Association Rule Mining for Suspicious Email Detection: A Data Mining Approach

Appavu, S.; Pandian, M. Thurai; Rajaram, R.

doi:10.1109/isi.2007.379491

Cited by 16 publications

(14 citation statements)

References 5 publications

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“…Criminals these days are becoming technologically intelligent in planning and committing crimes [6]. E-mail has become one of the popular means of communication.…”

Section: Introductionmentioning

confidence: 99%

Z - CRIME: A data mining tool for the detection of suspicious criminal activities based on decision tree

Sharma

2014

2014 International Conference on Data Mining and Intelligent Computing (ICDMIC)

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Data mining is the extraction of knowledge from large databases. One of the popular data mining techniques is Classification in which different objects are classified into different classes depending on the common properties among them. Decision Trees are widely used in Classification. This paper proposes a tool which applies an enhanced Decision Tree Algorithm to detect the suspicious e-mails about the criminal activities. An improved ID3 Algorithm with enhanced feature selection method and attribute-importance factor is applied to generate a better and faster Decision Tree. The objective is to detect the suspicious criminal activities and minimize them. That's why the tool is named as "Z-Crime" depicting the "Zero Crime" in the society. This paper aims at highlighting the importance of data mining technology to design proactive application to detect the suspicious criminal activities.

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“…Criminals these days are becoming technologically intelligent in planning and committing crimes [6]. E-mail has become one of the popular means of communication.…”

Section: Introductionmentioning

confidence: 99%

Z - CRIME: A data mining tool for the detection of suspicious criminal activities based on decision tree

Sharma

2014

2014 International Conference on Data Mining and Intelligent Computing (ICDMIC)

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“…A study by Appavu et al [5] applied the association rule mining for suspicious email detection task. In the article [5], the authors added a specialized class of suspicious emails as an alert or the information using verb.…”

Section: Suspicious Email Detectionmentioning

confidence: 99%

“…In the article [5], the authors added a specialized class of suspicious emails as an alert or the information using verb. An email is considered suspicious if in addition to keywords it contains future tenses to consider it as an alarm for future suspicious activity.…”

Section: Suspicious Email Detectionmentioning

confidence: 99%

“…An email is considered suspicious if in addition to keywords it contains future tenses to consider it as an alarm for future suspicious activity. It should be noted that in the articles [2,5], the suspicious emails considered are the terrorism related emails which give some clue regarding future terrorist acts.…”

Section: Suspicious Email Detectionmentioning

confidence: 99%

“…Emails can be categorized in many groups, based on the purpose for which email is intended. It can be categorized as legitimate and illegitimate [3], spam and ham [4], suspicious and non-suspicious [2,5], fraudulent and normal, formal and informal which can further be classified as personal, family, friends, business, work, etc. [3].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Detection of fraudulent emails by employing advanced feature abundance

Nizamani

Memon

Glasdam

et al. 2014

Egyptian Informatics Journal

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In this paper, we present a fraudulent email detection model using advanced feature choice. We extracted various kinds of features and compared the performance of each category of features with the others in terms of the fraudulent email detection rate. The different types of features are incorporated step by step. The detection of fraudulent email has been considered as a classification problem and it is evaluated using various state-of-the art algorithms and on CCM (Nizamani et al., 2011) [1] which is authors' previous cluster based classification model. The experiments have been performed on diverse feature sets and the different classification methods. The comparison of the results is also presented and the evaluation show that for the fraudulent email detection tasks, the feature set is more important regardless of classification method. The results of the study suggest that the task of fraudulent emails detection requires the better choice of feature set; while the choice of classification method is of less importance.Ó 2014 Production and hosting by Elsevier B.V. on behalf

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A review of data mining applications in crime

Hassani

Huang

Silva

et al. 2016

Statistical Analysis

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Crime continues to remain a severe threat to all communities and nations across the globe alongside the sophistication in technology and processes that are being exploited to enable highly complex criminal activities. Data mining, the process of uncovering hidden information from Big Data, is now an important tool for investigating, curbing and preventing crime and is exploited by both private and government institutions around the world. The primary aim of this paper is to provide a concise review of the data mining applications in crime. To this end, the paper reviews over 100 applications of data mining in crime, covering a substantial quantity of research to date, presented in chronological order with an overview table of many important data mining applications in the crime domain as a reference directory. The data mining techniques themselves are briefly introduced to the reader and these include entity extraction, clustering, association rule mining, decision trees, support vector machines, naive Bayes rule, neural networks and social network analysis amongst others

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Association Rule Mining for Suspicious Email Detection: A Data Mining Approach

Cited by 16 publications

References 5 publications

Z - CRIME: A data mining tool for the detection of suspicious criminal activities based on decision tree

Z - CRIME: A data mining tool for the detection of suspicious criminal activities based on decision tree

Detection of fraudulent emails by employing advanced feature abundance

A review of data mining applications in crime

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