Modelling Intelligent Phishing Detection System for E-banking Using Fuzzy Data Mining

Aburrous, Maher; Hossain, Alamgir; Dahal, Keshav; Thabatah, Fadi

doi:10.1109/cw.2009.43

Cited by 28 publications

(17 citation statements)

References 14 publications

(8 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…o An interesting solution has been proposed in [13] to detect e-banking phishing websites using an artificial intelligent technique, authors propose a model based on using association and classification data mining algorithms and tools, which were used to classify the fishing websites and the relationship that correlate them with each other, six classification algorithm were implemented to extract the phishing training datasets criteria to classify their legitimacy.…”

Section: Related Workmentioning

confidence: 99%

Phishing Websites Detection Using Data Mining Classification Model

Jabri

Ibrahim

2015

TMLAI

View full text Add to dashboard Cite

Phishing is a significant security threat to the Internet; it is an electronic online identity theft in which the attackers use spoofing techniques like fake websites that mimic legal websites to trick users into revealing their private information. Many of successful phishing attacks do exist and subsequently a considerable number of anti-phishing methods have been proposed. However, they vary in terms of their accuracy and error rate. This paper proposes an algorithm for phishing websites detection using data mining classification model. It is implemented and experimented using a dataset composed of 20 different webpage features and 1,000 instances. The experimental results showed that the proposed algorithm outperforms the original one in terms of the number of classification rules, accuracy (87%) and less error rate (0.1 %).

show abstract

Section: Related Workmentioning

confidence: 99%

Phishing Websites Detection Using Data Mining Classification Model

Jabri

Ibrahim

2015

TMLAI

View full text Add to dashboard Cite

show abstract

“…Major researches have considered content-based approaches based on machine learning techniques to detect phishing websites [2], [10], [11], [12], [13 [14], [15]. Aburrous proposed a model to identify electronic banking sites [2].…”

Section: A Content-based Through Machine Learning Techniquesmentioning

confidence: 99%

“…These approaches include feature-based techniques [2], [3], blacklist-based [4], [5], [6], [7], and content-based approaches applying machine learning algorithms have attempted to solve the problem [8], [2]. However, there is still high false positive causing inaccuracy in online transaction.…”

Section: Introductionmentioning

confidence: 99%

“…The aim is to design a parameter tuning method based on an adaptive neuro-fuzzy inference system, using comprehensive data from six inputs that can be used by researchers in the field. The specific objectives are: (1) to identify samples and gather comprehensive data to be used as input data, (2) to develop fuzzy models based on ANFIS comprehensive dataset, (3) to train and check/test the models using cross-validation methods, and (4) to conduct a comparative study to prove the capability and merit of the parameter tuning framework.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Parameter optimization for intelligent phishing detection using Adaptive Neuro-Fuzzy

Barraclough

Sexton

Aslam

2014

ijarai

View full text Add to dashboard Cite

Abstract-Phishing attacks has been growing rapidly in the past few years. As a result, a number of approaches have been proposed to address the problem. Despite various approaches proposed such as feature-based and blacklist-based via machine learning techniques, there is still a lack of accuracy and real-time solution. Most approaches applying machine learning techniques requires that parameters are tuned to solve a problem, but parameters are difficult to tune to a desirable output. This study presents a parameter tuning framework, using adaptive Neuronfuzzy inference system with comprehensive data to maximize systems performance. Extensive experiment was conducted. During ten-fold cross-validation, the data is split into training and testing pairs and parameters are set according to desirable output and have achieved 98.74% accuracy. Our results demonstrated higher performance compared to other results in the field. This paper contributes new comprehensive data, novel parameter tuning method and applied a new algorithm in a new field. The implication is that adaptive neuron-fuzzy system with effective data and proper parameter tuning can enhance system performance. The outcome will provide a new knowledge in the field.

show abstract

“…Because of the subjective considerations and the ambiguities involved in the detection, Fuzzy Data Mining (DM) Techniques can be an effective tool in assessing and identifying phishing websites for e-banking since it offers a more natural way of dealing with quality factors rather than exact values. "Modelling Intelligent Phishing Detection System for e-Banking using Fuzzy Data Mining" [11], a novel approach to overcome the 'fuzziness' in the e-banking phishing website assessment propose an intelligent resilient and effective model for detecting e-banking phishing websites. The proposed model is based on Fuzzy logic (FL) combined with Data Mining algorithms to characterize the e-banking phishing website factors and to investigate its techniques by classifying there phishing types and defining six e-banking phishing website attack criteria's with a layer structure.…”

Section: Related Workmentioning

confidence: 99%