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Computational intelligence for distributed fault management in networks using fuzzy cognitive maps
Abstract
This paper proposes a computationally based expert system for
managing fault propagation in internetworks using the concept of fuzzy
cognitive maps (FCM), a graphical archetype which encodes and processes
vague causal reasoning numerically. The dynamic features of FCM are
exploited to characterize the time-varying aspects of network faults,
while its graphical features are used as a framework for representing
the distributed properties of fault propagation. In this scheme, a
network fault due to one or more managed objects induces causal fuzzy
relationships on adjacent objects. This causal relationship is captured
in a matrix which allows causal inference representations to be viewed
as feedback associative memory with computational recall capabilities
... 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. ...
... Although the work presented in [11] does not focus on network security, it comprehensively describes the FCM concept with clear examples. Similarly, the authors in [10] provide a detailed description of an FCM and examples that use FCM to model fault propagation in interconnected systems. ...
... The events C 4-7 define four throughput levels that are considered normal when one of the services is running. The four events C [8][9][10][11] define the periods of time at which the different services are scheduled. Both C 12 and C 13 are the two possible outcome decisions about each state. ...
In the last few years there has been considerable increase in the efficiency of Intrusion Detection Systems (IDSs). However, networks are still the victim of attacks. As the complexity of these attacks keeps increasing, new and more robust detection mechanisms need to be developed. The next generation of IDSs should be designed incorporating reasoning engines supported by contextual information about the network, cognitive information from the network users and situational awareness to improve their detection results. In this paper, we propose the use of a Fuzzy Cognitive Map (FCM) in conjunction with an IDS to incorporate contextual information into the detection process. We have evaluated the use of FCMs to adjust the Basic Probability Assignment (BPA) values defined prior to the data fusion process, which is crucial for the IDS that we have developed. The results that we present verify that FCMs can improve the efficiency of our IDS by reducing the number of false alarms, while not affecting the number of correct detections.
... • Rule based approaches Early work in the area of fault or anomaly detection was based on expert systems. In the expert systems approach, an exhaustive database containing the rules of behavior exhibited by a faulty system is used to determine fault occurrence [33], [27]. In practice, this entails matching predefined rules of network anomalies to observed sequences of behavior. ...
... In practice, this entails matching predefined rules of network anomalies to observed sequences of behavior. Rule based expert systems are not only too slow for real time applications, but also depend on prior knowledge about the fault conditions of the network [33], [11], [37], [8]. The identification of faults in this approach depends on symptoms that are specific to a particular manifestation of a fault [42]. ...
... (FOCALE) is a network architecture that is designed for both legacy devices, applications and next generation cognitive network devices [33]. • The Inference Plane [13] was created in response to some of the shortcomings of the Knowledge Plane. ...
SUMMARY We describe an efficient failure prediction system based on new algorithms that model and detect anomalous behaviors using multi-scale trend analysis of multiple network parameters. Our approach enjoys many advantages over prior approaches. By operating at multiple timescales simultaneously, the new system achieves robustness against unreliable, redundant, incomplete and contradictory information. The algorithms employed operate with low time complexity, making the system scalable and feasible in real-time environments. Anomalous behaviors identified by the system can be stored efficiently with low space complexity, making it possible to operate with minimal resource requirements even when processing high-rate streams of network parameter values. The developed algorithms generate accurate failure predictions quickly, and the system can be deployed in sa distributed setting. Prediction quality can be improved by considering larger sets of network parameters, allowing the approach to scale as network complexity increases. The system is validated by experiments that demonstrate its ability to produce accurate failure predictions in an efficient and scalable manner. Copyright © 2013 John Wiley & Sons, Ltd.
... A technique that also provides the capability of integrating contextual information from the network user to the detection process is the FCM. FCMs have been previously described and used in [6], [11], and [12] to model human knowledge. Stylios and Groumpos [6] provide a detailed description of the FCM and its mathematical foundation. ...
... Although the work presented in [12] does not focus on network security, it comprehensively describes the FCM concept with clear examples. Similarly, Ndousse and Okuda [11] provide a detailed description of an FCM and examples that use an FCM to model fault propagation in interconnected systems. ...
As the complexity of cyber-attacks keeps increasing, new robust detection mechanisms need to be developed. The next generation of Intrusion Detection Systems (IDSs) should be able to adapt their detection characteristics based not only on the measureable network traffic, but also on the available highlevel information related to the protected network. To this end, we make use of the Pattern-of-Life (PoL) of a computer network as the main source of high-level information. We propose two novel approaches that make use of a Fuzzy Cognitive Map (FCM) to incorporate the PoL into the detection process. There are four main aims of the work. First, to evaluate the efficiency of the proposed approaches in identifying the presence of attacks. Second, to identify which of the proposed approaches to integrate an FCM into the IDS framework produces the best results. Third, to identify which of the metrics used in the design of the FCM produces the best detection results. Fourth, to evidence the improved detection performance that contextual information can offer in IDSs. The results that we present verify that the proposed approaches improve the effectiveness of our IDS by reducing the total number of false alarms; providing almost perfect Detection Rate (i.e. 99.76%), and only 6.33% False Positive Rate, depending on the particular metric combination. CCBY
... Edge values can be positive or negative depending on the nature and direction of effect. C i e ij C J Researchers have used FCMs for many tasks in several different domains [17, 20, 23, 24, 27]. In this research effort we have found a novel use of FCMs for decision support in the domain of network security and intrusion detection. ...
... Researchers have used FCMs for many tasks in several different domains [17,20,23,24,27]. In this research effort we have found a novel use of FCMs for decision support in the domain of network security and intrusion detection. ...
Most modern intrusion detection systems employ multiple intrusion sensors to maximize their trustworthiness. The overall security view of the multi-sensor intrusion detection system can serve as an aid to appraise the trustworthiness in the system. This paper presents our research effort in that direction by describing a decision engine for an intelligent intrusion detection system (IIDS) that fuses information from different intrusion detection sensors using an artificial intelligence technique. The decision engine uses fuzzy cognitive maps (FCMs) and fuzzy rule-bases for causal knowledge acquisition and to support the causal knowledge reasoning process. In this paper, we report on the workings of the decision engine that has been successfully embedded into the IIDS architecture being built at the Center for Computer Security Research (CCSR), Mississippi State University.
... and diverse applications in a variety of engineering domains (analysis of electrical circuits, fault diagnosis, manufacturing organization, systems control, context dependent processing systems) and decision making (stock investment, strategic planning, modeling of organizational behavior, educational, social and psychological systems, games, virtual worlds) [6] [7] [9] [10] [11] [15] [17] [21] [22] [26] [31] [32] [34] [37] [38] [40] [41] [43] [44] [47] [50] support the aforementioned statement. However, the FCM background remains an open field of analytical research mostly because of the existence of weaknesses, such as the abstract estimation of initial concept values, the lack of an efficient mechanism for the development and fine-tuning of the maps, and the questionable reasoning in case of parallel stimulations (multi-stimulus situations). ...
... The most pronounced of such features are the flexibility concerning system design and control, the comprehensible (white-box) structure and operation, the adaptability to problem-specific prerequisites and the capability for abstractive representation and fuzzy reasoning. Many theoretical studies [1, 18, 19, 20, 23, 24, 28, 30, 33, 39, 42, 46, 48, 49] and diverse applications in a variety of engineering domains (analysis of electrical circuits, fault diagnosis, manufacturing organization, systems control, context dependent processing systems) and decision making (stock investment, strategic planning, modeling of organizational behavior, educational, social and psychological systems, games, virtual worlds) [6, 7, 9, 10, 11, 15, 17, 21, 22, 26, 31, 32, 34, 37, 38, 40, 41, 43, 44, 47, 50] support the aforementioned statement. However, the FCM background remains an open field of analytical research mostly because of the existence of weaknesses, such as the abstract estimation of initial concept values, the lack of an efficient mechanism for the development and fine-tuning of the maps, and the questionable reasoning in case of parallel stimulations (multi-stimulus situations). ...
Fuzzy Cognitive Maps (FCMs) have gradually emerged as a powerful modeling and simulation technique applicable to numer- ous research and application fields. This primarily springs from the inherent capabilities of the conventional Cognitive Maps (CMs), most important of which are the flexibility, the abstractive reasoning and the white-box inference engine; however, extensions to the underlying theory are more than anything needed because of the feeble mathe- matical structure of FCMs and mostly the desire to assign advanced characteristics not met in other computational methodologies. Under this standpoint, four core issues are discussed and respective solutions are proposed; the first one concerns the case of multi-stimulus situ- ations (parallel stimulation of many FCM concepts), the second one focuses on the design of a learning algorithm (using evolution strate- gies), and finally the generic real-world phenomena of conditional ef- fects and synergies are properly modeled to support the inference mechanism of FCMs. Copyright c ∞2002 Yang's Scientific Research
... This article is from Current Science Vol. 87,No. 4,25 August 2004 [7]. ...
... Due to their ease to construct and use, their flexibility and adaptation in applying to any problem domain, support on uncertain knowledge, ability to execute fast, relatively simple and comprehensible modeling philosophy which is very close to human reasoning, they have the capability to handle the complex issues efficiently in different domains (Papageorgiou, 2013). Therefore, FCM has become popular and found large applicability in many diverse application areas including medical diagnosis (Papageorgiou et al., 2008Subramanian et al., 2015), modeling of plant control (Gotoh et al., 1989), analysis of electrical circuits (Styblinski and Meyer, 1991), fault managing in networks (Ndousse and Okuda, 1996), modeling of political affairs in South Africa (Khan and Quaddus, 2004), coconut yield management , etc. In addition, FCM has been applied by Papageorgiou et al. (2010Papageorgiou et al. ( , 2011Papageorgiou et al. ( , 2013 in precision agriculture to model the relationship between the factors influencing the cotton yield and analyze their cause -effect relationships since it has been already proved as a successful modeling methodology for many real life applications. ...
www.sciencedirect.com/science/article/pii/S0168169916303088
... Due to their ease to construct and use, their flexibility and adaptation in applying to any problem domain, support on uncertain knowledge, ability to execute fast, relatively simple and comprehensible modeling philosophy which is very close to human reasoning, they have the capability to handle the complex issues efficiently in different domains (Papageorgiou, 2013). Therefore, FCM has become popular and found large applicability in many diverse application areas including medical diagnosis (Papageorgiou et al., 2008Subramanian et al., 2015), modeling of plant control (Gotoh et al., 1989), analysis of electrical circuits (Styblinski and Meyer, 1991), fault managing in networks (Ndousse and Okuda, 1996), modeling of political affairs in South Africa (Khan and Quaddus, 2004), coconut yield management , etc. In addition, FCM has been applied by Papageorgiou et al. (2010Papageorgiou et al. ( , 2011Papageorgiou et al. ( , 2013 in precision agriculture to model the relationship between the factors influencing the cotton yield and analyze their cause -effect relationships since it has been already proved as a successful modeling methodology for many real life applications. ...
... There are many applications for using FCMs such as: analyzing the extend graph theoretical behavior [15], automating human problem solving skills [16], using as behavior models of virtual world [17]. modeling and supporting water control systems [18], designing system model for failure models and effect analysis in process industry [19], strategy planning and analysis for business behavior of automobile industry [20], diagnosis of diseases [6], analysis of electrical circuits [7], fault management in distributed network environment [10], modeling of software development project [8,9], and many others. ...
One of the main issues in developing automatic response systems especially autonomous robots is selecting the best action among all possible actions. Fuzzy Cognitive Maps (FCMs) aim to mimic the reasoning process of the human. FCMs are able to capture and imitate human behavior by describing, developing and representing models. FCMs are also popular for their simplicity and transparency while being successful in a variety of applications. We developed a novel model that could be used for action selection in robots. This model is constructed on a learning FCM which is relied on improved nonlinear Hebbian Algorithm. We tested our model through a series of practical experiments on the latest version of Soccer Server Simulation3D environment. Our tests involved carefully defined factors to measure the team performance. The significance of the proposed model was verified by analysis of variance and independent t-test.
... Gotoh et al. [188] plant control 1991 Styblinski et al. [189] analysis of electrical circuits 1991 Taber [190] disease diagnosis 1992 Kosko [191] political affairs 1993 Dickerson et al. [192] modeling of virtual worlds 1994 Dickerson et al. [193] modeling of virtual worlds 1995 Pelaezet al. [194] analysis of failure modes effects 1996 Ndousseet al. [195] fault management in distributed network environment 1997 ...
Through our four years experiments on students' Scrum based agile software
development (ASD) process, we have gained deep understanding into the human
factors of agile methodology. We designed an agile project management tool -
the HASE collaboration development platform to support more than 400 students
self-organized into 80 teams to practice ASD. In this thesis, Based on our
experiments, simulations and analysis, we contributed a series of solutions and
insights in this researches, including 1) a Goal Net based method to enhance
goal and requirement management for ASD process, 2) a novel Simple Multi-Agent
Real-Time (SMART) approach to enhance intelligent task allocation for ASD
process, 3) a Fuzzy Cognitive Maps (FCMs) based method to enhance emotion and
morale management for ASD process, 4) the first large scale in-depth empirical
insights on human factors in ASD process which have not yet been well studied
by existing research, and 5) the first to identify ASD process as a
human-computation system that exploit human efforts to perform tasks that
computers are not good at solving. On the other hand, computers can assist
human decision making in the ASD process.
... Intelligence of e-Business. Ndousse and Okuda [79] proposed a computationally based expert system for managing fault propagation in internetworks using the concept of fuzzy cognitive maps. ...
Computational intelligence is defined as the study of the design of intelligent agents. Since its relation to other branches of computer science is not well-defined, computational intelligence means different things to different people. In this chapter the history of computational intelligence with a wide literature review will be first given. Then, a detailed classification of the existing methodologies will be made. Later, the international computational intelligence journals will be handled and the characteristics of these journals will be examined. As an example, a special literature review on computational intelligence in complex decision systems will be also given. The direction of computational intelligence in the future will be evaluated.
... Early work in the area of fault detection was based on using rule-based expert systems. In expert systems, a comprehensive database containing the rules of behavior of the faulty system is used to determine if a fault occurred [2], [3]. Rule-based systems are too slow for real-time applications and are dependent on prior knowledge about the fault conditions on the network [4]. ...
... They are also characterized by flexibility of system design and control, comprehensible structure and operation, adaptability to a given domain, and capability of abstract representation and fuzzy reasoning [14]. The FCM models were developed and used in numerous areas of applications such as electrical engineering, medicine, political science, international relations, military science, history, supervisory systems, etc. Examples of specific applications include medical diagnosis [3], analysis of electrical circuits [22], analysis of failure modes effects [18], fault management in distributed network environment [15], modelling and analysis of business performance indicators [7], modelling of supervisors [23], modelling of software development project [24, 25], modelling of plant control [4], modelling of political affairs in South Africa [8] and modelling of virtual worlds [2]. The diversity and number of applications clearly show popularity of this modelling technique, justifying further research to enhance it. ...
Many real world intelligence systems are too complex to be modeled and controlled by traditional mathematical mechanism models. The large amount of data from the systems and their environment is important information which objectively and truthfully reflects the nature of complex dynamic systems. Currently the advanced digital technology has made the digitized data easy to capture and cheap to store. However, raw digital data is rarely useful in practice and the capability to extract information from raw data is extremely important for description and understanding dynamic phenomenon of the data source. The description and control of complex dynamic systems without traditional mathematical mechanism models require new methods that can utilize the existing knowledge, human experience and data. In this paper, applying fuzzy set logic and Fuzzy Cognitive Map (FCM), we propose an innovative data based description and control of complex dynamic systems, which is totally different from the tradition mechanism mathematical model based approaches. In the FCM framework, all necessary information for description and control of the systems is directly extracted from the raw data and human experiences. In other words, as long as a few rough simple human experiences and sufficient data are given, the proposed FCMs can describe and control a complex dynamic system for which the traditional mathematical mechanism models can not be established. As an illustrative example, the simulation results of truck back-upper control problem verify the effectiveness, validity and advantageous characteristics of the learning techniques and control ability of the proposed methodology.
... In FCM's, it is able to represent all types of concepts and express the arcs (edges) connecting these concepts in terms of symbols or numeric values. Over the last ten years, fuzzy cognitive maps have been applied to represent knowledge and artificial inference, such as geographic information systems [18][19][20], fault detection [14,15], policy analysis [17], etc. Although many developments have been achieved recently, progress in the detailed investigation of basic behavior of inference patterns and the analysis has been little. ...
Fuzzy cognitive maps are approaches to knowledge representation and inference that are essential to any intelligent dynamical system. However, the current techniques for constructing fuzzy cognitive maps are inadequate and infeasible in practice. In this paper, we propose the fuzzy cognitive maps based on AFS fuzzy logic, by which we can design and analyze the fuzzy cognitive maps for large-scale intelligent systems. It makes the systematic and theoretical approaches possible. The most important things are that the fuzzy cognitive maps based on AFS fuzzy logic can be transacted by computers easily.
... There are many applications for using FCMs such as: analysing the extend graph theoretical behaviour (Zhang and Chen, 1988), automating human problem solving skills (Juliano, 1990), modelling and supporting water control systems (Gotoh and Yamaguchi, 1991), diagnosis of diseases (Taber, 1991), analysis of electrical circuits (Styblinski and Meyer, 1991), using as behaviour models of virtual world (Dickens and Kosko, 1994), strategy planning and analysis for business behaviour of automobile industry (Tsadiras et al., 1995), designing system model for failure models and effect analysis in process industry (Pelaez and Bowles, 1996), fault management in distributed network environment (Ndousse and Okuda, 1996), modelling of software development project , analysis usability of electronic consumer products (Yucel et al., 2009), assembly design decision support (Cheah et al., 2009), strategic information systems planning process (Nalchigar et al., 2011), evaluating supply chain dependency on critical infrastructures (Ferrari et al.,2011), and many others. ...
One of the main issues in developing automatic response systems especially autonomous robots is selecting the best action among all possible actions. Fuzzy cognitive maps (FCMs) aim to mimic the reasoning process of the human. FCMs are able to capture and imitate human behaviour by describing, developing and representing models. FCMs are also popular for their simplicity and transparency while being successful in a variety of applications. We developed a novel model that could be used for action selection in robots. This model is constructed on a learning FCM which is relied on improved non-linear Hebbian algorithm. We tested our model through a series of practical experiments on the latest version of Soccer Server Simulation 3D environment. Our tests involved carefully defined factors to measure the team performance. Our results showed a significant improvement in overall performance. The significance of the proposed model was verified by analysis of variance (ANOVA).
... Fuzzy set theory is behind the computational theory of FCM. Since Lotfi Zadeh published a paper titled " Fuzzy Sets " [8], the various applications using fuzzy sets have been successfully tested in the control engineering distributed networks [9], health care [10], decision support systems [11], and situation awareness for army infantry [12]. ...
In this paper, by using Fuzzy Cognitive Mapping (FCM) technique, we developed the metacognitive models for team-based dynamic environment. Preliminary findings from our metacognitive studies provided a possible metacognitive framework in dynamic control tasks [1, 2]. By analyzing metacognition, performance, and communication data between team, we are able to develop the team-based evolving metacognitive models for the dynamic environments using a fuzzy cognitive map. In this research, a human-in-the-loop simulation experiment was conducted to collect communication data, objective performance data (operator on-time action performance), and subjective rating data (retrospective confident metacognitive judgment) from 6 dyads (12 participants). Within the Anti-Air Warfare Coordinator (AAWC) simulation domain, the simulation test bed provides an interactive simulating condition in which the monitoring team must communicate with their team member to defend their ship against hostile aircraft.
... Over the last years, a variety of FCMs have been used for capturing -representing knowledge and intelligent information in engineering applications, for instance, GIS (Liu & Satur, 1999) and fault detection (e.g. Ndouse & Okuda, 1996; Pelaez & Bowles, 1995). FCMs have been used in modeling the supervision of distributed systems ( Stylios, Georgopoulos, & Groumpos, 1997). ...
... Early work in the area of fault detection was based on using rule-based expert systems. In expert systems, a comprehensive database containing the rules of behavior of the faulty system is used to determine if a fault occurred [2], [3]. Rule-based systems are too slow for real-time applications and are dependent on prior knowledge about the fault conditions on the network [4]. ...
In this paper, we present a Failure Prediction System (FPS) using a novel algorithm that extracts frequent anomalous behaviors based on multi-scale trend analysis of multiple network parameters. The proposed Correlation Analysis Across Parameters algorithm (CAAP) utilizes multiple levels of time- scale analysis to reveal the frequent anomalous behaviors. The CAAP philosophy is that failures usually do not occur because of change in a single parameter behavior; instead, a set of interrelated parameters change their behaviors jointly and lead to a particular failure. The proposed algorithm requires an enhanced version of FABM algorithm which was presented by the authors in a previous paper and was used to analyze each parameter's behavior individually. Moreover, the new version, called FABMG algorithm, has the same polynomial computational complexity of O( n 2 ). The CAAP utilizes the data mining techniques of association rules mining in order to reveal the existed correlation relationships. Consequently, as found in this work, this approach improves the quality of the FPS results which was relying on individual parameter analysis only. One of the strengths of CAAP is that it requires the FABMG output only, i.e. it does not require rescanning the database in order to produce the correlation results.
... The advantages of FCM modelling, such as simplicity , adaptability and capability of approximating abstractive structures, provide the potential to model complex problems [21]. FCM has been employed across many different scientific fields as a tool for modelling complex problems [22][23][24][25][26][27][28][29][30][31][32][33][34]and in the domains of medicine for analyzing complex medical processes and supporting clinical decision-making [35][36][37][38][39][40][41][42][43][44]. Also, FCM has the potential to capture and represent both static and dynamic factors, allowing knowledge to be represented from various sources including qualitative and quantitative sources (as fuzzy values), defining the association between concepts and to establish forward reasoning (decision-making on the basis of symptoms and clinical measurements). ...
Meningitis is characterized by an inflammation of the meninges, or the membranes surrounding the brain and spinal cord. Early diagnosis and treatment is crucial for a positive outcome, yet identifying meningitis is a complex process involving an array of signs and symptoms and multiple causal factors which require novel solutions to support clinical decision-making. In this work, we explore the potential of fuzzy cognitive map to assist in the modeling of meningitis, as a support tool for physicians in the accurate diagnosis and treatment of the condition.
Fuzzy cognitive mapping (FCM) is a method for analysing and depicting human perception of a given system. FCM facilitates the development of a conceptual model which is not limited by exact values and measurements and thus is well suited to representing relatively unstructured knowledge and associations expressed in imprecise terms. A team of doctors (physicians), comprising four paediatricians, was formed to define the multifarious signs and symptoms associated with meningitis and to identify risk factors integral to its causality, as indicators used by clinicians to identify the presence or absence of meningitis in patients. The FCM model, consisting of 20 concept nodes, has been designed by the team of paediatricians in collaborative dialogue with the research team.
The paediatricians were supplied with a form containing various input parameters to be completed at the time of diagnosing meningitis among infants and children. The paediatricians provided information on a total of 56 patient cases amongst children whose age ranged from 2 months to 7 years. The physicians' decision to diagnose meningitis was available for each individual case which was used as the outcome measure for evaluating the model. The FCM was trained using 40 cases with an accuracy of 95%, and later 16 test cases were used to analyze the accuracy and reliability of the model. The system produced the results with sensitivity of 83.3% and specificity of 80%.
This work suggests that the application and development of a knowledge based system, using the formalization of FCMs for understanding the symptoms and causes of meningitis in children and infants, can provide a reliable front-end decision-making tool to better assist physicians.
... Over the last 10 years, a variety of FCMs have been used for capturing—representing knowledge and intelligent information in engineering applications, for instance, geographical information systems Liu and Satur (1999) and fault detection (Ndouse and Okuda, 1996; Pelaez and Bowles, 1995). FCMs have been used in modelling the supervision of distributed systems (Stylios et al., 1997). ...
This paper addresses the problem of designing a knowledge management methodology tool to act as a decision support mechanism for geographically dispersed financial enterprises. The underlying research addresses the problem of information capture and representation in financial institutions in order to provide an implementation of the virtuous cycle of knowledge flow. The proposed methodology tool utilizes the fuzzy causal characteristics of Fuzzy Cognitive Maps (FCMs) to generate a hierarchical and dynamic network of interconnected financial performance concepts. By using FCMs, the proposed mechanism simulates the operational efficiency of distributed organizational models with imprecise relationships and quantifies the impact of the geographically dispersed activities to the overall business model. Generic adaptive maps that supplement the decision-making process present a roadmap for integrating hierarchical FCMs into the business model of typical financial sector enterprises. Copyright © 2004 John Wiley & Sons, Ltd.
... Furthermore, FCMs can easily incorporate human knowledge and adapt to a given domain. Applications of FCMs cover a wide range of research and industrial areas, such as electrical engineering, medicine, political science, international relations, military science, history, supervisory systems, etc. Examples of specific applications include diagnosis of diseases [30], analysis of electrical circuits [27], analysis of failure modes effects [19], fault management in distributed network environment [15], modeling and analysis of business performance indicators [8], modeling of supervisory systems [29], modeling of software development project [22] [24], modeling of plant control [6], modeling of political affairs in South Africa [10], modeling of virtual worlds [3], and protein sequence analysis [26]. According to literature research, a vast majority of FCM models were developed solely on the basis of expert(s) knowledge from a given domain [1]. ...
... Furthermore, in such a framework it is possible to handle different types of uncertainties effectively and to combine readily several FCMs into one FCM that takes the knowledge from different experts into consideration [13]. The theory of FCMs represents a very promising paradigm for the development of functional intelligent systems [18,19,22,29,30]. ...
Causation plays a critical role in many predictive and inference tasks. Bayesian networks (BNs) have been used to construct inference systems for diagnostics and decision making. More recently, fuzzy cognitive maps (FCMs) have gained con-siderable attention and offer an alternative framework for representing structured human knowledge and causal inference. In this paper I briefly introduce Bayesian networks and cognitive networks and their causal inference processes in intelligent systems.
... But the above models and algorithms proposed are not suitable for the multi-relational data. Besides, FCM models of large-scale complex systems have been proposed, such as aggregation FCM [18], Hierarchical FCM [19] and quotient FCM [20], etc. The complex FCMs can be used to model the multi-relational data resource. ...
Fuzzy Cognitive Map (FCM) is a new kind of intelligent facility, which has many advantages such as intuitive representing
knowledge skills and strong inference mechanisms based on numeric matrix, etc. In practical application, a majority of data
is stored into the relational database in the form of Entity-Relationship schema. How to mine FCM directly from multi-relational
data resource has become a key problem in researching FCM and an important direction and area of data mining. However, traditional
approaches for obtaining FCM always rely on experience of domain experts or do not take into account the characteristics of
multi-relationship. Based on these, the paper proposes a new model of Two-layer Tree-type FCM (TTFCM) and a new mining methodology
based on gradient descent method.
... If there are no common concepts among different maps, the combined matrix W is constructed according to the previous equation and the dimension of the matrix W is the total number of distinct concepts in all the FCMs. Ndousse [24] provides a simple illustrative example of FCM merging. ...
The target of many studies in the health sciences is the discovery of cause-effect relationships among observed variables of interest, for example: treatments, exposures, preconditions, and outcomes. Causal modeling and causal discovery are central to medical science. In order to algorithmically consider causal relations, the relations must be placed into a representation that supports manipulation. Knowledge of at least some causal effects is imprecise. The most widespread causal representation is directed acyclic graphs (DAGs). However, DAGs are severely limited in what portion of the common sense world they can represent. This paper considers the needs of commonsense causality and suggests Fuzzy Cognitive Maps as an alternative to DAGs.
Due to the current pandemic that is causing psychological problems, sequelae, in some cases, irreparable damage, and, mainly, leading people around the planet to death; this work aims to create an intelligent application from a validated table, presented by Texas Medical Association. Specifically, the application of fuzzy cognitive map can facilitate the contagion risk level’s inference of SARS-CoV-2 from information on human behavior of everyday life. As a possible contribution of this investigation, in addition to the listed and classified risks, the individual’s behavior should mitigate or increase his contagion risk level. The results are presented and normalized on a scale from 0 to 10.KeywordsFuzzy cognitive mapsDiagnosisSimplified comportamental dynamic fuzzy cognitive mapsCOVID-19 contamination risk level
Disaster events, such as September 11, 2001, Hurricane Katrina, and the Southeast Asian Tsunami, have taught America and the world the importance of preparing for emergency response to a disaster that may arise from natural disasters or manmade disasters. Decisions regarding emergency response often rely on incomplete information and imprecise data, whereas responsive measures to disasters must be efficient in time and effective in accuracy to minimize possible loss of lives and properties. The domain of emergency response requires the interaction and collaboration of multiple stakeholders with different standard operating procedures. Excluding lean principles in the design of the emergency management information system can be as devastating as the disaster itself. This chapter analyzes the impacts of lean principles in the understanding of command and control, its nature, and the characteristics of an emergency domain, providing better insight into the problems associated with information processing during emergency response planning.
Managers need tools to provide them with relevant information in real time. These applications are called Executive Information Systems (EIS). These offer different means for analysing the information in a wide range of managers decisions. In order to improve these tools, this paper proposes an extension of the functionality of EIS. This is carried out by using the Fuzzy Cognitive Maps (FCMs) to model dynamic patterns. This represents existing static connections between decisional factors. The proposal supports managers by making prediction and comparisons between the research projects under evaluation.
The Internet has become a vital source of information; internal and external attacks threaten the integrity of the LAN connected to the Internet. In this work, several techniques have been described for detection of such threats. We have focussed on anomaly-based intrusion detection in the campus environment at the network edge. A campus LAN consisting of more than 9000 users with a 90 Mbps internet access link is a large network. Therefore, efficient techniques are required to handle such big data and to model user behaviour. Proxy server logs of a campus LAN and edge router traces have been used for anomalies like abusive Internet access, systematic downloading (internal threats) and DDoS attacks (external threat); our techniques involve machine learning and time series analysis applied at different layers in TCP/IP stack. Accuracy of our techniques has been demonstrated through extensive experimentation on huge and varied datasets. All the techniques are applicable at the edge and can be integrated into a Network Intrusion Detection System.
The Internet has become a vital source of information. This comes with the attendant problems, namely misuse. Systematic downloading of academic and digitized media have become commonplace. Academic institutions in particular get blacklisted owing to free availability of Internet across campus. The objective of this paper is to pro-actively detect systematic downloading. Time series of number of requests are analyzed with pattern analysis techniques. A characterization of the model in the Z-domain shows that the roots of the transfer function form separate clusters during normal and abnormal behavior of traffic. Stability of the system has been used as a cue to detect systematic downloading. Analyzing the trajectory and location of roots to detect systematic downloading involves complex decisions which may not be robust to evolving traffic. This issue is addressed by using Support Vector Data Description that learns a hypersphere enclosing normal traffic. Our empirical evaluations with features like AutoRegressive model roots and Line Spectral Pairs (LSP) obtained from the request time series along with data description show a lot of promise for detecting systematic downloading with F-measure and accuracy as high as 0.90 and 99.5%, respectively. This hybrid approach ensures low false alarms, misses and guarantees robustness of the system.
This paper presents the application of a Fuzzy Cognitive Map (FCM) based theoretical framework and its associated modeling and simulation tool to StrategyMaps (SMs). Existing limitations of SMs are presented in a literature survey. The need for scenario based SMs with inherited ability to change scenarios dynamically as well as the missing element of time are highlighted and discussed upon. FCMs are presented as an alternative to overcome these shortfalls with the introduction of fuzziness in their weights and the robust calculation mechanism. An FCM tool is presented that allows simulation of SMs as well as interconnection of nodes (performance measures) in different SMs which enable the creation of SM hierarchies. An augmented FCM calculation mechanism that allows this type of interlinking is also presented. The resulting methodology and tool are applied to two Banks and the results of these case studies are presented.
Fuzzy Cognitive Maps are fuzzy graph structures that represent causal reasoning. This article proposes a means of building and using such maps to support the managers as well as analysts of R&D portfolios by making prediction comparisons between the research projects under evaluation. The proposal considers the factors within the decision environment and their relationships. With this methodology it becomes possible for analysts to automate (or support, at least) their decision processes.
Clustering, while systematically applied in anomaly detection, has a direct impact on the accuracy of the detection methods. Existing cluster-based anomaly detection methods are mainly based on spherical shape clustering. In this paper, we focus on arbitrary shape clustering methods to increase the accuracy of the anomaly detection. However, since the main drawback of arbitrary shape clustering is its high memory complexity, we propose to summarize clusters first. For this, we design an algorithm, called Summarization based on Gaussian Mixture Model (SGMM), to summarize clusters and represent them as Gaussian Mixture Models (GMMs). After GMMs are constructed, incoming new samples are presented to the GMMs, and their membership values are calculated, based on which the new samples are labeled as “normal” or “anomaly.” Additionally, to address the issue of noise in the data, instead of labeling samples individually, they are clustered first, and then each cluster is labeled collectively. For this, we present a new approach, called Collective Probabilistic Anomaly Detection (CPAD), in which, the distance of the incoming new samples and the existing SGMMs is calculated, and then the new cluster is labeled the same as of the closest cluster. To measure the distance of two GMM-based clusters, we propose a modified version of the Kullback-Libner measure. We run several experiments to evaluate the performances of the proposed SGMM and CPAD methods and compare them against some of the well-known algorithms including ABACUS, local outlier factor (LOF), and one-class support vector machine (SVM). The performance of SGMM is compared with ABACUS using Dunn and DB metrics, and the results indicate that the SGMM performs superior in terms of summarizing clusters. Moreover, the proposed CPAD method is compared with the LOF and one-class SVM considering the performance criteria of (a) false alarm rate, (b) detection rate, and (c) memory efficiency. The experimental results show that the CPAD method is noise resilient, memory efficient, and its accuracy is higher than the other methods.
Fuzzy cognitive map (FCM) [5] was a modification of the cognitive map of Axelrod [1]. FCMs can be used in knowledge representation and inference which are essential to any intelligent system. FCM encodes rules in its networked structure in which all concepts are causally connected. Rules are fired based on a given set of initial conditions and the structure of the FCM. The resulting map pattern represents the causal inference of the FCM. In FCMs, we are able to represent all concepts and arcs (edges) connecting the concepts by symbols or numerical values. Moreover, in such a framework it is possible to handle different types of uncertainties effectively and to combine readily several FCMs into a single FCM that takes the knowledge from different experts into consideration [6]. FCM provides a mechanism for handling causality between events/objects in a more natural fashion. Indeed, FCM is a flexible and realistic representation scheme for dealing with knowledge. This scheme is potentially useful in the development of human-centered systems that require soft-knowledge in the sense that system concepts, their relationships, and the meta-system knowledge can be represented only to a certain degree. In addition, subtle (spatial and temporal) variations in the knowledge base can often result in completely different outcomes or decisions [25]. Many recently developed systems and successful applications have shown that fuzzy cognitive maps represent a promising paradigm for the development of functional intelligent systems [10, 15, 19, 20].
Fuzzy Cognitive Map (FCM) is a powerful and flexible framework for knowledge representation and causal inference. However, in most real applications, it is difficult to design and analyze FCMs due to their structural complexity. Simplification, merging, and division are the important operations on the structure of FCMs. In this paper we present approaches to simplifying FCMs. These approaches show how to clean up a FCM, how to divide a complex FCM into basic FCMs, and how to extract the eigen structure of these basic FCMs. Two improved methods for merging FCMs from different human experts are also proposed in this paper. We discuss difficulties in merging FCMs and present possible solutions.
Fuzzy Cognitive Maps are fuzzy graph structures that represent causal reasoning. This article proposes a means of building and using such maps to support the managers as well as analysts of R&D portfolios by making prediction comparisons between the research projects under evaluation. The proposal considers the factors within the decision environment and their relationships. With this methodology it becomes possible for analysts to automate (or support, at least) their decision processes.
Fuzzy cognitive maps are a qualitative tool that can capture the extensive cause/effect relationships that an expert believes exist within a complicated system such as an electronic circuit. In addition to predictive capabilities when inputs are applied and propagated through the model, the topology of the map itself can be used in diagnosing failures by identifying causes for nodes of interest. But simply tagging a node as a cause of another is inadequate because this identification does not indicate whether this node is a sole cause or whether other nodes must also be present. The identification of combinations of nodes is key to the determination of a strategy for locating the system problem. An algorithm is given in this paper that uses a modified version of the reachability matrix to identify multi-node combinations that can cause a node of interest.
This paper presents the application of a fuzzy cognitive map (FCM) based theoretical framework and its associated modeling and simulation tool to strategy maps (SMs). Existing limitations of SMs are presented in a literature survey. The need for scenario based SMs with inherited ability to change scenarios dynamically as well as the missing element of time are highlighted and discussed upon. FCMs are presented as an alternative to overcome these shortfalls with the introduction of fuzziness in their weights and the robust calculation mechanism. An FCM tool is presented that allows simulation of SMs as well as interconnection of nodes (performance measures) in different SMs which enables the creation of SM hierarchies. An augmented FCM calculation mechanism that allows this type of interlinking is also presented. The resulting methodology and tool are applied to two Banks and the results of these case studies are presented.
The research presented addresses two problems evident in the Human Computer Interaction (HCI) and Cognitive Systems Engineering (CSE) literature. First, research has shown that emergency crisis management (ECM) work involves teams and "teams of teams". Yet, there are few models of naturalistic decision systems (i.e., crisis-management) that allow multiple teams to work collectively. Research efforts in modeling the ECM work domain typically involve single team-based simulations. However, few simulations have been designed to study multiple teams operating simultaneously. Secondly, previous efforts in supporting teamwork have involved the development of decision-aids; however, a prevalent problem with team decision aids is the requirement of rigid and stable information needed to make decisions. The ECM work domain is highly dynamic with incomplete and ambiguous information. Consequently, decision-aids within the ECM domain oftentimes make inaccurate and erroneous recommendations. An alternative design that makes use of contextualized information could be more effective. The aforementioned problems are addressed by (1) the development of a simulation studying teams operating in an ECM work domain, and (2) the conducting of an experiment to investigate the effect a decision-aid has on teamwork. The first objective of the research involved designing a simulation that effectively mimics ECM operations. Hence, the research constructed NeoCITIES as a crisis management scaled world to study team decision-making for a variety of examinations related to HCI and CSE suppositions. The second objective of the research included the development of a decision-aid that utilizes contextualized information. Specifically, a Fuzzy Cognitive Map (FCM) that handles dynamic and incomplete information was developed to function as a decision-aid supporting teams operating within the NeoCITIES simulation. Lastly, the research includes an examination of the efficacy of a Fuzzy Cognitive Map (FCM) decision aid on teamwork within NeoCITIES. The study measures whether the presence of a FCM improves decision-making involving resource allocation amongst teams. Experimental sessions using undergraduate students enrolled at Pennsylvania State University provided data to measure team performance and team cognition. The contributions of the research are threefold. First, NeoCITIES is a technological artifact developed to function as a test-bed to study teams. Secondly, the research assesses the functional use of fuzzy cognitive mapping to serve as a decision-aid for teams. Lastly, in accordance with the interdisciplinary nature of the Information Sciences and Technology program, the present study serves as a model research agenda by developing technologies, examining theories, and incorporating user-centric methods from several disciplines: cognitive systems engineering, human computer interaction, computer science, psychology, and artificial intelligence.
This paper presents a hybrid algorithm based on fuzzy linear regression (FLR) and fuzzy cognitive map (FCM) to deal with the problem of forecasting and optimization of housing market fluctuations. Due to the uncertainty and severe noise associated with the housing market, the application of crisp data for forecasting and optimization purposes is insufficient. Hence, in order to enable the decision-makers to make decisions with respect to imprecise/fuzzy data, FLR is used in the proposed hybrid algorithm. The best-fitted FLR model is then selected with respect to two indicators including Index of Confidence (IC) and Mean Absolute Percentage Error (MAPE). To achieve this objective, analysis of variance (ANOVA) for a randomized complete block design (RCBD) is employed. The primary objective of this study is to utilize imprecise/fuzzy data in order to improve the analysis of housing price fluctuations, in accordance with the factors obtained through the best-fitted FLR model. The secondary objective of this study is the exhibition of the resulted values in a schematic way via FCM. Hybridization of FLR and FCM provides a decision support system (DSS) for utilization of historical data to predict housing market fluctuation in the future and identify the influence of the other parameters. The proposed hybrid FLR-FCM algorithm enables the decision-makers to utilize imprecise and ambiguous data and represent the resulted values of the model more clearly. This is the first study that utilizes a hybrid intelligent approach for housing price and market forecasting and optimization.
Recently, Fuzzy Grey Cognitive Map (FGCM) has been proposed as a FCM extension. It is based on Grey System Theory, that it is focused on solving problems with high uncertainty, under discrete incomplete and small data sets. The FGCM nodes are variables, representing grey concepts. The relationships between nodes are represented by directed edges. An edge linking two nodes models the grey causal influence of the causal variable on the effect variable. Since FGCMs are hybrid methods mixing Grey Systems and Fuzzy Cognitive Maps, each cause is measured by its grey intensity. An improved construction process of FGCMs is presented in this study, proposing an intensity value to assign the vibration of the grey causal influence, thus to handle the trust of the causal influence on the effect variable initially prescribed by experts’ suggestions. The explored methodology is implemented in a well-known medical decision making problem pertaining to the problem of radiotherapy treatment planning selection, where the FCMs have previously proved their usefulness in decision support. Through the examined medical problem, the FGCMs demonstrate their functioning and dynamic capabilities to approximate better human decision making.
This paper describes a prototype intelligent intrusion detection system (IIDS) that is being developed to demonstrate the effectiveness of specific artificial intelligence approaches to intrusion detection within a high performance computing (HPC) architecture. It is an update of previously reported research. The techniques being investigated include both neural networks and data mining techniques that use fuzzy logic including fuzzy association rules, fuzzy frequent episodes, and fuzzy cognitive maps. The system under development combines both anomaly and misuse detection mechanisms and uses both network traffic and system audit data as inputs. Suspected intrusions are reported to a decision-making module for analysis and action. Fuzzy cognitive maps (FCMs) and fuzzy rule-bases are used for the causal knowledge acquisition and to support the causal knowledge reasoning process in the decision-making module. Preliminary results dealing with detection of intrusions unique to an HPC environment are presented.
This paper addresses the problem of designing an “intelligent” decision support methodology tool to act as a back end to financial
planning. The methodology tool proposes a novel approach to supplementing typical financial strategy formulation projects
by utilizing the fuzzy causal characteristics of Fuzzy Cognitive Maps (FCMs) to generate a hierarchical and dynamic network
of interconnected profit and loss (P&L) concepts. By using FCMs, the mechanism simulates the efficiency of complex hierarchical
financial models with imprecise relationships and external stimuli while quantifying the impact of strategic changes to the
overall P&L status. Generic maps that supplement the decision making process demonstrate a roadmap for integrating hierarchical
FCMs into the P&L model of typical financial sector enterprises. Preliminary experiments indicate that ex ante reasoning of the impact of strategic changes (actual or hypothetical) to the status of financial performance can be effective
and realistic, without employing detailed P&L numerical calculations.
KeywordsFuzzy cognitive maps-financial performance management-strategy planning-banking-profit and loss accounts
This paper describes a model for software reliability prediction. The output is a ‘forward-looking’ model, which helps testers
to predict and manage software reliability. Despite the availability of various approaches developed in the field of software
reliability, there are still issues that require further research in order to succeed in supporting the decision making process
and improving software quality. In the present paper we propose the use of Fuzzy Cognitive Maps (FCMs) as an approach to modelling
of software reliability. FCMs capture information in the relationships between concepts, are dynamic, express hidden relationships,
and are combinable and tunable. Preliminary experiments indicate that the proposed mechanism forms a sound support aid for
software reliability modelling.
KeywordsSoftware Reliability-Fuzzy Cognitive Maps-Modelling-Uncertainty
Fund management is a major function for most financial sector enterprises with geographically dispersed activities. The risk associated to such managerial decisions affects directly the continuity, profitability and reputation of the enterprise. This paper presents a knowledge modeling methodology tool to act as a decision support mechanism for geographically dispersed financial enterprises. The underlying research addresses the problem of financial information capture and representation by utilizing the soft computing characteristics of Fuzzy Cognitive Maps (FCMs). By using FCMs, the proposed mechanism generates a hierarchical and dynamic network of interconnected financial knowledge concepts, simulates the operational efficiency of distributed organizational models with imprecise relationships and quantifies the impact of geographically dispersed activities to the overall business performance. Generic adaptive maps offer an alternative approach to financial management based on strategic level knowledge modeling and integrate hierarchical FCMs into the decision making model of typical financial sector enterprises. Finally, this paper discusses experiments with the proposed mechanism and comments on its usability.
One of the main issues in developing automatic response systems especially autonomous robots is selecting the best action among all possible actions. Fuzzy cognitive maps (FCMs) aim to mimic the reasoning process of the human. FCMs are able to capture and imitate human behavior by describing, developing and representing models. FCMs are also popular for their simplicity and transparency while being successful in a variety of applications. We developed a novel model that could be used for action selection in robots. This model is constructed on a learning FCM which is relied on improved nonlinear Hebbian algorithm. We tested our model through a series of practical experiments on the latest version of soccer server simulation 3D environment. Our tests involved carefully defined factors to measure the team performance. Our results showed a significant improvement in overall performance.
Fuzzy Virtual Worlds
- K Bart
- Ai Expert