed across the field of information security, is described. Based on this paradigm a novel approach to anomaly detection is presented. Our approach is based on a simple yet powerful analogy from the innate part of the human immune system, the Toll-Like Receptors. We argue that such receptors incorporated as part of an anomaly detector enhance the detector’s ability to distinguish normal and anomalous behaviour. In addition we propose that Toll-Like Receptors enable the classification of detected anomalies based on the types of attacks that perpetrate the anomalous behaviour. Classification of such type is either missing in existing literature or is not fit for the purpose of reducing the burden of an administrator of an intrusion detection system. For our model to work, we propose the creation of a taxonomy of the digital Acytota, based on which our receptors are created.

Further exploration of the Dendritic Cell Algorithm: antigen multiplier and time windows

As an immune-inspired algorithm, the Dendritic Cell Algorithm (DCA), produces promising performance in the field of anomaly detection. This paper presents the application of the DCA to a standard data set, the KDD 99 data set. The results of different implementation versions of the DCA, including antigen multiplier and moving time windows, are reported. The real-valued Negative Selection Algorithm (NSA) using constant-sized detectors and the C4.5 decision tree algorithm are used, to conduct a baseline comparison. The results suggest that the DCA is applicable to KDD 99 data set, and the antigen multiplier and moving time windows have the same effect on the DCA for this particular data set. The real-valued NSA with contant-sized detectors is not applicable to the data set. And the C4.5 decision tree algorithm provides a benchmark of the classification performance for this data set.