Cooperative Automated Worm Response and Detection ImmuNe ALgorithm (CARDINAL) Inspired by T-Cell Immunity and Tolerance

Kim, Jungwon; Wilson, William; Aickelin, Uwe; McLeod, Julie

doi:10.2139/ssrn.2832002

Cited by 18 publications

(23 citation statements)

References 11 publications

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“…Related work by Greensmith et al [5] employed DCs within AIS that coordinated T-cell immune responses. Kim et al [11] continued Greensmith et al's work by discussing T-cell immunity and tolerance for computer worm detection. This work presented how three different processes within the function of T-cells, namely T-cell maturation, differentiation and proliferation could be embedded within the Danger Theory-based AIS.…”

Section: Previous Workmentioning

confidence: 94%

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Danger Is Ubiquitous: Detecting Malicious Activities in Sensor Networks Using the Dendritic Cell Algorithm

Kim

Bentley

Wallenta

et al. 2006

Lecture Notes in Computer Science

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Abstract. There is a list of unique immune features that are currently absent from the existing artificial immune systems and other intelligent paradigms. We argue that some of AIS features can be inherent in an application itself, and thus this type of application would be a more appropriate substrate in which to develop and integrate the benefits brought by AIS. We claim here that sensor networks are such an application area, in which the ideas from AIS can be readily applied. The objective of this paper is to illustrate how closely a Danger Theory based AIS -in particular the Dendritic Cell Algorithm matches the structure and functional requirements of sensor networks. This paper also introduces a new sensor network attack called an Interest Cache Poisoning Attack and discusses how the DCA can be applied to detect this attack.

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Section: Previous Workmentioning

confidence: 94%

“…The second stage of the job involves sending immune cells and signals between the nodes of the sensor network. This may be performed by a different immune inspired algorithm such as the one introduced in [11]. This paper focuses on the first stage.…”

Section: Using the DC Algorithm To Detect Interest Cache Poisoningmentioning

confidence: 99%

Danger Is Ubiquitous: Detecting Malicious Activities in Sensor Networks Using the Dendritic Cell Algorithm

Kim

Bentley

Wallenta

et al. 2006

Lecture Notes in Computer Science

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“…The TLRA was deployed in the libtissue framework to detect process anomaly [267,268,269]. Kim et al in [177] emulated interactions between DCs and T cells in the CARDINAL (Cooperative Automated worm Response and Detection ImmuNe ALgorithm), too. However, T cells in CARDINAL will differentiate into various effector T cells, such as helper T cells and cytotoxic T cells.…”

Section: Danger Theorymentioning

confidence: 99%

“…Self and nonself [15], [129], [127], [130], [125], [128], [126], [176], [258] T Cells and Dendritic Cells TLR Algorithm Self [177], [267], [269], [157], [268] a a…”

Section: Algorithmmentioning

confidence: 99%

The use of computational intelligence in intrusion detection systems: A review

Banzhaf

2010

Applied Soft Computing

568

248

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Intrusion detection based upon computational intelligence is currently attracting considerable interest from the research community. Characteristics of computational intelligence (CI) systems, such as adaptation, fault tolerance, high computational speed and error resilience in the face of noisy information fit the requirements of building a good intrusion detection model. Here we want to provide an overview of the research progress in applying CI methods to the problem of intrusion detection. The scope of this review will be on core methods of CI, including artificial neural networks, fuzzy systems, evolutionary computation, artificial immune systems, swarm intelligence, and soft computing. The research contributions in each field are systematically summarized and compared, allowing us to clearly define existing research challenges, and to highlight promising new research directions. The findings of this review should provide useful insights into the current IDS literature and be a good source for anyone who is interested in the application of CI approaches to IDSs or related fields.

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“…During the past decade, the artificial immune networks have been successfully applied to a large variety of engineering areas, such as data mining [32], time series prediction, pattern recognition [33], optimization, fault detection [30], computer security [31], and process control. In this section, a few examples of these applications are demonstrated and discussed.…”

Section: Applications Of Artificial Immune Networkmentioning

confidence: 99%

Artificial Immune Networks: Models and Applications

Shen

Gao²,

Bie

et al. 2006

2006 International Conference on Computational Intelligence and Security

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Artificial Immune Systems (AIS), which is inspired by the nature immune system, has been applied for solving complex computational problems in classification, pattern recognition, and optimization. In this paper, the theory of the natural immune system is first briefly introduced. Next, we compare some well-known AIS and their applications. Several representative artificial immune networks models are also discussed. Moreover, we demonstrate the applications of artificial immune networks in various engineering fields.

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Cooperative Automated Worm Response and Detection ImmuNe ALgorithm (CARDINAL) Inspired by T-Cell Immunity and Tolerance

Cited by 18 publications

References 11 publications

Danger Is Ubiquitous: Detecting Malicious Activities in Sensor Networks Using the Dendritic Cell Algorithm

Danger Is Ubiquitous: Detecting Malicious Activities in Sensor Networks Using the Dendritic Cell Algorithm

The use of computational intelligence in intrusion detection systems: A review

Artificial Immune Networks: Models and Applications

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