The Application of a Dendric Cell Algorithm to a Robotic Classifier

The dendritic cell algorithm is an immune-inspired technique for processing time-dependant data. Here we propose it as a possible solution for a robotic classification problem. The dendritic cell algorithm is implemented on a real robot and an investigation is performed into the effects of varying the migration threshold median for the cell population. The algorithm performs well on a classification task with very little tuning. Ways of extending the implementation to allow it to be used as a classifier within the field of robotic security are suggested

An Immune Inspired Approach to Anomaly Detection

The immune system provides a rich metaphor for computer security: anomaly detection that works in nature should work for machines. However, early artificial immune system approaches for computer security had only limited success. Arguably, this was due to these artificial systems being based on too simplistic a view of the immune system. We present here a second generation artificial immune system for process anomaly detection. It improves on earlier systems by having different artificial cell types that process information. Following detailed information about how to build such second generation systems, we find that communication between cells types is key to performance. Through realistic testing and validation we show that second generation artificial immune systems are capable of anomaly detection beyond generic system policies. The paper concludes with a discussion and outline of the next steps in this exciting area of computer security.Comment: 19 pages, 4 tables, 2 figures, Handbook of Research on Information Security and Assuranc

Information Fusion for Anomaly Detection with the Dendritic Cell Algorithm

Dendritic cells are antigen presenting cells that provide a vital link between the innate and adaptive immune system, providing the initial detection of pathogenic invaders. Research into this family of cells has revealed that they perform information fusion which directs immune responses. We have derived a Dendritic Cell Algorithm based on the functionality of these cells, by modelling the biological signals and differentiation pathways to build a control mechanism for an artificial immune system. We present algorithmic details in addition to experimental results, when the algorithm was applied to anomaly detection for the detection of port scans. The results show the Dendritic Cell Algorithm is sucessful at detecting port scans.Comment: 21 pages, 17 figures, Information Fusio

Articulation and Clarification of the Dendric Cell Algorithm

The Dendritic Cell algorithm (DCA) is inspired by recent work in innate immunity. In this paper a formal description of the DCA is given. The DCA is described in detail, and its use as an anomaly detector is illustrated within the context of computer security. A port scan detection task is performed to substantiate the influence of signal selection on the behaviour of the algorithm. Experimental results provide a comparison of differing input signal mappings

The deterministic Dendritic Cell Algorithm

The Dendritic Cell Algorithm is an immune-inspired algorithm originally based on the function of natural dendritic cells. The original instantiation of the algorithm is a highly stochastic algorithm. While the performance of the algorithm is good when applied to large real-time datasets, it is difficult to analyse due to the number of random-based elements. In this paper a deterministic version of the algorithm is proposed, implemented and tested using a port scan dataset to provide a controllable system. This version consists of a controllable amount of parameters, which are experimented with in this paper. In addition the effects are examined of the use of time windows and variation on the number of cells, both which are shown to influence the algorithm. Finally a novel metric for the assessment of the algorithms output is introduced and proves to be a more sensitive metric than the metric used with the original Dendritic Cell Algorithm

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

The Application of a Dendric Cell Algorithm to a Robotic Classifier

The dendritic cell algorithm is an immune-inspired technique for processing time-dependant data. Here we propose it as a possible solution for a robotic classification problem. The dendritic cell algorithm is implemented on a real robot and an investigation is performed into the effects of varying the migration threshold median for the cell population. The algorithm performs well on a classification task with very little tuning. Ways of extending the implementation to allow it to be used as a classifier within the field of robotic security are suggested