November 2016, No 6
On the Security of Two Ownership Transfer Protocols and Their Improvements Print E-mail

On the Security of Two Ownership Transfer Protocols and Their Improvements

Nasour Bagheri1, Seyed Aghili1, and Masoumeh Safkhani2

1 Electrical Engineering Department, Shahid Rajaee Teacher Training University, Iran

2 Computer Engineering Department, Shahid Rajaee Teacher Training University, Iran

Abstract: In recent years, Radio Frequency Identification (RFID) systems are widely used in many applications. In some applications, the ownership of an RFID tag might change. To provide a solution, researchers have proposed several ownership transfer protocols based on encryption functions for RFID-tagged objects. In this paper, we consider the security of Kapoor and Piramuthu [3] ownership transfer protocol and Kapoor et al. [4] ownership transfer protocol. More precisely, we present de-synchronization attacks against these protocols. The success probability of all attacks is 1 while the complexity is only two runs of protocol. Finally, we present our suggestions to improve the security of these protocols.

Keywords: RFID, cryptanalysis, ownership transfer protocol, de-synchronization attack.

Received February 4, 2014; accepted December 23, 2015


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Enhancing Cloud Security Based On Group Signature Print E-mail

Enhancing Cloud Security Based On Group


Arumugam Sakthivel

Department of Computer Science and Engineering, Kalasalingam University, India

Abstract: Using the eccentric of truncated preservation, cloud computing gives a reasonable and proficient result for distributing cluster resources among cloud clients. Regrettably, distributing data in a multi user fashion whereas maintaining data and individuality privacy from an unfaith cloud is quiet a puzzling concern, because of the recurrent change of the participation. The proposed system focuses a protected multi user data distributing method, for active clusters in the cloud. Using group signature and active broadcast encryption methods, any cloud client can secretly distribute data among others. Provisionally, the storage load and encryption calculation cost of the proposed method is liberated from the amount of repealed clients. Additionally, the security and performance analysis of the proposed method shows that, much more efficient and secure than all other existing methods.

Keywords: Active broadcast encryption, cloud, data distribution, group signature.

Received September 12, 2014; accepted June 18, 2015


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Effective and Efficient Utility Mining Technique for Incremental Dataset Print E-mail

Effective and Efficient Utility Mining Technique for Incremental Dataset

Kavitha JeyaKumar1, Manjula Dhanabalachandran1, and Kasthuri JeyaKumar2

1Department of Computer Science and Engineering, Anna University, India

2Department of Electronics and Communication Engineering, SRM University, India

Abstract: Traditional association rule mining, which is based on frequency values of items, cannot meet the demands of different factors in real world applications. Thus utility mining is presented to consider additional measures, such as profit or price according to user preference. Although several algorithms were proposed for mining high utility itemsets, they incur the problem of producing large number of candidate itemsets, results in performance degradation in terms of execution time and space requirement. On the other hand when the data come intermittently, the incremental and interactive data mining approach needs to be processed to reduce unnecessary calculations by using previous data structures and mining results. In this paper, an incremental mining algorithm for efficiently mining high utility itemsets is proposed to handle the above situation. It is based on the concept of Utility Pattern Growth (UP-Growth) for mining high utility itemsets with a set of effective strategies for pruning candidate itemsets and Fast Update (FUP) approach, which first partitions itemsets into four parts according to whether they are high-transaction weighted utilization items in the original and newly inserted transactions. Experimental results show that the proposed Fast Update Utility Pattern Tree (FUUP) approach can thus achieve a good trade between execution time and tree complexity.

Keywords: Data mining, utility mining, incremental mining.

Received January 30, 2014; accepted October 14, 2014



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Intelligent Human Resource Information System (i-HRIS): A Holistic Decision Support Framework for HR Print E-mail

Intelligent Human Resource Information System (i-HRIS): A Holistic Decision Support Framework for HR Excellence

Abdul-Kadar Masum1, Loo-See Beh2, Abul-Kalam Azad3, and Kazi Hoque4

1Department of Administrative Studies and Politics, University of Malaya, Malaysia

2Department of Administrative Studies and Politics, University of Malaya, Malaysia,

3Department of Applied Statistics, University of Malaya, Malaysia

4Department of Educational Management, Planning and Policy, University of Malaya, Malaysia

Abstract: Nowadays, Human Resource Information System (HRIS) plays a strategic role in the decision making process for effective and efficient Human Resource Management (HRM). For Human Resource (HR) decision making, most of the researchers propose expert systems or knowledge-based systems. Unfortunately, there are some limitations in both of expert system and knowledge-based system. In this paper, we have proposed a framework of Intelligent Human Resource Information System (i-HRIS) applying Intelligent Decision Support System (IDSS) along with Knowledge Discovery in Database (KDD) to improve structured, especially semistructured and unstructured HR decision making process. Moreover, the proposed HR IDSS stores and processes information with a set of Artificial Intelligent (AI) tools such as knowledge-based reasoning, machine learning and others. These AI tools are used to discover useful information or knowledge from past data and experience to support decision making process. We have likewise attempted to investigate IDSS applications for HR problems applying hybrid intelligent techniques such as machine learning and knowledge-based approach for new knowledge extraction and prediction. In summation, the proposed framework consists of input subsystems, decision making subsystems and output subsystems with ten HR application modules.  

Keywords: HRIS, KDD, DSS, framework.

Received October 1, 2014; accepted August 12, 2015



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Financial Time Series Forecasting Using Hybrid Wavelet-Neural Model Print E-mail

Financial Time Series Forecasting Using Hybrid Wavelet-Neural Model

Jovana Božić, Djordje Babić

School of Computing, University Union, Belgrade, Serbia

Abstract: In this paper, we examine and discuss results of financial time series prediction by using a combination of wavelet transform, neural networks and statistical time series analytical techniques. The analyzed hybrid model combines the capabilities of wavelet packet transform and neural networks that can capture hidden but crucial structure attributes embedded in the time series. The input data is decomposed into a wavelet representation using two different resolution levels. For each of the new time series, a neural network is created, trained and used for prediction. In order to create an aggregate forecast, the individual predictions are combined with statistical features extracted from the original input. Additional to the conclusion that the increase in resolution level does not improve the prediction accuracy, the analysis of obtained results indicates that the suggested model presents satisfactory predictor. The results also serve as an indication that denoising process generates more accurate results when applied.

Keywords: Time-series forecasting, wavelet packet transform, neural networks.

Received November 23, 2014; accepted January 20, 2016


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