A Feedback Negative Selection Algorithm to Anomaly Detection

Zeng, Jinquan; Li, Tao; Liu, Xiaojie; Liu, Caiming; Peng, Lingxi; Sun, Feixian

doi:10.1109/icnc.2007.28

Cited by 12 publications

(9 citation statements)

References 7 publications

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“…Zheng et al [34] showed a self-adaptable negative selection algorithm known as ANSA. The methodology can create a legitimate profile of the framework just by using a subset of typical components and can alter the collections of self/non-self-space.…”

Section: Sobh and Mostafamentioning

confidence: 99%

Artificial Immune System Based Frameworks and Its Application in Cyber Immune System: A Comprehensive Review

Bejoy¹,

Tv²,

Janakiraman³

2020

jcr

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Computer science has always mixed the concepts of biology and computers to enhance the way in which systems are designed. Artificial Immune System (AIS) is a Computational Intelligence strategy dependent on an organically enlivened computational system that can be utilized for taking care of complex computational issues. It tends to be seen that AIS is an incredibly various locale of research, going from the modeling immune systems to complex algorithms for specific applications. This paper exhibits an exhaustive survey of different frameworks developed in the artificial immune system and its application. Reviews of frameworks in AIS are uncommon and henceforth this paper gives an inside out audit of progressing research and challenges in AIS. We start by presenting AIS and give a thorough survey of different systems in AIS and its application in anomaly detection. We investigate the utilization of AIS in the Intrusion Detection System named the Cyber Immune System(CIS) and compares various AIS works applied to CIS. We conclude with various future extensions in the area of AIS research.

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Section: Sobh and Mostafamentioning

confidence: 99%

Artificial Immune System Based Frameworks and Its Application in Cyber Immune System: A Comprehensive Review

Bejoy¹,

Tv²,

Janakiraman³

2020

jcr

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“…In 2008, Igawa and Ohashi [28] proposed a new negative selection algorithm named Artificial Negative Selection Classifier (ANSC) for multiclass classification. Some other proposed negative selection algorithms can be found in [29], [30], and [31].…”

Section: B Negative Selection Based Algorithmsmentioning

confidence: 99%

Advancement in Artificial Immune Systems: A perspective of models, algorithms and applications

Al-Enezi¹,

Abbod²,

Al-Sharhan

2009

Exhibition

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Artificial Immune Systems (AIS) are a branch of computational intelligence field inspired by the biological immune system. It has gained a lot of interest by researchers during the past decade aiming to develop immune-based models and techniques to solve complex computational or engineering problems. This work presents some applications of AIS techniques to health problems, i.e., cancer prediction, in addition to a thorough survey of existing AIS models and algorithms with a focus on the last five years.

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“…New NS algorithms are continuously being published. For example, a NS algorithm, enhanced by state graphs [204], is able to locate all occurrences of multi-patterns in an input string by just one scan operation; a feedback NS algorithm was proposed to solve the anomaly detection problem [286].…”

Section: Development Of Negative Selection Algorithmsmentioning

confidence: 99%

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

Banzhaf

2010

Applied Soft Computing

561

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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A Feedback Negative Selection Algorithm to Anomaly Detection

Cited by 12 publications

References 7 publications

Artificial Immune System Based Frameworks and Its Application in Cyber Immune System: A Comprehensive Review

Artificial Immune System Based Frameworks and Its Application in Cyber Immune System: A Comprehensive Review

Advancement in Artificial Immune Systems: A perspective of models, algorithms and applications

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

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