Fuzzy cognitive maps for decision support in an intelligent intrusion detection system

Siraj, Ambareen; Bridges, Susan M.; Vaughn, Rayford B.

doi:10.1109/nafips.2001.944405

Cited by 58 publications

(26 citation statements)

References 11 publications

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“…The reader can refer to [44] and [45] for details about CM early usages, and to [46], [47], [48] and [9] for more recent studies. Several applications were developed, reported in [49], [50], [51], [52], [53], [54], [55], [56], [57], [58], [59], [60], [61], etc.…”

Section: Fuzzy Cognitive Maps As Modelling Techniquementioning

confidence: 99%

Tackling Travel Behaviour: An approach based on Fuzzy Cognitive Maps

León¹,

Nápoles²,

Bello³

et al. 2013

IJCIS

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Although the individuals" transport behavioural modelling is a complex task, it can produce a notable social and economic impact. In this paper, Fuzzy Cognitive Maps are explored to represent the behaviour and operation of such complex systems. An automatic approach to extract mental representations from individuals and convert them into computational structures is defined. For the creation of knowledge bases the use of Knowledge Engineering is accounted and later on the data is transferred into structures based on Fuzzy Cognitive Maps. Once the maps are created, their performances get improved through the use of a Particle Swarm Optimisation algorithm as a learning method, readjusting its predicting capacity from stored scenarios, where individuals left their preferences in front of random situations. Another important result is clustering the maps for knowledge discovery. This permits to find useful groups of individuals that policymakers can use for simulating new rules and policies. After related maps are identified, to merge them as a unique structure could benefit for different usages. Therefore an aggregating procedure is elaborated for this task, constituting an alternative approach for selecting a centroid of a specific estimated group, and therefore having, in only one structure, the knowledge and behavioural acting from a collection of individuals. Learning, clustering and aggregation of Fuzzy Cognitive Maps are combined in a cascade experiment, with the intention of describing travellers" behaviour and change trends in different abstraction levels. The results of this approach will help transportation policy decision makers to understand the people"s needs in a better way, consequently will help them actualising different policy formulations and implementations.

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Section: Fuzzy Cognitive Maps As Modelling Techniquementioning

confidence: 99%

Tackling Travel Behaviour: An approach based on Fuzzy Cognitive Maps

León¹,

Nápoles²,

Bello³

et al. 2013

IJCIS

View full text Add to dashboard Cite

show abstract

“…The application of fuzzy inference rules in expert domains is a common knowledge and FCM has been applied to a wide variety of strategic domains [8][9][10][11] . In this illustration, fuzzy inference and cognitive systems have been presented in interconnected perspective to trace the critical path of a research organisation.…”

Section: Illustrationmentioning

confidence: 99%

Fuzzy Cognitive Maps for Identifying Critical Path in Strategic Domains

Banerjee¹

2009

DSJ

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Fuzzy cognitive maps (FCM) are hybrid tools of artificial neural network and fuzzy logic systems. One of their major uses is in decision support systems. To arrive at correct decisions, the fuzzy interconnections between attributes are either trained or assigned by domain experts. The network target may be fixed or unknown. In this paper, the FCM has been illustrated in a strategic domain in which the target is fuzzily defined and unknown. The target value is therefore estimated first by fuzzy inference rules for a collection of imprecisely defined attributes and then the fuzzy gradation to which the value belongs is used as the basis to tune the cognitive map The domain concerns a research institution. The target risk is the time by which the projects run-off beyond the stipulated time of completion. The paper shows that certain instinctively chosen membership functions to tune the cognitive map are able to reproduce the belief surrounding the criticality of the domain.

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“…Researchers have used FCMs for many tasks in several different domains. Use of FCMs was first reported in [11] for fusing alert information in a multi-sensor intrusion detection environment to assess network health. Fuzzy Intrusion Recognition Engine, a network based IDS, also use FCMs in detecting attacks from features extracted from network traffic [12].…”

Section: Related Workmentioning

confidence: 99%

“…Fuzzy Intrusion Recognition Engine, a network based IDS, also use FCMs in detecting attacks from features extracted from network traffic [12]. The work that we present in this paper differs from our previous work [11] primarily in the focus of the research, which is discovery of causal relationships between alerts rather than structural relationships and in the use of abstract FCM models to address issues of scalability and uncertainly.…”

Section: Related Workmentioning

confidence: 99%

A Cognitive Model for Alert Correlation in a Distributed Environment

Siraj¹,

Vaughn²

2005

Intelligence and Security Informatics

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Abstract. The area of alert fusion for strengthening information assurance in systems is a promising research area that has recently begun to attract attention. Increased demands for "more trustworthy" systems and the fact that a single sensor cannot detect all types of misuse/anomalies have prompted most modern information systems deployed in distributed environments to employ multiple, diverse sensors. Therefore, the outputs of the sensors must be fused in an effective and intelligent manner in order to provide an overall view of the status of such systems. A unified architecture for intelligent alert fusion will essentially combine alert prioritization, alert clustering and alert correlation. In this paper, we address the alert correlation aspect of sensor data fusion in distributed environments. A causal knowledge based inference technique with fuzzy cognitive modeling is used to correlate alerts by discovering causal relationships in alert data.

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Fuzzy cognitive maps for decision support in an intelligent intrusion detection system

Cited by 58 publications

References 11 publications

Tackling Travel Behaviour: An approach based on Fuzzy Cognitive Maps

Tackling Travel Behaviour: An approach based on Fuzzy Cognitive Maps

Fuzzy Cognitive Maps for Identifying Critical Path in Strategic Domains

A Cognitive Model for Alert Correlation in a Distributed Environment

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