191 research outputs found
A review of associative classification mining
Associative classification mining is a promising approach in data mining that utilizes the
association rule discovery techniques to construct classification systems, also known as
associative classifiers. In the last few years, a number of associative classification algorithms
have been proposed, i.e. CPAR, CMAR, MCAR, MMAC and others. These algorithms
employ several different rule discovery, rule ranking, rule pruning, rule prediction and rule
evaluation methods. This paper focuses on surveying and comparing the state-of-the-art associative
classification techniques with regards to the above criteria. Finally, future directions in associative
classification, such as incremental learning and mining low-quality data sets, are also
highlighted in this paper
Predicting Phishing Websites using Neural Network trained with Back-Propagation
Phishing is increasing dramatically with the development of modern technologies and the global worldwide computer networks. This results in the loss of customer’s confidence in e-commerce and online banking, financial damages, and identity theft. Phishing is fraudulent effort aims to acquire sensitive information from users such as credit card credentials, and social security number. In this article, we propose a model for predicting phishing attacks based on Artificial Neural Network (ANN). A Feed Forward Neural Network trained by Back Propagation algorithm is developed to classify websites as phishing or legitimate. The suggested model shows high acceptance ability for noisy data, fault tolerance and high prediction accuracy with respect to false positive and false negative rates
Experimental Case Studies for Investigating E-Banking Phishing Techniques and Attack Strategies
Phishing is a form of electronic identity theft in which a combination of social engineering and web site spoofing techniques are used to trick a user into revealing confidential information with economic value. The problem of social engineering attack is that there is no single solution to eliminate it completely, since it deals largely with the human factor. This is why implementing empirical experiments is very crucial in order to study and to analyze all malicious and deceiving phishing website attack techniques and strategies. In this paper, three different kinds of phishing experiment case studies have been conducted to shed some light into social engineering attacks, such as phone phishing and phishing website attacks for designing effective countermeasures and analyzing the efficiency of performing security awareness about phishing threats. Results and reactions to our experiments show the importance of conducting phishing training awareness for all users and doubling our efforts in developing phishing prevention techniques. Results also suggest that traditional standard security phishing factor indicators are not always effective for detecting phishing websites, and alternative intelligent phishing detection approaches are needed
A Classification Rules Mining Method based on Dynamic Rules' Frequency
Rule based classification or rule induction (RI) in data mining is an approach that normally generates classifiers containing simple yet effective rules. Most RI algorithms suffer from few drawbacks mainly related to rule pruning and rules sharing training data instances. In response to the above two issues, a new dynamic rule induction (DRI) method is proposed that utilises two thresholds to minimise the items search space. Whenever a rule is generated, DRI algorithm ensures that all candidate items' frequencies are updated to reflect the deletion of the rule’s training data instances. Therefore, the remaining candidate items waiting to be added to other rules have dynamic frequencies rather static. This enables DRI to generate not only rules with 100% accuracy but rules with high accuracy as well. Experimental tests using a number of UCI data sets have been conducted using a number of RI algorithms. The results clearly show competitive performance in regards to classification accuracy and classifier size of DRI when compared to other RI algorithms
Constrained Dynamic Rule Induction Learning
The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.One of the known classification approaches in data mining is rule induction (RI). RI algorithms such as PRISM usually produce If-Then classifiers, which have a comparable predictive performance to other traditional classification approaches such as decision trees and associative classification. Hence, these classifiers are favourable for carrying out decisions by users and hence they can be utilised as decision making tools. Nevertheless, RI methods, including PRISM and its successors, suffer from a number of drawbacks primarily the large number of rules derived. This can be a burden especially when the input data is largely dimensional. Therefore, pruning unnecessary rules becomes essential for the success of this
type of classifiers. This article proposes a new RI algorithm that reduces the search space for candidate rules by early pruning any irrelevant items during the process of building the classifier. Whenever a rule is generated, our algorithm updates the candidate items frequency to reflect the discarded data examples associated with the rules derived. This makes items frequency dynamic rather static and ensures that irrelevant rules are deleted in preliminary stages when they don’t hold enough data representation. The major benefit will be a concise set of decision making rules that are easy to understand and controlled by the decision maker. The proposed algorithm has been implemented in WEKA (Waikato Environment for Knowledge Analysis) environment and hence it can now be utilised by different types of users such as managers, researchers, students and others. Experimental results using real data from the security domain as well as sixteen classification datasets from University of California Irvine (UCI) repository reveal that the proposed algorithm is competitive in regards to classification accuracy when compared to known RI algorithms. Moreover, the classifiers produced by our algorithm are smaller in size which increase their possible use in practical applications
Detection of Dementia Progression from Functional Activities Data using Machine Learning Techniques
An Improved Associative Classification Algorithm based on Incremental Rules
In Associative classification (AC), the step of rule generation is necessarily exhaustive because of the inherited search problems from the association rule. Besides which, the entire rules set must be induced prior constructing the classifier. This article proposes a new AC algorithm called Dynamic Covering Associative Classification (DCAC) that learns each rule from a training dataset, removes its classified instances, and then learns the next rule from the remaining unclassified data rather than the original training dataset. This ensures that the exhaustive steps of rule evaluation and candidate generation will no longer be needed, thereby maintaining a real time rule generation process. The proposed algorithm constantly amends the support and confidence for each rule rather restricting itself with the support and confidence computed from the original dataset. Experiments on 20 datasets from different domains showed that the proposed algorithm generates higher quality and more accurate classifiers than other AC rule induction approaches
Phishing Websites Dataset
Such Dataset have been collected using our own tool, in the attached pdf document you can find details of the dataset and the features in these datasets
All datasets were collected using our own tool (Mohammad, McCluskey, & Thabtah, 2012), and based on the extraction rules suggested in this article
A Dynamic Self-Structuring Neural Network
Creating a neural network based classification model is commonly accomplished using the trial and error technique. However, the trial and error structuring method have several difficulties such as time and availability of experts. In this article, an algorithm that simplifies structuring neural network classification models has been proposed. The algorithm aims at creating a large enough structure to learn models from the training dataset that can be generalised well on the testing dataset. Our algorithm dynamically tunes the structure parameters during the training phase aiming to derive accurate non-overfitting classifiers. The proposed algorithm has been applied to phishing websites classification problem and it shows competitive results with respect to various evaluation measures such as Harmonic Mean (F1-score), precision, accuracy, etc
Crime Analyses Using Data Analytics
One potential approach for crime analysis that has shown promising results is data analytics, particularly descriptive and predictive techniques. Data analytics can explore former criminal incidents seeking hidden correlations and patterns, which potentially could be used in crime prevention and resource management. The purpose of this research is to build a crime analysis model using supervised techniques to predict the arrest status of serious crimes in Chicago. This is based on specific indicators, such as timeframe, location in terms of district, community, and beat, and crime type among others. We used time series and clustering techniques to help us identify influential features. Supervised machine learning algorithms then modelled the subset of features against incidents related to battery and assaults in specific timeframes and locations to predict the arrest status response variable. The models derived from Naïve Bayes, Decision Tree, and Support Vector Machine (SVM) algorithms reveal a high predictive accuracy rate at certain times in some communities within Chicago
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