Deceptive phishing detection system: From audio and text messages in Instant Messengers using Data Mining approach

Ali, Mohammed Mahmood; Rajamani, Lakshmi

doi:10.1109/icprime.2012.6208390

Cited by 10 publications

(5 citation statements)

References 20 publications

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“…First, the offender sends a communication to the target, which 62 of the definitions state. Typically, the offender sends the target an email (n = 30) or sends a message using a method that is not specified (n = 22), occasionally using other methods such as websites (Hodgson 2005;Levy 2004;Olurin et al 2012), social spaces (Piper 2007), instant messages (Ali and Rajamani 2012;Verma et al 2012), text messaging (Hinson 2010) or even letters (Workman 2008). Then, the target may reply by sending information to the offender, which is mentioned in 64 of the definitions, mostly through the use of a website (n = 40).…”

Section: Resultsmentioning

confidence: 99%

Achieving a consensual definition of phishing based on a systematic review of the literature

Lastdrager

2014

Crime Sci

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Background: Phishing is a widely known phenomenon, but currently lacks a commonly accepted definition. As a result, many studies about phishing use their own definition. The lack of a common definition prevents knowledge accumulation and makes analysing studies or aggregating data about phishing a difficult task. Method:To develop a definition, we used existing definitions as input and combined them using crime science theories as the theoretical framework. A systematic review of the literature up to August 2013 was conducted, resulting in 2458 publications mentioning the word phishing. All journal articles, together with both highly cited and recent conference papers were selected, giving a total of 536 peer-reviewed publications (22%) to be manually reviewed. This resulted in 113 distinct definitions to be analysed.Results: An analysis identified key concepts that were found in most definitions and formed the building blocks for a consensual definition. We propose a new definition that is based upon current ones, which defines phishing in a comprehensive way and -in our opinion -addresses all important elements of phishing: 'phishing is a scalable act of deception whereby impersonation is used to obtain information from a target'. Conclusions:A consensual definition allows future research to be aligned and it facilitates the interpretation and comparison of existing research. The findings suggest that the routine activity approach can be applied to the digital world. Finally, the 'scalability' concept of our definition provides a new theoretical notion to digital crime that is independent of the employed channel.

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Section: Resultsmentioning

confidence: 99%

Achieving a consensual definition of phishing based on a systematic review of the literature

Lastdrager

2014

Crime Sci

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“…It is worth noticing that data mining techniques have been adopted in this context, to prevent phishing variants. Ali and Rajamani (2012) adopted association rule mining algorithm for detection of deceptive phishing from instant messages. The model predicted phishing threats for text and audio messages effectively.…”

Section: Related Workmentioning

confidence: 99%

Detection of phishing websites using a novel twofold ensemble model

Nagaraj¹,

Bhattacharjee

Sridhar³

et al. 2018

JSIT

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Purpose Phishing is one of the major threats affecting businesses worldwide in current times. Organizations and customers face the hazards arising out of phishing attacks because of anonymous access to vulnerable details. Such attacks often result in substantial financial losses. Thus, there is a need for effective intrusion detection techniques to identify and possibly nullify the effects of phishing. Classifying phishing and non-phishing web content is a critical task in information security protocols, and full-proof mechanisms have yet to be implemented in practice. The purpose of the current study is to present an ensemble machine learning model for classifying phishing websites. Design/methodology/approach A publicly available data set comprising 10,068 instances of phishing and legitimate websites was used to build the classifier model. Feature extraction was performed by deploying a group of methods, and relevant features extracted were used for building the model. A twofold ensemble learner was developed by integrating results from random forest (RF) classifier, fed into a feedforward neural network (NN). Performance of the ensemble classifier was validated using k-fold cross-validation. The twofold ensemble learner was implemented as a user-friendly, interactive decision support system for classifying websites as phishing or legitimate ones. Findings Experimental simulations were performed to access and compare the performance of the ensemble classifiers. The statistical tests estimated that RF_NN model gave superior performance with an accuracy of 93.41 per cent and minimal mean squared error of 0.000026. Research limitations/implications The research data set used in this study is publically available and easy to analyze. Comparative analysis with other real-time data sets of recent origin must be performed to ensure generalization of the model against various security breaches. Different variants of phishing threats must be detected rather than focusing particularly toward phishing website detection. Originality/value The twofold ensemble model is not applied for classification of phishing websites in any previous studies as per the knowledge of authors.

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“…Mahmood Ali M. et.al. [19] presented a paper on 'Deceptive Phishing Detection System (From Audio and Text messages in Instant Messengers using Data Mining Approach)' in which, words are recognized from speech with the help of FFT spectrum analysis and LPC coefficients methodologies.…”

Section: Overview Of Previous Study On Phishingmentioning

confidence: 99%

Comparison of Classification Algorithms to Detect Phishing Web Pages Using Feature Selection and Extraction

Gupta

2016

Int. J. Res. Granthaalayah

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The phishing is a kind of e-commerce lure which try to steal the confidential information of the web user by making identical website of legitimate one in which the contents and images almost remains similar to the legitimate website with small changes. Another way of phishing is to make minor changes in the URL or in the domain of the legitimate website. In this paper, a number of anti-phishing toolbars have been discussed and proposed a system model to tackle the phishing attack. The proposed anti-phishing system is based on the development of the Plug-in tool for the web browser. The performance of the proposed system is studied with three different data mining classification algorithms which are Random Forest, Nearest Neighbour Classification (NNC), Bayesian Classifier (BC). To evaluate the proposed anti-phishing system for the detection of phishing websites, 7690 legitimate websites and 2280 phishing websites have been collected from authorised sources like APWG database and PhishTank. After analyzing the data mining algorithms over phishing web pages, it is found that the Bayesian algorithm gives fast response and gives more accurate results than other algorithms.

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Deceptive phishing detection system: From audio and text messages in Instant Messengers using Data Mining approach

Cited by 10 publications

References 20 publications

Achieving a consensual definition of phishing based on a systematic review of the literature

Achieving a consensual definition of phishing based on a systematic review of the literature

Detection of phishing websites using a novel twofold ensemble model

Comparison of Classification Algorithms to Detect Phishing Web Pages Using Feature Selection and Extraction

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