A Negative Selection Approach to Intrusion Detection

Mostardinha, Patricia; Faria, Bruno; Zúquete, André; Abreu, F. Vistulo de

doi:10.1007/978-3-642-33757-4_14

Cited by 14 publications

(4 citation statements)

References 14 publications

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“…However, its impact in the anomaly detection performance of the algorithm is also reduced provided f is not too small (see S9 Fig). The f parameter was first introduced in [58] to take into account knowledge of the typical pairing durations observed in the calibration stage. Since detection mechanisms involve the number of long lived pairings, it could be expected that only those agents performing the longest pairings should be considered.…”

Section: Resultsmentioning

confidence: 99%

“…Therefore, the algorithm proposed here reduced the complexity in initial proposals [3, 43, 58] by eliminating the need of using the calibration stage and by reducing the number of parameters that one should tune to only one. It should be mentioned, however, that these conclusions are restricted to semi-supervised anomaly detection applications with stationary data.…”

Section: Discussionmentioning

confidence: 99%

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Cellular frustration algorithms for anomaly detection applications

Faria

Abreu

2019

PLoS ONE

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Cellular frustrated models have been developed to describe how the adaptive immune system works. They are composed by independent agents that continuously pair and unpair depending on the information that one sub-set of these agents display. The emergent dynamics is sensitive to changes in the displayed information and can be used to detect anomalies, which can be important to accomplish the immune system main function of protecting the host. Therefore, it has been hypothesized that these models could be adequate to model the immune system activation. Likewise it has been hypothesized that these models could provide inspiration to develop new artificial intelligence algorithms for data mining applications. However, computational algorithms do not need to follow strictly the immunological reality. Here, we investigate efficient implementation strategies of these immune inspired ideas for anomaly detection applications and use real data to compare the performance of cellular frustration algorithms with standard implementations of one-class support vector machines and deep autoencoders. Our results demonstrate that more efficient implementations of cellular frustration algorithms are possible and also that cellular frustration algorithms can be advantageous for semi-supervised anomaly detection applications given their robustness and accuracy.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Cellular frustration algorithms for anomaly detection applications

Faria

Abreu

2019

PLoS ONE

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“…Methods based on negative/positive selection are typically used for classification and pattern recognition problems (e.g., anomaly detection [ 109 ]). In anomaly-based IDS, the pathogens represent the potential attacks, and the antibodies are a way to identify that attacks [ 110 ].…”

Section: Classical Ais Theories and Their Applicationsmentioning

confidence: 99%

A Review on Immune-Inspired Node Fault Detection in Wireless Sensor Networks with a Focus on the Danger Theory

Widhalm

Goeschka

Kästner

2023

Sensors

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The use of fault detection and tolerance measures in wireless sensor networks is inevitable to ensure the reliability of the data sources. In this context, immune-inspired concepts offer suitable characteristics for developing lightweight fault detection systems, and previous works have shown promising results. In this article, we provide a literature review of immune-inspired fault detection approaches in sensor networks proposed in the last two decades. We discuss the unique properties of the human immune system and how the found approaches exploit them. With the information from the literature review extended with the findings of our previous works, we discuss the limitations of current approaches and consequent future research directions. We have found that immune-inspired techniques are well suited for lightweight fault detection, but there are still open questions concerning the effective and efficient use of those in sensor networks.

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“…Anomaly detection technique behavior is quite old, it is also used to detect suspicious behavior in telephony. The main idea is to model, during a learning period, the "normal" behavior of a system/program/user, defining a line of conduct (profile), and then considering (detection phase) as suspect any unusual behavior (significant deviations compared to the model of "normal" behavior) [20].…”

Section: Intrusion Detectionmentioning

confidence: 99%

Immunological Approach for Intrusion Detection

Zekri

Souici-Meslati

2014

Revue Africaine De Recherche en Informatique Et Mathématiques Appliquées

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International audience One of the central challenges with computer security is determining the difference between normal and potentially harmful behavior. For decades, developers have protected their systems using classical methods. However, the growth and complexity of computer systems or networks to protect require the development of automated and adaptive defensive tools. Promising solutions are emerging with biological inspired computing, and in particular, the immunological approach. In this paper, we propose two artificial immune systems for intrusion detection using the KDD Cup'99 database. The first one is based on the danger theory using the dendritic cells algorithm and the second is based on negative selection. The obtained results are promising

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A Negative Selection Approach to Intrusion Detection

Cited by 14 publications

References 14 publications

Cellular frustration algorithms for anomaly detection applications

Cellular frustration algorithms for anomaly detection applications

A Review on Immune-Inspired Node Fault Detection in Wireless Sensor Networks with a Focus on the Danger Theory

Immunological Approach for Intrusion Detection

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