5 research outputs found
A Fuzzy Association Rule Mining Expert-Driven (FARME-D) approach to Knowledge Acquisition
Fuzzy Association Rule Mining Expert-Driven (FARME-D) approach to knowledge acquisition is proposed in this paper as a viable solution to the challenges of rule-based unwieldiness and sharp boundary problem in building a fuzzy rule-based expert system. The fuzzy models were based on domain experts’ opinion about the data description. The proposed approach is committed to modelling of a
compact Fuzzy Rule-Based Expert Systems. It is also aimed at providing a platform for instant update of the knowledge-base in case new knowledge is discovered. The insight to the new approach strategies and underlining assumptions, the structure of FARME-D and its
practical application in medical domain was discussed. Also, the modalities for the validation of the FARME-D approach were discussed
A Fuzzy-Mining Approach for Solving Rule Based Expert System Unwieldiness in Medical Domain
Over the years, one of the challenges of a rule based expert system is the possibility of evolving a compact and
consistent knowledge-base with a fewer numbers of rules that are relevant to the application domain, in order to
enhance the comprehensibility of the expert system. In this paper, the hybrid of fuzzy rule mining interestingness
measures and fuzzy expert system is exploited as a means of solving the problem of unwieldiness and maintenance
complication in the rule based expert system. This negatively increases the knowledge-base space complexity and
reduces rule access rate which impedes system response time. To validate this concept, the Coronary Heart Disease risk
ratio determination is used as the case study. Results of fuzzy expert system with a fewer numbers of rules and fuzzy
expert system with a large numbers of rules are presented for comparison. Moreover, the effect of fuzzy linguistic
variable risk ratio is investigated. This makes the expert system recommendation close to human perception
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Phishing website detection using intelligent data mining techniques. Design and development of an intelligent association classification mining fuzzy based scheme for phishing website detection with an emphasis on E-banking.
Phishing techniques have not only grown in number, but also in sophistication. Phishers might
have a lot of approaches and tactics to conduct a well-designed phishing attack. The targets of
the phishing attacks, which are mainly on-line banking consumers and payment service
providers, are facing substantial financial loss and lack of trust in Internet-based services. In
order to overcome these, there is an urgent need to find solutions to combat phishing attacks.
Detecting phishing website is a complex task which requires significant expert knowledge and
experience. So far, various solutions have been proposed and developed to address these
problems. Most of these approaches are not able to make a decision dynamically on whether the
site is in fact phished, giving rise to a large number of false positives. This is mainly due to
limitation of the previously proposed approaches, for example depending only on fixed black
and white listing database, missing of human intelligence and experts, poor scalability and their
timeliness.
In this research we investigated and developed the application of an intelligent fuzzy-based
classification system for e-banking phishing website detection. The main aim of the proposed
system is to provide protection to users from phishers deception tricks, giving them the ability
to detect the legitimacy of the websites. The proposed intelligent phishing detection system
employed Fuzzy Logic (FL) model with association classification mining algorithms. The
approach combined the capabilities of fuzzy reasoning in measuring imprecise and dynamic
phishing features, with the capability to classify the phishing fuzzy rules. Different phishing experiments which cover all phishing attacks, motivations and deception
behaviour techniques have been conducted to cover all phishing concerns. A layered fuzzy
structure has been constructed for all gathered and extracted phishing website features and
patterns. These have been divided into 6 criteria and distributed to 3 layers, based on their attack
type. To reduce human knowledge intervention, Different classification and association
algorithms have been implemented to generate fuzzy phishing rules automatically, to be
integrated inside the fuzzy inference engine for the final phishing detection.
Experimental results demonstrated that the ability of the learning approach to identify all
relevant fuzzy rules from the training data set. A comparative study and analysis showed that
the proposed learning approach has a higher degree of predictive and detective capability than
existing models. Experiments also showed significance of some important phishing criteria like
URL & Domain Identity, Security & Encryption to the final phishing detection rate.
Finally, our proposed intelligent phishing website detection system was developed, tested and
validated by incorporating the scheme as a web based plug-ins phishing toolbar. The results
obtained are promising and showed that our intelligent fuzzy based classification detection
system can provide an effective help for real-time phishing website detection. The toolbar
successfully recognized and detected approximately 92% of the phishing websites selected from
our test data set, avoiding many miss-classified websites and false phishing alarms
Novel Attack Detection Using Fuzzy Logic and Data Mining
Abstract:- Intrusion Detection Systems are increasingly a key part of systems defense. Various approaches to Intrusion Detection are currently being used, but they are relatively ineffective. Artificial Intelligence plays a driving role in security services. This paper proposes a dynamic Intelligent Intrusion Detection System model, based on specific AI approach for intrusion detection. The technique that is being investigated includes fuzzy logic with network profiling, which uses simple data mining techniques to process the network data. The proposed hybrid system combines anomaly and misuse detection. Simple fuzzy rules, allow us to construct if-then rules that reflect common ways of describing security attacks. Suspicious intrusions can be traced back to its original source and any traffic from that particular source will be redirected back to them in future. Both network traffic and system audit data are used as inputs for the experimental needs. 1.0 Introduction. Information has become an organization’s most precious asset. Organizations have become increasingly dependent on information, since more information is being stored and processed o