CDKP - 2014

Accepted Papers

Assessing Email Classification Attributes using Feature Selection
Issa Qabaja¹, Fadi Thabtah² and Aladdin Ayesh³,
Demontfort University,United Kingdom ^1,3,Canadian University of Dubai,United Arab Emirates ².
ABSTRACT
Email phishing is one of the vital problems in the online security research domain that has attracted several scholars due to its impact on the users purchase transactions which they perform online on a daily basis. One aspect to reach a good performance by the phishing detection algorithm in this problem is to identify the minimal set of features that significantly have an impact on the phishing detection rate. This paper investigates three known feature selection methods named Information Gain (IG), Chi-square and Correlation Features Set (CFS) on the email phishing problem to separate high influential features from low influential ones. We measure the detection rate by applying four data mining algorithms on a large set of email features, i.e. 47 features. We compare the accuracy of these algorithms on the original data set and after feature selection methods have been applied (reduced features set). After conducting experiments, the results show 12 common significant features from the 47 features set by the feature selection methods. Further, the average accuracy derived by the data mining algorithms on the reduced 12-features set was slight deteriorated when compared with the one derived from the 47-features set.
Combining Decision Trees and K-NN for Case-Based Planning
Sofia Benbelkacem, Baghdad Atmani and Mohamed Benamina,
Computer Science Laboratory of Oran (LIO), University of Oran, Algeria.
ABSTRACT
In everyday life, we are often faced with similar problems which we resolve with our experience. Case-based reasoning is a paradigm of problem solving based on past experience. Thus, case-based reasoning is considered as a valuable technique for the implementation of various tasks involving solving planning problem. Planning is considered as a decision support process designed to provide resources and required services to achieve specific objectives, allowing the selection of a better solution among several alternatives. However, we propose to exploit decision trees and k-NN combination to choose the most appropriate solutions. In a previous work [1], we have proposed a new planning approach guided by case-based reasoning and decision tree, called DTR, for case retrieval. In this paper, we use a classifier combination for similarity calculation in order to select the best solution to the target case. Thus, the use of the decision trees and k-NN combination allows improving the relevance of results and finding the most relevant cases. Index Terms- Cloud computing, Active storage data privacy, self-destructing, data privacy
A Boolean Modeling for Improving the Algorithm Apriori
Abdelhak Mansoul and Baghdad Atmani
Oran University, Algeria
ABSTRACT
Mining association rules is one of the most important data mining tasks. Its purpose is to generate intelligible relations between attributes in a database. However, its use in practice is difficult and still raises several challenges, in particular, the number of learned rules is often very large. Several techniques for reducing the number of rules have been proposed as measures of quality, syntactic filtering constraints, etc. However, these techniques do not limit the shortcomings of these methods. In this paper, we propose a new approach to mine association, assisted by a Boolean modeling of results in order to mitigate the shortcomings mentioned above and propose a cellular automaton based on a boolean process for mining, optimizing, managing and representing of the learned rules.
Qubit Data Structures for Analyzing Computing Systems
Vladimir Hahanov¹, Eugenia Litvinova², Svitlana Chumachenko³ and Wajeb Gharibi⁴
Kharkov National University of Radioelectronics^1,2,3,,Jazan University⁴.
ABSTRACT
Qubit models and methods for improving the performance of software and hardware for analyzing digital devices through increasing the dimension of the data structures and memory are proposed. The basic concepts, terminology and definitions necessary for the implementation of quantum computing when analyzing virtual computers are introduced. The investigation results concerning design and modeling computer systems in a cyberspace based on the use of twocomponent structure are presented.
Arabic Tweets Categorization Based on Rough Set Theory
Mohammed Bekkali and Abdelmonaime Lachkar
L.S.I.S, E.N.S.A,University Sidi Mohamed Ben Abdellah (USMBA) Morocco.
ABSTRACT
Twitter is a popular microblogging service where users create status messages (called "tweets"). These tweets sometimes express opinions about different topics; and are presented to the user in a chronological order. This format of presentation is useful to the user since the latest tweets from are rich on recent news which is generally more interesting than tweets about an event that occurred long time back. Merely, presenting tweets in a chronological order may be too embarrassing to the user, especially if he has many followers. Therefore, there is a need to separate the tweets into different categories and then present the categories to the user. Nowadays Text categorization (TC) becomes more significant especially for the Arabic language which is one of the most complex languages. In this paper, in order to improve the accuracy of tweets categorization a system based on Rough Set Theory is proposed for enrichment the document's representation. The effectiveness of our system was evaluated and compared in term of the F-measure of the Naive Bayesian classifier and the Support Vector Machine classifier.
Variable Length Key Based Visual Cryptography Scheme for Color Imgae
Akhil Anjikar, Prashant Dahiwale and Suchita Tarare
Rajiv Gandhi College of Engineering & Research,India
ABSTRACT
Visual Cryptography is a special encryption technique that encrypts the secret image into n number of shares to hide information in images in such a way that it can be decrypted by the human visual system. It is imperceptible to reveal the secret information unless a certain number of shares (k) or more are superimposed. Simple visual cryptography is very insecure. Variable length key based visual cryptography for color image uses a variable length Symmetric Key based Visual Cryptographic Scheme for color images where a secret key is used to encrypt the image and division of the encrypted image is done using Random Number. Unless the secret key, the original image will not be decrypted. Here secret key ensures the security of image. This paper describes the overall process of above scheme. Encryption process encrypts the Original Image using variable length Symmetric Key, gives encrypted image. Share generation process divides the encrypted image into n number of shares using random number. Decryption process stacks k number of shares out of n to reconstruct encrypted image and uses the same key for decryption.



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Home Paper Submission Program Committee Accepted Papers Contact Us	Accepted Papers Assessing Email Classification Attributes using Feature Selection Issa Qabaja¹, Fadi Thabtah² and Aladdin Ayesh³, Demontfort University,United Kingdom ^1,3,Canadian University of Dubai,United Arab Emirates ². ABSTRACT Email phishing is one of the vital problems in the online security research domain that has attracted several scholars due to its impact on the users purchase transactions which they perform online on a daily basis. One aspect to reach a good performance by the phishing detection algorithm in this problem is to identify the minimal set of features that significantly have an impact on the phishing detection rate. This paper investigates three known feature selection methods named Information Gain (IG), Chi-square and Correlation Features Set (CFS) on the email phishing problem to separate high influential features from low influential ones. We measure the detection rate by applying four data mining algorithms on a large set of email features, i.e. 47 features. We compare the accuracy of these algorithms on the original data set and after feature selection methods have been applied (reduced features set). After conducting experiments, the results show 12 common significant features from the 47 features set by the feature selection methods. Further, the average accuracy derived by the data mining algorithms on the reduced 12-features set was slight deteriorated when compared with the one derived from the 47-features set. Combining Decision Trees and K-NN for Case-Based Planning Sofia Benbelkacem, Baghdad Atmani and Mohamed Benamina, Computer Science Laboratory of Oran (LIO), University of Oran, Algeria. ABSTRACT In everyday life, we are often faced with similar problems which we resolve with our experience. Case-based reasoning is a paradigm of problem solving based on past experience. Thus, case-based reasoning is considered as a valuable technique for the implementation of various tasks involving solving planning problem. Planning is considered as a decision support process designed to provide resources and required services to achieve specific objectives, allowing the selection of a better solution among several alternatives. However, we propose to exploit decision trees and k-NN combination to choose the most appropriate solutions. In a previous work [1], we have proposed a new planning approach guided by case-based reasoning and decision tree, called DTR, for case retrieval. In this paper, we use a classifier combination for similarity calculation in order to select the best solution to the target case. Thus, the use of the decision trees and k-NN combination allows improving the relevance of results and finding the most relevant cases. Index Terms- Cloud computing, Active storage data privacy, self-destructing, data privacy A Boolean Modeling for Improving the Algorithm Apriori Abdelhak Mansoul and Baghdad Atmani Oran University, Algeria ABSTRACT Mining association rules is one of the most important data mining tasks. Its purpose is to generate intelligible relations between attributes in a database. However, its use in practice is difficult and still raises several challenges, in particular, the number of learned rules is often very large. Several techniques for reducing the number of rules have been proposed as measures of quality, syntactic filtering constraints, etc. However, these techniques do not limit the shortcomings of these methods. In this paper, we propose a new approach to mine association, assisted by a Boolean modeling of results in order to mitigate the shortcomings mentioned above and propose a cellular automaton based on a boolean process for mining, optimizing, managing and representing of the learned rules. Qubit Data Structures for Analyzing Computing Systems Vladimir Hahanov¹, Eugenia Litvinova², Svitlana Chumachenko³ and Wajeb Gharibi⁴ Kharkov National University of Radioelectronics^1,2,3,,Jazan University⁴. ABSTRACT Qubit models and methods for improving the performance of software and hardware for analyzing digital devices through increasing the dimension of the data structures and memory are proposed. The basic concepts, terminology and definitions necessary for the implementation of quantum computing when analyzing virtual computers are introduced. The investigation results concerning design and modeling computer systems in a cyberspace based on the use of twocomponent structure are presented. Arabic Tweets Categorization Based on Rough Set Theory Mohammed Bekkali and Abdelmonaime Lachkar L.S.I.S, E.N.S.A,University Sidi Mohamed Ben Abdellah (USMBA) Morocco. ABSTRACT Twitter is a popular microblogging service where users create status messages (called "tweets"). These tweets sometimes express opinions about different topics; and are presented to the user in a chronological order. This format of presentation is useful to the user since the latest tweets from are rich on recent news which is generally more interesting than tweets about an event that occurred long time back. Merely, presenting tweets in a chronological order may be too embarrassing to the user, especially if he has many followers. Therefore, there is a need to separate the tweets into different categories and then present the categories to the user. Nowadays Text categorization (TC) becomes more significant especially for the Arabic language which is one of the most complex languages. In this paper, in order to improve the accuracy of tweets categorization a system based on Rough Set Theory is proposed for enrichment the document's representation. The effectiveness of our system was evaluated and compared in term of the F-measure of the Naive Bayesian classifier and the Support Vector Machine classifier. Variable Length Key Based Visual Cryptography Scheme for Color Imgae Akhil Anjikar, Prashant Dahiwale and Suchita Tarare Rajiv Gandhi College of Engineering & Research,India ABSTRACT Visual Cryptography is a special encryption technique that encrypts the secret image into n number of shares to hide information in images in such a way that it can be decrypted by the human visual system. It is imperceptible to reveal the secret information unless a certain number of shares (k) or more are superimposed. Simple visual cryptography is very insecure. Variable length key based visual cryptography for color image uses a variable length Symmetric Key based Visual Cryptographic Scheme for color images where a secret key is used to encrypt the image and division of the encrypted image is done using Random Number. Unless the secret key, the original image will not be decrypted. Here secret key ensures the security of image. This paper describes the overall process of above scheme. Encryption process encrypts the Original Image using variable length Symmetric Key, gives encrypted image. Share generation process divides the encrypted image into n number of shares using random number. Decryption process stacks k number of shares out of n to reconstruct encrypted image and uses the same key for decryption.
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Accepted Papers

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