SAK-AKA: A Secure Anonymity Key of Authentication and Key Agreement protocol for LTE network

Shadi Nashwan

Department of Computer Science and Information, Aljouf University, Saudi Arabia

Abstract: 3^rdGeneration Partnership Project (3GPP) has proposed the Authentication and Key Agreement (AKA) protocol to achieve the security requirements of Evolved Packet System (EPS) in the Long Term Evolution (LTE) network, called (EPS-AKA) protocol. Nevertheless, the EPS-AKA protocol is still has some drawbacks in authentication process due to inherit some weaknesses from the predecessor protocols. This paper proposes a secure anonymity Key of Authentication and Key Agreement (SAK-AKA) protocol for LET network to enhance the security level of EPS-AKA protocol. The same security architecture of EPS-AKA is used by the proposed protocol without adding extra cost to the system. Specifically, the SAK-AKA protocol increases the difficulty of defeating the authentication messages by complete concealing of IMSI with perfect forward security. The extensive security analysis proves that the SAK-AKA protocol is secure against the drawbacks of current EPS-AKA protocol. Moreover, the performance analysis in terms of the bandwidth consumption, the authentication transmission overhead and the storage consumption demonstrates that the SAK-AKA protocol relatively is more efficient than the current EPS-AKA protocol.

Keywords: 3GPP, LTE network, IPsec protocol, UMTS-AKA protocol, EPS-AKA protocol.

Received March 21, 2017; accepted May 28, 2017

Rule Schema Multi-Level for Local Patterns

Analysis: Application in Production Field

Salim Khiat¹, Hafida Belbachir², and Sid Rahal³

¹Computer Sciences Department, University of science and technology–Mohamed Boudiaf Oran, Algeria

²The Science and Technology University USTO, Algeria

³System and Data Laboratory (LSSD)

Abstract: Recently, Multi-Database Mining (MDBM) for association rules has been recognized as an important and timely research area in the Knowledge Discovery Database (KDD) community. It consists of mining different databases in order to obtain frequent patterns which are forwarded to a centralized place for global pattern analysis. Various synthesizing models [8,9,13,14,15,16] have been proposed to build global patterns from the forwarded patterns. It is desired that the synthesized rules from such forwarded patterns must closely match with the mono-mining results, ie., the results that would be obtained if all the databases are put together and mining has been done. When the pattern is present in a site but fails to satisfy the minimum support threshold value, it is not allowed to take part in the pattern synthesizing process. Therefore this process can lose some interesting patterns which can help the decision maker to make the right decisions. To adress this problem, we propose to integrate the users knowledge in the local and global mining process. For that we describe the users beliefs and expectation by the rule schemas multi-level and integrate them in both the local association rules mining and in the synthesizing process. In this situation we get true global patterns of select items as there is no need to estimate them. Furthermore, a novel Condensed Patterns Tree (CP_TREE)structure is defined in order to store the candidates patterns for all organization levels which can improve the time processing and reduce the space requirement. In addition CP_TREE structure facilitate the exploration and the projection of the candidates patterns in differents levels. finally We conduct some experimentations in real world databases which are the production field and demonstrate the effectivlness of the CP_TREE structure on time processing and space requirement.

Keywords: Schema, association rules, exceptional rules, global rules, ontology.

Features Modelling in Discrete and Continuous Hidden Markov Models for Handwritten Arabic Words Recognition

Amine Benzenache¹, Hamid Seridi¹, and Herman Akdag²

¹LabSTIC, University of 8 Mai 1945 of Guelma, Algeria

²LIASD, University Paris 8, France

Abstract: The arab writing is originally cursive, difficult to segment and has a great variability. To overcome these problems, we propose two holistic approaches for the recognition of the handwritten arabic words in a limited vocabulary based on the Hidden Markov Models (HMMs): discrete with wk-means and continuous. In the suggested approach, each word of the lexicon is modelled by a discrete or continuous HMM. After a series of pre-processing, the word image is segmented from right to left in succession frames of fixed or variable size in order to generate a sequence vector of statistical and structural parameters which will be submitted to two classifiers to identify the word. To illustrate the efficiency of the proposed systems, significant experiments are carried out on IFN/ENIT benchmark database.

Keywords: Recognition of the handwritten arabic words, holistic approach, DHMMs, CHMMs, k-means, wk-means, algorithm of Viterbi, modified EM algorithm.

Service-Oriented Process Modelling for Device Control in Future Networks

Muhammad Khan and DoHyeun Kim

Computer Engineering Department, Jeju National University, South Korea

Abstract: The recent advancements in the fields of electronics, information and communication technologies have paved a pathway towards a world-wide future network of connected smart devices. Researchers from industry and academia are taking even more interests in the realization of such an infrastructure where a seamless interaction of sensing and actuating devices will take place in order to provide valuable services to the human kind and other systems. So far the major focus of research is towards the connectivity, management and control of sensing devices and no major attention has been given to the control of actuating devices in such an environment. This paper presents a generic process model for actuating device control service in future networks. A prototype implementation of the proposed model based on the presented platform has been described along with the performance analysis of the proposed model.

Keywords: Process modelling, future networks, device profile, device control.

Generalization of Impulse Noise Removal

Hussain Dawood¹, Hassan Dawood², and Ping Guo³

¹Faculty of Computing and Information Technology, University of Jeddah, Saudi Arabia

²Department of Software Engineering, University of Engineering and Technology, Pakistan

³Image Processing and Pattern Recognition Laboratory, Beijing Normal University, China

Abstract: In this paper, a generalization for the identification and removal of an impulse noise is proposed. To remove the salt-and-pepper noise an Improved Directional Weighted Median Filter (IDWMF) is proposed. Number of optimal direction are proposed to increase from four to eight directions to preserve the edges and to identify the noise, effectively. Modified Switching Median Filter (MSMF) is proposed to replace the identified noisy pixel. In which, two special cases are considered to replace the identified noisy pixel. To remove the random-valued impulse noise, we have proposed an efficient random-valued impulse noise identifier and removal algorithm named as Local Noise Identifier and Multi-texton Removal (LNI-MTR). We have proposed to use the local statistics of four neighbouring and the central pixel for the identification of noisy pixel in current sliding window. The pixel identified as noisy, is proposed to replace by using the information of multi-texton in current sliding window. Experimental results show that the proposed methods cannot only identify the impulse noise efficiently, but also can preserve the detailed information of an image.

Keywords: Directional weighted median filter, multi-texton, impulse noise, random-valued impulse noise, salt-and-pepper noise, noise identification, modified switching median filter.

Received September 22, 2014; accepted April 23, 2015

A ­­­­­Novel Approach for Sentiment Analysis of Punjabi Text using SVM

Amandeep Kaur and Vishal Gupta

Department Computer Science and Engineering, Panjab University, India

Abstract: Opinion mining or sentiment analysis is to identify and classify the sentiments/opinion/emotions from text. Over the last decade, in addition to english language, many indian languages include interest of research in this field. For this paper, we compared many approaches developed till now and also reviewed previous researches done in case of indian languages like telugu, Hindi and Bengali. We developed a hybrid system for Sentiment analysis of Punjabi text by integrating subjective lexicon, N-gram modelling and support vector machine. Our research includes generation of corpus data, algorithm for Stemming, generation of punjabi subjective lexicon, developing Feature set, Training and testing support vector machine. Our technique proves good in terms of accuracy on the testing data. We also reviewed the results provided by previous approaches to validate the accuracy of our system.

Keywords: Sentiment analysis, subjective lexicon, punjabi language, n-gram modeling, support vector machine.

Combination of Multiple Classifiers for Off-Line Handwritten Arabic Word Recognition

Rachid Zaghdoudi and Hamid Seridi

laboratory of Science and Information Technologies and Communication, University of 08 may 1945, Algeria

Abstract: This study investigates the combination of different classifiers to improve Arabic handwritten word recognition. Features based on Discrete Cosine Transform (DCT) and Histogram of Oriented Gradients (HOG) are computed to represent the handwritten words. The dimensionality of the HOG features is reduced by applying Principal Component Analysis (PCA). Each set of features is separately fed to two different classifiers, support vector machine (SVM) and fuzzy k-nearest neighbor (FKNN) giving a total of four independent classifiers. A set of different fusion rules is applied to combine the output of the classifiers. The proposed scheme evaluated on the IFN/ENIT database of Arabic handwritten words reveal that combining the classifiers results in improved recognition rates which, in some cases, outperform the state-of-the-art recognition systems.

 Keywords: Handwritten Arabic word recognition; Classifier combination; Support vector machine; Fuzzy K-nearest neighbor; Discrete cosine transform; Histogram of oriented gradients.

Received September 22, 2014; accepted August 31, 2015

A Metrics Driven Design Approach for Real Time

Environment Application

Mahmood Ahmed and Muhammad Shoaib

Department of computer science and engineering, University of engineering and technology lahore, Pakistan.

Abstract: Design of real time environment application is the most exigent task for the designers comparing to non real time application design. The stringent timing requirement for task completion is the problem to handle at design time. The design complexity is increased manifolds when object oriented design methods are used and task deadlines are introduced at design stage. There are many design methodologies available for the real time systems but as far as the researcher is concerned none addresses all the problems of real time system design specially the issues of deadline inheritance and dynamic behavior of system if deadlines are introduced at early stages of the design. Most of the methodologies leave the task of handling the timing constraints for the implementation phase at the programming language level. In this paper we have proposed a design approach incorporated with our novel design metrics verification for measuring the design of real time environment applications. The metrics are measured for design of a real time weapon delivery system and it is illustrated that how design quality can be assessed before implementation.

Keywords: Deadlines, timed state statecharts, design metrics, real time systems

  Received November 26, 2011; accepted June 11, 2012

Full text 

Diagnosis of Leptomeningeal Metastases Disease

in MRI Images by Using Image Enhancement

Methods

Mehmet Gül¹, Sadık Kara¹, Abdurrahman Işıkdoğan², and Yusuf Yarar³

¹Biomedical Engineering Institute, Fatih University, Istanbul

²Hospital of Oncology, Dicle University, Diyarbakır

³Selahaddin’i Eyyubi Hospital, Diyarbakır

Abstract: Leptomeningeal Metastases (LM) disease is the advanced stages of some complicated cancers. It Contaminates in the Cerebrospinal Fluid (CSF). Tumors might be in macroscopic or microscopic sizes. The medical operation is more risky than other cancers. Consequently, diagnosis of leptomeningeal metastases is important. Different methods are used to diagnose LM disease such as CSF examination and imaging systems Magnetic Resonance Imaging (MRI) or Computer Tomography (CT) examination. CSF examination result is more accurate compared to CT or MRI imaging systems. However imaging systems’ results are taken more early than CSF examination. Some details in MRI images are hidden and if the proper image enhancement method is used, the details will be revealed. Diagnosis of LM disease can be earlier with accurate results at that time. In this study, some image enhancement methods were used. The probability of result of Logarithmic Transformation (LT) method and Power-Law Transformation (PLT) method were almost the same and result was p=0.000 (p<0.001), and statistically high result was obtained. The probability of Contrast Stretching (CS) method was p=0.031 (p<0.05), and this result was statistically significant. The other four methods’ results were insignificant. These methods are Image Negatives Transformation (INT) method, thresholding transformations method; Gray-Level Slicing (GLS) method and Bit-Plane Slicing (BPS) method.

Keywords: Cerebrospinal Fluid (CSF) examination, Computed Tomography (CT), Image Enhancement methods, Leptomeningeal Metastases, Magnetic Resonance Imaging (MRI).

Received March 23, 2015; accepted August 12, 2015

Full text 

Analysis and Performance Evaluation of

Cosine Neighbourhood Recommender System

Kola Periyasamy¹, Jayadharini Jaiganesh¹, Kanchan Ponnambalam¹, Jeevitha Rajasekar¹, and Kannan Arputharaj²

¹Department of Information Technology, Anna University, India.

²Department of Information Science and Technology, Anna University, India.

Abstract: Growth of technology and innovation leads to large and complex data which is coined as Bigdata. As the quantity of information increases, it becomes more difficult to store and process data. The greater problem is finding right data from these enormous data. These data are processed to extract the required data and recommend high quality data to the user. Recommender system analyses user preference to recommend items to user. Problem arises when Bigdata is to be processed for Recommender system. Several technologies are available with which big data can be processed and analyzed. Hadoop is a framework which supports manipulation of large and varied data. In this paper, a novel approach Cosine Neighbourhood Similarity measure is proposed to calculate rating for items and to recommend items to user and the performance of the recommender system is evaluated under different evaluator which shows the proposed Similarity measure is more accurate and reliable.

Keywords: Big Data, Recommender System, Cosine Neighbourhood Similarity, Recommender Evaluator.

Received April 28, 2014; accepted June 12, 2014

An Approach for Instance Based Schema Matching with Google Similarity and Regular Expression

Osama Mehdi, Hamidah Ibrahim, and Lilly Affendey

Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, Malaysia

Abstract: Instance based schema matching is the process of comparing instances from different heterogeneous data sources in determining the correspondences of schema attributes. It is a substitutional choice when schema information is not available or might be available but worthless to be used for matching purpose. Different strategies have been used  by various instance based schema matching approaches for discovering correspondences between schema attributes. These strategies are neural network, machine learning, information theoretic discrepancy and rule based. Most of these approaches treated instances including instances with numeric values as strings which prevents discovering common patterns or performing statistical computation between the numeric instances. As a consequence, this causes unidentified matches especially for numeric instances. In this paper, we propose an approach that addresses the above limitation of the previous approaches. Since we only fully exploit the instances of the schemas for this task, we rely on strategies that combine the strength of Google as a web semantic and regular expression as pattern recognition. The results show that our approach is able to find 1-1 schema matches with high accuracy in the range of 93%-99% in terms of Precision (P), Recall (R), and F-measure (F). Furthermore, the results showed that our proposed approach outperformed the previous approaches although only a sample of instances is used instead of considering the whole instances during the process of instance based schema matching as used in the previous works.

Keywords: Schema matching, instance based schema matching, Google similarity, regular expression.

Received April 24, 2014; accepted August 31, 2015