Frequency analysis for dendritic cell population tuning

Oates, Robert; Kendall, Graham; Garibaldi, Jonathan M.

doi:10.1007/s12065-008-0011-y

Cited by 30 publications

(25 citation statements)

References 23 publications

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“…In the case that the input data consist of m (m ∈ N) signal instances, by formula 10, m m lifespans of the cell are required to process the whole input data. To satisfy the real-time bound, the duration needed for the standard DCA to get the final detection result can be formalised as in 13.…”

Section: Discussion Of the Analysis Processmentioning

confidence: 99%

Exploration of the Dendritic Cell Algorithm with the Duration Calculus

Greensmith

Aickelin

2009

SSRN Journal

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Abstract. As one of the newest members in Artificial Immune Systems (AIS), the Dendritic Cell Algorithm (DCA) has been applied to a range of problems. These applications mainly belong to the field of anomaly detection. However, real-time detection, a new challenge to anomaly detection, requires improvement on the real-time capability of the DCA. To assess such capability, formal methods in the research of real-time systems can be employed. The findings of the assessment can provide guideline for the future development of the algorithm. Therefore, in this paper we use an interval logic based method, named the Duration Calculus (DC), to specify a simplified single-cell model of the DCA. Based on the DC specifications with further induction, we find that each individual cell in the DCA can perform its function as a detector in real-time. Since the DCA can be seen as many such cells operating in parallel, it is potentially capable of performing real-time detection. However, the analysis process of the standard DCA constricts its real-time capability. As a result, we conclude that the analysis process of the standard DCA should be replaced by a real-time analysis component, which can perform periodic analysis for the purpose of real-time detection.

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Section: Discussion Of the Analysis Processmentioning

confidence: 99%

Exploration of the Dendritic Cell Algorithm with the Duration Calculus

Greensmith

Aickelin

2009

SSRN Journal

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“…Diversity is generated within the DC population through the application of lifespans, which limit the amount of information an individual DC object can process. Different DCs are given different limits for their lifespan, which creates a variable time window effect, with different DC objects processing the signal and antigen data sources during different time periods across the analysed time series (Oates et al, 2008). It is postulated that the combination of signal/antigen temporal correlation and diversity of the DC population are responsible for the detection capability of the DCA.…”

Section: Algorithm Overviewmentioning

confidence: 99%

The Dendritic Cell Algorithm for Intrusion Detection

Greensmith

Aickelin

2012

Biologically Inspired Networking and Sensing

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As one of the solutions to intrusion detection problems, Artificial Immune Systems (AIS) have shown their advantages. Unlike genetic algorithms, there is no one archetypal AIS, instead there are four major paradigms. Among them, the Dendritic Cell Algorithm (DCA) has produced promising results in various applications. The aim of this chapter is to demonstrate the potential for the DCA as a suitable candidate for intrusion detection problems. We review some of the commonly used AIS paradigms for intrusion detection problems and demonstrate the advantages of one particular algorithm, the DCA. In order to clearly describe the algorithm, the background to its development and a formal definition are given. In addition, improvements to the original DCA are presented and their implications are discussed, including previous work done on an online analysis component with segmentation and ongoing work on automated data preprocessing. Based on preliminary results, both improvements appear to be promising for online anomaly-based intrusion detection. IntroductionArtificial Immune Systems (AIS) (de Castro and Timmis, 2003) are computer systems inspired by both theoretical immunology and observed immune functions, principles and models, which are applied to real world problems. The human immune system, from which AIS draw inspiration, is evolved to protect the host from a wealth of invading microorganisms. AIS are developed to provide the similar defensive properties within a computing context. Initially AIS were based on simple models of the human immune system. As noted by Stibor et al. (2005), "first generation algorithms", including negative selection and clonal selection do not produce the same high quality performance as the human immune system. These algorithms, negative selection in particular, are prone to problems with scaling and the generation of excessive false alarms when used to solve problems such as network based intrusion detection. Recent AIS use more rigourous and up-to-date immunology and are developed in collaboration with modellers and immunologists. The resulting algorithms are believed to encapsulate the desirable properties of immune systems including robustness, error tolerance, and self-organisation (de Castro and Timmis, 2003).One such "second generation" AIS is the Dendritic Cell Algorithm (DCA) , inspired by the function of the dendritic cells (DCs) of the innate immune system. It incorporates the principles of a key novel theory in immunology, termed the "danger theory" (Matzinger, 2002). This theory suggests that DCs are responsible for the initial detection of invading microorganisms, in addition to the induction of various immune responses against such invaders. An abstract model of natural DC behaviour is used as the foundation of the developed algorithm. The DCA has been successfully applied to numerous computer security related, more specific, 2 intrusion detection problems, including port scan detection , botnet detection (Al-Hammadi et al., 2008) and a classifier for robot secur...

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“…In [12] frequency analysis was used to characterise the behaviour of an individual cell. However, the cells within the population can migrate asynchronously, which makes analysing the algorithm as a whole in the frequency domain challenging [13].…”

Section: The Dcamentioning

confidence: 99%

Geometrical insights into the dendritic cell algorithm

Stibor¹,

Oates

Kendall

et al. 2009

Proceedings of the 11th Annual Conference on Genetic and Evolutionary Computation

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This work examines the dendritic cell algorithm (DCA) from a mathematical perspective. By representing the signal processing phase of the algorithm using the dot product it is shown that the signal processing element of the DCA is actually a collection of linear classifiers. It is further shown that the decision boundaries of these classifiers have the potentially serious drawback of being parallel, severely limiting the applications for which the existing algorithm can be potentially used on. These ideas are further explored using artificially generated data and a novel visualisation technique that allows an entire population of dendritic cells to be inspected as a single classifier. The paper concludes that the applicability of the DCA to more complex problems is highly limited.

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Frequency analysis for dendritic cell population tuning

Cited by 30 publications

References 23 publications

Exploration of the Dendritic Cell Algorithm with the Duration Calculus

Exploration of the Dendritic Cell Algorithm with the Duration Calculus

The Dendritic Cell Algorithm for Intrusion Detection

Geometrical insights into the dendritic cell algorithm

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