Architecture for an Artificial Immune System

By S. Hofmeyr, Stephanie Forrest. Evolutionary Computation, 2000

959 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Computer Networks and Communications, Public Health, Environmental and Occupational Health

View Paper: https://www.semanticscholar.org/paper/0247d6e7370f43de4e764c7aa14f80bb73fa9215

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In Biomedical Engineering

In Computer Networks and Communications

In Public Health, Environmental and Occupational Health

Citations in the corpus

Artificial Immune Recognition System (AIRS): An Immune-Inspired Supervised Learning Algorithm (2004, background): “Some of these aspects, such as the distinction between self and non-self and the concept of negative selection, have a natural and intuitive fit for applications involving computer security, network intrusion detection [16], [ 20 ], [23], change detection, and the like.”
Theoretical advances in artificial immune systems (2008, background): “The values are chosen as proposed in [25].”
Immune system approaches to intrusion detection – a review (2004, methodology): “Early work from this group is described in Forrest et al [34], and aims to build an intrusion detection system based on the notion of self within a computer system.”
Anomaly Detection Using Real-Valued Negative Selection (2003, methodology): “Accordingly, many approaches have been proposed which include statistical [13], machine learning [28], data mining [29] and immunological inspired techniques [7, 18, 22].”
Generation of a new IDS test dataset: Time to retire the KDD collection (2013, neutral): “A discussion of these results is conducted in Section V, with concluding remarks in Section VI.”
A Review of Anomaly based Intrusion Detection Systems (2011, methodology): “This has now become a tool for research which studied well and applied in solving problems in the computer security field and particularly to detect the viruses [37] in computers and also network intruders [38].”
A survey on bio-inspired networking (2010, background): “inspired by the principles and processes of the mammalian immune system [25], is to efficiently detect changes in the environment or deviations from the normal system behavior in complex problems domains.”
Fundamentals of natural computing: an overview (2007, neutral): “, [104,120,141]).”
An immunity-based technique to characterize intrusions in computer networks (2002, methodology): “The idea of using immunological principles in computer security [8], [10], [19], [20] has progressed since 1994.”
Artificial Immune Systems (2009, positive): “case of Intrusion Detection as solved by Hofmeyr and Forrest (2000). One way of solving this problem is by defining a set of ‘self’, i.”
A survey of autonomic communications (2006, background): “…been developed which very closely model elements and processes of natural immune systems, such as lymphocytes, their generation, maturation, circulation, binding to pathogens, and activation, as well as both primary and secondary immune responses [Hofmeyr and Forrest 2000; Esponda et al. 2004].”
Complex systems: Network thinking (2006, neutral): “In addition to the long-standing areas of neural- and evolutionary-inspired computation, several new nature-inspired techniques have been developed over the past decade or so: ant-colony optimization [13], or more generally swarm intelligence [7]; computer and network immune systems [14,19]; economi”
A Semantic Approach to Host-Based Intrusion Detection Systems Using Contiguousand Discontiguous System Call Patterns (2014, neutral): “Forrest’s methodology [20], [25], [26], [46] and [47], where the maximum window length allowed is in fact the length of each trace.”
Advances in artificial immune systems (2006, positive): “Artificial Immune Systems (AIS) are being used in many applications such as anomaly detection [37], [59], pattern recognition [36], data mining [38], computer security [6], [7], [25], [63], [65], adaptive control [39] and fault detection [1], [60].”