Search results for: Data security

Authors: Radoslav Židek, Veronika Šidlová, Radovan Kasarda, Birgit Fuerst-Waltl

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Machine learning represents a set of topics dealing with the creation and evaluation of algorithms that facilitate pattern recognition, classification, and prediction, based on models derived from existing data. The data can present identification patterns which are used to classify into groups. The result of the analysis is the pattern which can be used for identification of data set without the need to obtain input data used for creation of this pattern. An important requirement in this process is careful data preparation validation of model used and its suitable interpretation. For breeders, it is important to know the origin of animals from the point of the genetic diversity. In case of missing pedigree information, other methods can be used for traceability of animal´s origin. Genetic diversity written in genetic data is holding relatively useful information to identify animals originated from individual countries. We can conclude that the application of data mining for molecular genetic data using supervised learning is an appropriate tool for hypothesis testing and identifying an individual.

Keywords: Genetic data, Pinzgau cattle, supervised learning.

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Authors: Leau Y. B., Dinna Nina M. N., Habeeb S. A. H., Jetol B.

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Authentication plays a vital role in many secure systems. Most of these systems require user to log in with his or her secret password or pass phrase before entering it. This is to ensure all the valuables information is kept confidential guaranteeing also its integrity and availability. However, to achieve this goal, users are required to memorize high entropy passwords or pass phrases. Unfortunately, this sometimes causes difficulty for user to remember meaningless strings of data. This paper presents a new scheme which assigns a weight to each personal question given to the user in revealing the encrypted secrets or password. Concentration of this scheme is to offer fault tolerance to users by allowing them to forget the specific password to a subset of questions and still recover the secret and achieve successful authentication. Comparison on level of security for weight-based and weightless secret recovery scheme is also discussed. The paper concludes with the few areas that requires more investigation in this research.

Keywords: Secret Recovery, Personal Entropy, Cryptography, Secret Sharing and Key Management.

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Authors: A. A. Aiyeloja, A.T. Oladele, O. Tumulo

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Raphia wine is an important forest product with cultural significance besides its use as medicine and food in southern Nigeria. This work aims to evaluate the profitability of Raphia wine production and marketing in Sapele Local Government Area, Nigeria. Four communities (Sapele, Ogiede, Okuoke and Elume) were randomly selected for data collection via questionnaires among producers and marketers. A total of 50 producers and 34 marketers were randomly selected for interview. Data was analyzed using descriptive statistics, profit margin, multiple regression and rate of returns on investment (RORI). Annual average profit was highest in Okuoke (Producers – N90, 000.00, Marketers - N70, 000.00) and least in Sapele (Producers N50, 000.00, Marketers – N45, 000.00). Calculated RORI for marketers were Elume (40.0%), Okuoke (25.0%), Ogiede (33.3%) and Sapele (50.0%). Regression results showed that location has significant effects (0.000, ρ ≤ 0.05) on profit margins. Male (58.8%) and female (41.2%) invest in Raphia wine marketing, while males (100.0%) dominate production. Results showed that Raphia wine has potentials to generate household income, enhance food security and improve quality of life in rural, semi-urban and urban communities. Improved marketing channels, storage facilities and credit facilities via cooperative groups are recommended for producers and marketers by concerned agencies.

Keywords: Raphia wine, Profit margin, RORI, Livelihood, Nigeria.

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Authors: Baisa L. Gunjal, Suresh N. Mali

Abstract:

Medical images require special safety and confidentiality because critical judgment is done on the information provided by medical images. Transmission of medical image via internet or mobile phones demands strong security and copyright protection in telemedicine applications. Here, highly secured and robust watermarking technique is proposed for transmission of image data via internet and mobile phones. The Region of Interest (ROI) and Non Region of Interest (RONI) of medical image are separated. Only RONI is used for watermark embedding. This technique results in exact recovery of watermark with standard medical database images of size 512x512, giving 'correlation factor' equals to 1. The correlation factor for different attacks like noise addition, filtering, rotation and compression ranges from 0.90 to 0.95. The PSNR with weighting factor 0.02 is up to 48.53 dBs. The presented scheme is non blind and embeds hospital logo of 64x64 size.

Keywords: Compression, DWT, ROI, Scrambling, Vertices

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Authors: Y. Harold Robinson, M. Rajaram

Abstract:

Designing cost-efficient, secure network protocols for Wireless Sensor Networks (WSNs) is a challenging problem because sensors are resource-limited wireless devices. Security services such as authentication and improved pairwise key establishment are critical to high efficient networks with sensor nodes. For sensor nodes to correspond securely with each other efficiently, usage of cryptographic techniques is necessary. In this paper, two key predistribution schemes that enable a mobile sink to establish a secure data-communication link, on the fly, with any sensor nodes. The intermediate nodes along the path to the sink are able to verify the authenticity and integrity of the incoming packets using a predicted value of the key generated by the sender’s essential power. The proposed schemes are based on the pairwise key with the mobile sink, our analytical results clearly show that our schemes perform better in terms of network resilience to node capture than existing schemes if used in wireless sensor networks with mobile sinks.

Keywords: Wireless Sensor Networks, predistribution scheme, cryptographic techniques.

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Authors: Sujala.D.Shetty, S.Vadivel, Sakshi Vaghella

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Data mining is the process of sifting through large volumes of data, analyzing data from different perspectives and summarizing it into useful information. One of the widely used desktop applications for data mining is the Weka tool which is nothing but a collection of machine learning algorithms implemented in Java and open sourced under the General Public License (GPL). A web service is a software system designed to support interoperable machine to machine interaction over a network using SOAP messages. Unlike a desktop application, a web service is easy to upgrade, deliver and access and does not occupy any memory on the system. Keeping in mind the advantages of a web service over a desktop application, in this paper we are demonstrating how this Java based desktop data mining application can be implemented as a web service to support data mining across the internet.

Keywords: desktop application, Weka mining, web service

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Authors: Md. Ataullah, Naveen Chauhan

Abstract:

The Address Resolution Protocol (ARP) is used by computers to map logical addresses (IP) to physical addresses (MAC). However ARP is an all trusting protocol and is stateless which makes it vulnerable to many ARP cache poisoning attacks such as Man-in-the-Middle (MITM) and Denial of service (DoS) attacks. These flaws result in security breaches thus weakening the appeal of the computer for exchange of sensitive data. In this paper we describe ARP, outline several possible ARP cache poisoning attacks and give the detailed of some attack scenarios in network having both wired and wireless hosts. We have analyzed each of proposed solutions, identify their strengths and limitations. Finally get that no solution offers a feasible solution. Hence, this paper presents an efficient and secure version of ARP that is able to cope up with all these types of attacks and is also a feasible solution. It is a stateful protocol, by storing the information of the Request frame in the ARP cache, to reduce the chances of various types of attacks in ARP. It is more efficient and secure by broadcasting ARP Reply frame in the network and storing related entries in the ARP cache each time when communication take place.

Keywords: ARP cache poisoning, MITM, DoS

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Authors: Farhad Asadi, Mohammad Javad Mollakazemi, Aref Ghafouri

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Recent research in neural networks science and neuroscience for modeling complex time series data and statistical learning has focused mostly on learning from high input space and signals. Local linear models are a strong choice for modeling local nonlinearity in data series. Locally weighted projection regression is a flexible and powerful algorithm for nonlinear approximation in high dimensional signal spaces. In this paper, different learning scenario of one and two dimensional data series with different distributions are investigated for simulation and further noise is inputted to data distribution for making different disordered distribution in time series data and for evaluation of algorithm in locality prediction of nonlinearity. Then, the performance of this algorithm is simulated and also when the distribution of data is high or when the number of data is less the sensitivity of this approach to data distribution and influence of important parameter of local validity in this algorithm with different data distribution is explained.

Keywords: Local nonlinear estimation, LWPR algorithm, Online training method.

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Authors: Julius Onyancha, Valentina Plekhanova

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One of the significant issues facing web users is the amount of noise in web data which hinders the process of finding useful information in relation to their dynamic interests. Current research works consider noise as any data that does not form part of the main web page and propose noise web data reduction tools which mainly focus on eliminating noise in relation to the content and layout of web data. This paper argues that not all data that form part of the main web page is of a user interest and not all noise data is actually noise to a given user. Therefore, learning of noise web data allocated to the user requests ensures not only reduction of noisiness level in a web user profile, but also a decrease in the loss of useful information hence improves the quality of a web user profile. Noise Web Data Learning (NWDL) tool/algorithm capable of learning noise web data in web user profile is proposed. The proposed work considers elimination of noise data in relation to dynamic user interest. In order to validate the performance of the proposed work, an experimental design setup is presented. The results obtained are compared with the current algorithms applied in noise web data reduction process. The experimental results show that the proposed work considers the dynamic change of user interest prior to elimination of noise data. The proposed work contributes towards improving the quality of a web user profile by reducing the amount of useful information eliminated as noise.

Keywords: Web log data, web user profile, user interest, noise web data learning, machine learning.

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Authors: Walid Cherif

Abstract:

Data mining has, over recent years, seen big advances because of the spread of internet, which generates everyday a tremendous volume of data, and also the immense advances in technologies which facilitate the analysis of these data. In particular, classification techniques are a subdomain of Data Mining which determines in which group each data instance is related within a given dataset. It is used to classify data into different classes according to desired criteria. Generally, a classification technique is either statistical or machine learning. Each type of these techniques has its own limits. Nowadays, current data are becoming increasingly heterogeneous; consequently, current classification techniques are encountering many difficulties. This paper defines new measure functions to quantify the resemblance between instances and then combines them in a new approach which is different from actual algorithms by its reliability computations. Results of the proposed approach exceeded most common classification techniques with an f-measure exceeding 97% on the IRIS Dataset.

Keywords: Data mining, knowledge discovery, machine learning, similarity measurement, supervised classification.

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Authors: Nittaya Kerdprasop, Kittisak Kerdprasop

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Data mining (DM) is the process of finding and extracting frequent patterns that can describe the data, or predict unknown or future values. These goals are achieved by using various learning algorithms. Each algorithm may produce a mining result completely different from the others. Some algorithms may find millions of patterns. It is thus the difficult job for data analysts to select appropriate models and interpret the discovered knowledge. In this paper, we describe a framework of an intelligent and complete data mining system called SUT-Miner. Our system is comprised of a full complement of major DM algorithms, pre-DM and post-DM functionalities. It is the post-DM packages that ease the DM deployment for business intelligence applications.

Keywords: Business intelligence, data mining, functionalprogramming, intelligent system.

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Authors: S. Sarumathi, N. Shanthi, M. Sharmila

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Clustering in data mining is an unsupervised learning technique of aggregating the data objects into meaningful groups such that the intra cluster similarity of objects are maximized and inter cluster similarity of objects are minimized. Over the past decades several clustering tools were emerged in which clustering algorithms are inbuilt and are easier to use and extract the expected results. Data mining mainly deals with the huge databases that inflicts on cluster analysis and additional rigorous computational constraints. These challenges pave the way for the emergence of powerful expansive data mining clustering softwares. In this survey, a variety of clustering tools used in data mining are elucidated along with the pros and cons of each software.

Keywords: Cluster Analysis, Clustering Algorithms, Clustering Techniques, Association, Visualization.

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Authors: Kittisak Kerdprasop, Nittaya Kerdprasop, Pairote Sattayatham

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Data stream analysis is the process of computing various summaries and derived values from large amounts of data which are continuously generated at a rapid rate. The nature of a stream does not allow a revisit on each data element. Furthermore, data processing must be fast to produce timely analysis results. These requirements impose constraints on the design of the algorithms to balance correctness against timely responses. Several techniques have been proposed over the past few years to address these challenges. These techniques can be categorized as either dataoriented or task-oriented. The data-oriented approach analyzes a subset of data or a smaller transformed representation, whereas taskoriented scheme solves the problem directly via approximation techniques. We propose a hybrid approach to tackle the data stream analysis problem. The data stream has been both statistically transformed to a smaller size and computationally approximated its characteristics. We adopt a Monte Carlo method in the approximation step. The data reduction has been performed horizontally and vertically through our EMR sampling method. The proposed method is analyzed by a series of experiments. We apply our algorithm on clustering and classification tasks to evaluate the utility of our approach.

Keywords: Data Stream, Monte Carlo, Sampling, DensityEstimation.

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Authors: Roelien Goede

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Data warehousing success is not high enough. User dissatisfaction and failure to adhere to time frames and budgets are too common. Most traditional information systems practices are rooted in hard systems thinking. Today, the great systems thinkers are forgotten by information systems developers. A data warehouse is still a system and it is worth investigating whether systems thinkers such as Churchman can enhance our practices today. This paper investigates data warehouse development practices from a systems thinking perspective. An empirical investigation is done in order to understand the everyday practices of data warehousing professionals from a systems perspective. The paper presents a model for the application of Churchman-s systems approach in data warehouse development.

Keywords: Data warehouse development, Information systemsdevelopment, Interpretive case study, Systems thinking

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Authors: Mohammed Fethi Khalfi, Malika Kandouci

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Over the past decade, mobile has experienced a revolution that will ultimately change the way we communicate.All these technologies have a common denominator exploitation of computer information systems, but their operation can be tedious because of problems with heterogeneous data sources.To overcome the problems of heterogeneous data sources, we propose to use a technique of adding an extra layer interfacing applications of management or supervision at the different data sources.This layer will be materialized by the implementation of a mediator between different host applications and information systems frequently used hierarchical and relational manner such that the heterogeneity is completely transparent to the VoIP platform.

Keywords: TOIP, Data Integration, Mediation, informationcomputer system, heterogeneous data sources

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Authors: M. Sumana, K. S. Hareesha

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Secure computations are essential while performing privacy preserving data mining. Distributed privacy preserving data mining involve two to more sites that cannot pool in their data to a third party due to the violation of law regarding the individual. Hence in order to model the private data without compromising privacy and information loss, secure multiparty computations are used. Secure computations of product, mean, variance, dot product, sigmoid function using the additive and multiplicative homomorphic property is discussed. The computations are performed on vertically partitioned data with a single site holding the class value.

Keywords: Homomorphic property, secure product, secure mean and variance, secure dot product, vertically partitioned data.

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Authors: Dae-ho Won, Jong-wook Hong, Yeon-Mo Yang, Jinung An

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To reduce accidents in the industry, WSNs(Wireless Sensor networks)- sensor data is used. WSNs- sensor data has the persistence and continuity. therefore, we design and exploit the buffer management system that has the persistence and continuity to avoid and delivery data conflicts. To develop modules, we use the multi buffers and design the buffer management modules that transfer sensor data through the context-aware methods.

Keywords: safe management system, buffer management, context-aware, input data stream

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Authors: Yacine Challal, Abdelmadjid Bouabdallah, Hamida Seba

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Group key management is an important functional building block for any secure multicast architecture. Thereby, it has been extensively studied in the literature. In this paper we present relevant group key management protocols. Then, we compare them against some pertinent performance criteria.

Keywords: Multicast, Security, Group Key Management.

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Authors: Rong-Terng Juang

Abstract:

Autonomous vehicles offer the possibility of significant benefits to social welfare. However, fully automated cars might not be going to happen in the near further. To speed the adoption of the self-driving technologies, many governments worldwide are passing laws requiring data recorders for the testing of autonomous vehicles. Currently, the self-driving vehicle, (e.g., shuttle bus) has to be monitored from a remote control center. When an autonomous vehicle encounters an unexpected driving environment, such as road construction or an obstruction, it should request assistance from a remote operator. Nevertheless, large amounts of data, including images, radar and lidar data, etc., have to be transmitted from the vehicle to the remote center. Therefore, this paper proposes a data compression method of in-vehicle networks for remote monitoring of autonomous vehicles. Firstly, the time-series data are rearranged into a multi-dimensional signal space. Upon the arrival, for controller area networks (CAN), the new data are mapped onto a time-data two-dimensional space associated with the specific CAN identity. Secondly, the data are sampled based on differential sampling. Finally, the whole set of data are encoded using existing algorithms such as Huffman, arithmetic and codebook encoding methods. To evaluate system performance, the proposed method was deployed on an in-house built autonomous vehicle. The testing results show that the amount of data can be reduced as much as 1/7 compared to the raw data.

Keywords: Autonomous vehicle, data recording, remote monitoring, controller area network.

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Authors: Tarandeep Singh, Parvinder S. Sandhu

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The Economic factors are leading to the rise of infrastructures provides software and computing facilities as a service, known as cloud services or cloud computing. Cloud services can provide efficiencies for application providers, both by limiting up-front capital expenses, and by reducing the cost of ownership over time. Such services are made available in a data center, using shared commodity hardware for computation and storage. There is a varied set of cloud services available today, including application services (salesforce.com), storage services (Amazon S3), compute services (Google App Engine, Amazon EC2) and data services (Amazon SimpleDB, Microsoft SQL Server Data Services, Google-s Data store). These services represent a variety of reformations of data management architectures, and more are on the horizon.

Keywords: Data Management in Cloud, AWS, EC2, S3, SQS, TQG.

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Authors: Neerja Bhatnagar, Benjoe A. Juliano, Renee S. Renner

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This paper presents data annotation models at five levels of granularity (database, relation, column, tuple, and cell) of relational data to address the problem of unsuitability of most relational databases to express annotations. These models do not require any structural and schematic changes to the underlying database. These models are also flexible, extensible, customizable, database-neutral, and platform-independent. This paper also presents an SQL-like query language, named Annotation Query Language (AnQL), to query annotation documents. AnQL is simple to understand and exploits the already-existent wide knowledge and skill set of SQL.

Keywords: annotation query language, data annotations, data annotation models, semantic data annotations

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Authors: Souleymane Oumtanaga, Kadjo Tanon Lambert, Koné Tiémoman, Tety Pierre, Dowa N’sreke Florent

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Nowadays data backup format doesn-t cease to appear raising so the anxiety on their accessibility and their perpetuity. XML is one of the most promising formats to guarantee the integrity of data. This article suggests while showing one thing man can do with XML. Indeed XML will help to create a data backup model. The main task will consist in defining an application in JAVA able to convert information of a database in XML format and restore them later.

Keywords: Backup, Proprietary format, parser, syntactic tree.

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Authors: Apkar Salatian

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To relieve the burden of reasoning on a point to point basis, in many domains there is a need to reduce large and noisy data sets into trends for qualitative reasoning. In this paper we propose and describe a new architectural design pattern called REDUCER for reducing large and noisy data sets that can be tailored for particular situations. REDUCER consists of 2 consecutive processes: Filter which takes the original data and removes outliers, inconsistencies or noise; and Compression which takes the filtered data and derives trends in the data. In this seminal article we also show how REDUCER has successfully been applied to 3 different case studies.

Keywords: Design Pattern, filtering, compression.

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Authors: Vesna Kirandziska, Nevena Ackovska, Ana Madevska Bogdanova

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The problem of emotion recognition is a challenging problem. It is still an open problem from the aspect of both intelligent systems and psychology. In this paper, both voice features and facial features are used for building an emotion recognition system. A Support Vector Machine classifiers are built by using raw data from video recordings. In this paper, the results obtained for the emotion recognition are given, and a discussion about the validity and the expressiveness of different emotions is presented. A comparison between the classifiers build from facial data only, voice data only and from the combination of both data is made here. The need for a better combination of the information from facial expression and voice data is argued.

Keywords: Emotion recognition, facial recognition, signal processing, machine learning.

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Authors: Ahmad Habibizad Navin, Yasaman Hashemi, Omid Mirmotahari

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In this paper, we introduce a novel platform encryption method, which modify its keys and random number generators step by step during encryption algorithms. According to complexity of the proposed algorithm, it was safer than any other method.

Keywords: Decryption, Encryption, Algorithm, security.

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Authors: Mpai Mokoena, Nsenda Lukumwena

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South Africa is one of the driest countries in the world and is facing a water crisis. In addition to inadequate infrastructure and poor planning, the country is experiencing high rates of water wastage due to pipe leaks. This study outlines the level of water wastage and develops a smart solution to efficiently manage and reduce the effects of pipe leaks, while monitoring the situation before and after fixing the pipe leaks. To understand the issue in depth, a literature review of journal papers and government reports was conducted. A questionnaire was designed and distributed to the general public. Additionally, the municipality office was contacted from a managerial perspective. The analysis from the study indicated that the majority of the citizens are aware of the water crisis and are willing to participate positively to decrease the level of water wasted. Furthermore, the response from the municipality acknowledged that more practical solutions are needed to reduce water wastage, and resources to attend to pipe leaks swiftly. Therefore, this paper proposes a specific solution for municipalities, local plumbers and citizens to minimize the effects of pipe leaks. The solution provides web and mobile application platforms to report and manage leaks swiftly. The solution is beneficial to the country in achieving water security and would promote a culture of responsibility toward water usage.

Keywords: Urban Distribution Networks, leak management, mobile application, responsible citizens, water crisis, water security.

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Authors: Bhed Bahadur Bista, Danda B. Rawat

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In this paper, we investigate multihop polling and data gathering schemes in layered sensor networks in order to extend the life time of the networks. A network consists of three layers. The lowest layer contains sensors. The middle layer contains so called super nodes with higher computational power, energy supply and longer transmission range than sensor nodes. The top layer contains a sink node. A node in each layer controls a number of nodes in lower layer by polling mechanism to gather data. We will present four types of data gathering schemes: intermediate nodes do not queue data packet, queue single packet, queue multiple packets and aggregate data, to see which data gathering scheme is more energy efficient for multihop polling in layered sensor networks.

Keywords: layered sensor network, polling, data gatheringschemes.

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Authors: S.Aranganayagi, K.Thangavel

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Clustering categorical data is more complicated than the numerical clustering because of its special properties. Scalability and memory constraint is the challenging problem in clustering large data set. This paper presents an incremental algorithm to cluster the categorical data. Frequencies of attribute values contribute much in clustering similar categorical objects. In this paper we propose new similarity measures based on the frequencies of attribute values and its cardinalities. The proposed measures and the algorithm are experimented with the data sets from UCI data repository. Results prove that the proposed method generates better clusters than the existing one.

Keywords: Clustering, Categorical, Incremental, Frequency, Domain

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Authors: Samar M. Alqhtani, Suhuai Luo, Brian Regan

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Data fusion technology can be the best way to extract useful information from multiple sources of data. It has been widely applied in various applications. This paper presents a data fusion approach in multimedia data for event detection in twitter by using Dempster-Shafer evidence theory. The methodology applies a mining algorithm to detect the event. There are two types of data in the fusion. The first is features extracted from text by using the bag-ofwords method which is calculated using the term frequency-inverse document frequency (TF-IDF). The second is the visual features extracted by applying scale-invariant feature transform (SIFT). The Dempster - Shafer theory of evidence is applied in order to fuse the information from these two sources. Our experiments have indicated that comparing to the approaches using individual data source, the proposed data fusion approach can increase the prediction accuracy for event detection. The experimental result showed that the proposed method achieved a high accuracy of 0.97, comparing with 0.93 with texts only, and 0.86 with images only.

Keywords: Data fusion, Dempster-Shafer theory, data mining, event detection.

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Authors: Chakkrit Snae

Abstract:

Names are important in many societies, even in technologically oriented ones which use e.g. ID systems to identify individual people. Names such as surnames are the most important as they are used in many processes, such as identifying of people and genealogical research. On the other hand variation of names can be a major problem for the identification and search for people, e.g. web search or security reasons. Name matching presumes a-priori that the recorded name written in one alphabet reflects the phonetic identity of two samples or some transcription error in copying a previously recorded name. We add to this the lode that the two names imply the same person. This paper describes name variations and some basic description of various name matching algorithms developed to overcome name variation and to find reasonable variants of names which can be used to further increasing mismatches for record linkage and name search. The implementation contains algorithms for computing a range of fuzzy matching based on different types of algorithms, e.g. composite and hybrid methods and allowing us to test and measure algorithms for accuracy. NYSIIS, LIG2 and Phonex have been shown to perform well and provided sufficient flexibility to be included in the linkage/matching process for optimising name searching.

Keywords: Data mining, name matching algorithm, nominaldata, searching system.

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