What is the output context value of an individual DC?

The output context value of an individual DC is reduced to one factor, k̄, which negative numbers indicate a safe context and positive numbers indicating analogous to the previously used mature context.

What are the requirements for the deterministic version of the DCA?

In order to produce the deterministic version, it is necessary to make a number of assumptions and modifications to the original DCA:– Both signals and antigen are required for the system to correctly function.

How long does the MCAV take to be able to show improved results?

improved results are shown with a delay of 2-4 seconds - which is equivalent to the average number of cell iterations per lifespan.

How many cells are used in the experiments described?

Unless specified otherwise, all experiments described use 100 artificial DCs with a maximum lifespan limit of 100 csm signal units.

What is the effect of the dDCA on the pts graph?

These results suggest that the dDCA has the potential to be error tolerant to at least a five second lag in signal data, which is a desirable property for any behaviour based anomaly detection approach, as this reflects the situation often seen in real world intrusion data.

What are the two optimizations of the DCA used in this paper?

Their researchalso contains two optimizations of the DCA which are used in this paper, namely a real valued representation of individual DC output and tissue centric processing of signals.

What are the advantages of the deterministic version of the DCA?

This version has several advantages, including the ability to replay experiments exactly, predictability of output and the reduction in the number of parameters required.

What is the effect of the pts process on the MCAV?

This could indicate that the pts process exhibits minor fluctuations around this point, with these fluctuations amplified by the binary classification of cells used in the MCAV, with Kα showing to be more sensitive to encapsulating such fluctuations.

What is the purpose of this paper?

The aim of this paper is to describe, implement, and test a deterministic DCA (dDCA) to uncover its inner relationships and function.

What was the use of probabilistic elements for the initial development of the DCA?

The use of various probabilistic elements was in part an artifact of the use of the Twycross’ libtissue framework for the initial algorithm development.

What is the result of the timed experiments?

The results of the timed experiments are also encouraging, giving that the relationship between the number of cells and the execution time appears to be linear.

Open AccessBook Chapter10.1007/978-3-540-85072-4_26

The Deterministic Dendritic Cell Algorithm

Julie Greensmith, +1 more

- 10 Aug 2008

- pp 291-302

148

TL;DR: A deterministic version of the algorithm is proposed, implemented and tested using a port scan dataset to provide a controllable system and a novel metric for the assessment of the algorithms output proves to be a more sensitive metric than the metric used with the original Dendritic Cell Algorithm.

Abstract: 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.

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Most frequently asked questions

1. What are the two signal categories required for the DCA to function correctly?

At minimum two signal categories are required, an activating signal and an inhibitory signal - the danger and safe signal respectively.

2. What is the aim of the dDCA for these experiments?

The aim of the dDCA for these experiments is to produce high MCAV and Kα for the nmap and pts with lower values for the bash and sshd processes.

3. Why is a reduction in parameters required from the original DCA?

To provide reproducibility and for the ease of sensitivity a reduction in parameters is required from those used with the original DCA.

4. How many parameters were used in the previous versions of the DCA?

Previous versions of the DCA featured in excess of 10 parameters, each of which were derived from empirical biological observation and through sensitivity analysis.

Fig. 1. The input signal data is displayed in (a) with results for both series of experiments given in (b) to (f). Figures (b) and (c) show the MCAV and Kα values across a range of cell numbers plotted on a log-scale, (d) shows the execution times for varying the cell numbers, with (e) and (f) showing the MCAV andKα with varying time delays.

Table 1. MCAVs produced for dDCA versus Original DCA (mean of 3 runs).

Citations

Journal Article•10.1016/J.INS.2013.02.041

A survey on optimization metaheuristics

Ilhem Boussaïd, +2 more

- 01 Jul 2013

- Information Sciences

TL;DR: The components and concepts that are used in various metaheuristics are outlined in order to analyze their similarities and differences and the classification adopted in this paper differentiates between single solution based metaheURistics and population based meta heuristics.

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Journal Article•10.1109/TEVC.2011.2173580

An Artificial Immune System for Classification With Local Feature Selection

Grzegorz Dudek

- 01 Dec 2012

- IEEE Transactions on Evolutionary Comput...

TL;DR: A new multiclass classifier based on immune system principles is proposed, which has the embedded property of local feature selection, and shows good performance in comparison with both other immune inspired classifiers and other classifiers in general.

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Journal Article•10.1007/S10115-015-0891-Y

A survey of the dendritic cell algorithm

Zeineb Chelly, +1 more

- 01 Sep 2016

- Knowledge and Information Systems

TL;DR: This study aims at summarizing the powerful characteristics of the DCA as well as making a general review of it.

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Journal Article•10.1007/S00500-019-04155-4

A comparative review of meta-heuristic approaches to optimize the SLA violation costs for dynamic execution of cloud services

Ajay Kumar, +1 more

- 01 Mar 2020

TL;DR: The Quality of Service criteria, SLA penalty cost and the cloud-domain-specific constraints have been mathematically formulated in this paper to optimize the service-level agreement (SLA) violation cost in cloud computing.

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•Journal Article•10.1016/J.BIOSYSTEMS.2013.01.001

Theoretical formulation and analysis of the deterministic dendritic cell algorithm

Feng Gu, +2 more

- 01 Feb 2013

- BioSystems

TL;DR: This paper defines the deterministic version of the DCA, named the dDCA, using set theory and mathematical functions, and proposes two runtime variables of the algorithm, formulated based on the input data, to understand its runtime behaviour as guidelines for further development.

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...

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References

Journal Article•10.1016/S1471-4906(02)02281-0

Immature, semi-mature and fully mature dendritic cells: which signals induce tolerance or immunity?

Manfred B. Lutz, +1 more

- 01 Sep 2002

- Trends in Immunology

TL;DR: Closer inspection reveals that tolerance is observed when partial- or semi-maturation of DCs occurs, whereas only full DC maturation is immunogenic, and the decisive immunogenic signal seems to be the release of proinflammatory cytokines from the DCs.

...read moreread less

1.5K

•Book Chapter•10.1007/11536444_12

Introducing dendritic cells as a novel immune-inspired algorithm for anomaly detection

Julie Greensmith, +2 more

- 14 Aug 2005

TL;DR: An algorithm is derived based on the functionality ofritic cells, and it is hoped that this algorithm will eventually become the key component within a large, distributed immune system, based on sound immunological concepts.

...read moreread less

333

•Posted Content

Introducing Dendritic Cells as a Novel Immune-Inspired Algorithm for Anomoly Detection

Julie Greensmith, +2 more

- 19 Apr 2010

- arXiv: Artificial Intelligence

TL;DR: In this paper, the authors derived an algorithm based on the functionality of Dendritic cells, and used the signals and differentiation pathways to build a control mechanism for an artificial immune system.

...read moreread less

268

•Proceedings Article•10.1109/CEC.2006.1688374

Dendritic Cells for Anomaly Detection

Julie Greensmith, +2 more

- 11 Sep 2006

TL;DR: The aim of this research is to develop an intrusion detection system based on a novel concept in immunology, the Danger Theory, which is used to detect context switching for a basic machine learning dataset and to detect outgoing portscans in real-time.

...read moreread less

118

•Posted Content

Information Fusion for Anomaly Detection with the Dendritic Cell Algorithm

Julie Greensmith, +2 more

- 03 Mar 2010

- arXiv: Artificial Intelligence

TL;DR: In this article, the authors 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.

...read moreread less

111

The Deterministic Dendritic Cell Algorithm

Chat with Paper

AI Agents for this Paper

Most frequently asked questions

1. What are the two signal categories required for the DCA to function correctly?

2. What is the aim of the dDCA for these experiments?

3. Why is a reduction in parameters required from the original DCA?

4. How many parameters were used in the previous versions of the DCA?

5. What is the output context value of an individual DC?

6. What are the requirements for the deterministic version of the DCA?

7. How long does the MCAV take to be able to show improved results?

8. How many cells are used in the experiments described?

9. What is the effect of the dDCA on the pts graph?

10. What are the two optimizations of the DCA used in this paper?

11. What are the advantages of the deterministic version of the DCA?

12. What is the effect of the pts process on the MCAV?

13. What is the purpose of this paper?

14. What was the use of probabilistic elements for the initial development of the DCA?

15. What is the result of the timed experiments?

Figures

Citations

A survey on optimization metaheuristics

An Artificial Immune System for Classification With Local Feature Selection

A survey of the dendritic cell algorithm

A comparative review of meta-heuristic approaches to optimize the SLA violation costs for dynamic execution of cloud services

Theoretical formulation and analysis of the deterministic dendritic cell algorithm

References

Immature, semi-mature and fully mature dendritic cells: which signals induce tolerance or immunity?

Introducing dendritic cells as a novel immune-inspired algorithm for anomaly detection

Introducing Dendritic Cells as a Novel Immune-Inspired Algorithm for Anomoly Detection

Dendritic Cells for Anomaly Detection

Information Fusion for Anomaly Detection with the Dendritic Cell Algorithm

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