Computational intelligence for distributed fault management in networks using fuzzy cognitive maps

Ndousse, T.D.; Okuda, Tetsuji

doi:10.1109/icc.1996.533672

Cited by 57 publications

(28 citation statements)

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“…FCMs have been previously described and used in [3,10,11] to model human knowledge, new and unseen behaviours of particular scenarios or actions. The authors of [3] provide a detailed description of the FCM and its mathematical foundation.…”

Section: Related Workmentioning

confidence: 99%

Adding contextual information to Intrusion Detection Systems using Fuzzy Cognitive Maps

Aparicio-Navarro

Kyriakopoulos

Parish

et al. 2016

2016 IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support (CogSIMA

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Section: Related Workmentioning

confidence: 99%

Adding contextual information to Intrusion Detection Systems using Fuzzy Cognitive Maps

Aparicio-Navarro

Kyriakopoulos

Parish

et al. 2016

2016 IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support (CogSIMA

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“…In these methods, first, a set of rules for detecting anomalous behavior is extracted. If newly-captured behavior fits one of the rules, it is considered to be an anomaly [Ndousse and Okuda, 1996] [Sajja and Akerkar, 2010]. In the first step of rule-based methods, an algorithm such as decision tree, RIPPER and etc.…”

Section: Rule-based Methodsmentioning

confidence: 99%

Incremental anomaly detection using two-layer cluster-based structure

Bigdeli

Mohammadi

Raahemi

et al. 2018

Information Sciences

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Anomaly detection algorithms face several challenges, including processing speed and dealing with noise in data. In this thesis, a two-layer clusterbased anomaly detection structure is presented which is fast, noise-resilient and incremental. In this structure, each normal pattern is considered as a cluster, and each cluster is represented using a Gaussian Mixture Model (GMM). Then, new instances are presented to the GMM to be labeled as normal or abnormal.The proposed structure comprises three main steps. In the first step, the data are clustered. The second step is to represent each cluster in a way that enables the model to classify new instances. The Summarization based on Gaussian Mixture Model (SGMM) proposed in this thesis represents each cluster as a GMM.In the third step, a two-layer structure efficiently updates clusters using In most real-time anomaly detection applications, incoming instances are often similar to previous ones. In these cases, there is no need to update clusters based on duplicates, since they have already been modeled in the cluster distribution. The two-layer structure is responsible for identifying redundant instances. In this structure, redundant instance are ignored, and the remaining new instances are used to update clusters. Ignoring redundant instances, which are typically in the majority, makes the detection phase fast.Each part of the general structure is validated in this thesis. The experiments include, detection rates, clustering goodness, time, memory usage and the complexity of the algorithms. The accuracy of the clustering and summarization of clusters using GMMs is evaluated, and compared to that of other methods. Using Davies-Bouldin (DB) and Dunn indexes, the distances for original and regenerated clusters using GMMs is almost zero with SGMM method while this value for ABACUS is around 0.01. Moreover, the results show that the SGMM algorithm is 3 times faster than ABACUS in running time, using one-third of the memory used by ABACUS.The CPL method, used to label new instances, is found to collectively remove the effect of noise, while increasing the accuracy of labeling new instances. In a noisy environment, the detection rate of the CPL method is 5% higher than other algorithms such as one-class SVM. The false alarm iii rate is decreased by 10% on average. Memory use is 20 times lesser that that of the one-class SVM.The proposed method is found to lower the false alarm rate, which is one of the basic problems for the one-class SVM. Experiments show the false alarm rate is decreased from 5% to 15% among different datasets, while the detection rate is increased from 5% to 10% in different datasets with twolayer structure. The memory usage for the two-layer structure is 20 to 50 times less than that of one-class SVM. One-class SVM uses support vectors in labeling new instances, while the labeling of the two-layer structure depends on the number of GMMs. The experiments show that the two-layer structure is 20 to 50 times faster than the one-class SVM in labelin...

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“…The authors of [4] provide a detailed description of the FCM and its mathematical foundation. The authors in [7] provide a detailed description of an FCM and examples that use an FCM to model fault propagation in interconnected systems.…”

Section: Related Workmentioning

confidence: 99%

“…However, in contrast to FCM, none of these techniques is able to model the influence of the different states/events in the overall system. FCMs have been previously described and used in [4,7] to model human knowledge, together with new and unseen behaviours of particular scenarios. The authors of [4] provide a detailed description of the FCM and its mathematical foundation.…”

Section: Related Workmentioning

confidence: 99%

Using the pattern-of-life in networks to improve the effectiveness of intrusion detection systems

Aparicio-Navarro

Chambers

Kyriakopoulos

et al. 2017

2017 IEEE International Conference on Communications (ICC)

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Computational intelligence for distributed fault management in networks using fuzzy cognitive maps

Cited by 57 publications

References 0 publications

Adding contextual information to Intrusion Detection Systems using Fuzzy Cognitive Maps

Adding contextual information to Intrusion Detection Systems using Fuzzy Cognitive Maps

Incremental anomaly detection using two-layer cluster-based structure

Using the pattern-of-life in networks to improve the effectiveness of intrusion detection systems

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