iosr-JCE   Volume-14 ~ Issue-5

Abstract: Many organizations are working hard to secure themselves from the growing threats of message hacking through various trends in cryptography. Yet the headlines are dominated with the latest news of message passing disaster more frequently than any time before. This document intends to review this problem and propose several possible solutions. The cryptographic industry has been responding to these threats with ever-quicker responses to the rapid onslaught of malicious techniques, while corporations establish strict cryptographic techniques.

Index Terms- Security Threats, Cryptosystems, Ciph Downline Load Security ertext,Encryption,Decryption, Interception, Interruption, Fabrication, Authentication, Password Hashing.

[1]. ANSI X9.19, Financial Institution Retail Message Authentication, American Bankers Association, August 13, 1986.
[2]. ANSI X9.52, Triple Data Encryption Algorithm Modes of Operation, American Bankers Association, 1998.
[3]. E. Barkan, E. Biham, N. Keller, Instant ciphertext-only cryptanalysis of GSM encrypted communication, Advances in Cryptology, Proceedings Crypto'03, LNCS 2729, D. Boneh, Ed., Springer, Heidelberg, 2003, pp. 600{616.
[4]. M. Bellare, New proofs for NMAC and HMAC: Security without collisionresistance, Advances in Cryptology, Proceedings Crypto'06, LNCS 4117, C. Dwork, Ed., Springer, Heidelberg, 2006, pp. 602{619.
[5]. M. Bellare, R. Canetti, H. Krawczyk, Keying hash functions for message authentication, Advances in Cryptology, Proceedings Crypto'96, LNCS 1109, N. Koblitz, Ed., Springer, Heidelberg, 1996, pp. 1{15.
[6]. M. Bellare, C. Namprempre, Authenticated encryption: Relations among notions and analysis of the generic composition paradigm, Advances in Cryptology, Proceedings Asiacrypt'00, LNCS 1976, T. Okamoto, Ed. (Springer, Heidelberg, 2000) 531{545.
[7]. M. Bellare, P. Rogaway, Random oracles are practical: A paradigm for designing efficient protocols, Proceedings ACM Conference on Computer and Communications Security (ACM Press 1993) 62{73.
[8]. M. Bellare, P. Rogaway, The exact security of digital signatures { How to sign with RSA and Rabin, Advances in Cryptology, Proceedings Eurocrypt'96, LNCS 1070, U. Maurer, Ed., Springer, Heidelberg, 1996, pp. 399{416.
[9]. D.J. Bernstein, The Poly1305-AES message-authentication code, Fast Software Encryption, LNCS 3557, H. Gilbert and H. Handschuh, Eds. (Springer, Heidelberg, 2005) 32{49.
[10]. D.J. Bernstein, Cache-timing attacks on AES, preprint, 2005, http://cr.yp.to/ papers.html#cachetiming

Abstract: Knowledge management is expected to be integral part any software development and services companies. Knowledge has become an important capital for many organizations in the international competition. So knowledge management is gradually becoming the core competence and key of sustainable development for organizations. Many enterprises have already used concepts and methods of knowledge management for operation and achieved remarkable results. Based on the analysis of knowledge management system, a framework model for enterprise knowledge management is presented in this paper. For an enterprise, it is necessary to build this knowledge management system to share knowledge resources, provide scientific supports for decision-making, face fiercely competitive market, and so on.

Keywords: Knowledge Management, Software Enterprise, Knowledge Creation, Generation, Acquisition, Application, Distribution, Identification

[1]. Research on Knowledge Management System in Enterprise - Hua Jiang ; Sch. of Economic & Manage., Hebei Univ. of Eng., Handan, China ; Cuiqing Liu ; Zhenxing Cui
[2]. Tacit knowledge - http://en.wikipedia.org/wiki/Tacit_knowledge
[3]. Explicit knowledge - http://en.wikipedia.org/wiki/Explicit_knowledge
[4]. Ref. Nonaka I., Takeuchi H., The Knowledge Creating Company, (1995), Oxford University Press
[5]. Knowledge in software life cycle - Havlice, Z. ; Dept. of Computer& Inf., Tech. Univ. of Kosice, Kosice ; Kunstar, J. ; Adamuscinova, I. ; Plocica, O.
[6]. Nonaka, I. and Takeuchi, H. 1995. The Knowledge-Creating Company. Oxford University Press.
[7]. Dybå, T., Kitchenham, B. A. and Jørgensen, M. 2005. Evidence-Based Software Engineering for Practitioners. IEEE Software 22(1): 58-65.
[8]. http://www.k-strategian.com/knowledge-based-value-creation/
[9]. http://en.wikipedia.org/wiki/Knowledge_organization_(management)
[10]. http://www.epistemics.co.uk/Notes/40-0-0.htm

Abstract: Historically solving crimes has been the privilege of the criminal justice and law enforcement specialists. With the increasing use of the computerized systems to track crimes, computer data analysts have started helping the law enforcement officers and detectives to speed up the process of solving crimes. According to, solving crimes is a complex task that requires human intelligence and experience. In this research we belief data mining is a technique that can assist law enforcement officers with crime detection problems, so the proposal tries to benefits years of human experience into computer models via data mining. Here we will take an interdisciplinary approach between computer science and criminal justice to develop a proposed data mining model. The proposed model is a three correlated dimensional model; each dimension is a datasets, first one present crime dataset second present criminal dataset and the third present geo-crime dataset. This model apply the Association Rules AR data mining algorithm on each of the three correlated dataset separately then using Genetic Algorithm GA as mixer of the resulted ARs to exploit the relational patterns among crime, criminal and geo-crime to help to detect universal crimes patterns and speed up the process of solving crime with more accurate. This research introduces suggestion to secure the results of the data mining association rules. For privacy preserving secure datasets we aim to hide the general secure and sensitive rules from appearing as a result of applying AR. This could be done by making the confidence of secure rules equal to zero by modifying the supports of critical and sensitive items in these rules. The proposal applied on real crime data from a dependable sheriff's office and validated our results.

Keywords: Crime Analysis, Data Mining, AR, GA, Crimnal.

[1]. Chen H. , Chung W. , Qin Y., Chau l. , Xu J.ennifer, Wang G., Zheng R., Atabakhsh H., "Crime Data Mining: An Overview and Case Studies", AI Lab, University of Arizona, proceedings National Conference on Digital Government Research, 2003, available at: http://ai.bpa.arizona.edu/
[2]. Fayyad U.M. and Uthurusamy R. ," Evolving data mining into solutions for insights". Communications of the ACM, 45(8), 28-31, 2002.
[3]. Chau M., Xu J., and Chen H., "Extracting meaningful entities from police narrative reports". In: Proceedings of the National Conference for Digital Government Research (dg.o 2002), Los Angeles, California, USA.

Abstract: Securing web is like securing our nation. Our whole world is Internet dependent In each sector internet is very much essential. So, internet security is very much promising task for us. More than 80% attacks are at application layer and almost 90% applications are vulnerable to these attacks. The essential services like banking, education, medicine and defense are internet based application needed high level security services which are essential for the socio-eco growth of the society. In this paper we are discussed the different types of web application attacks like DOS attack, Cross Site Scripting attack(XSS), SQL Injection Attack ,Request Encoding Attack. Survey of these attacks happening in last three to four years .latest happening with these attacks in India & out of India in the year 2012-13 & 13-14. Similarly we are measuring impact of each attack and putting its proposed counter measures.

Keywords: IDS - Intrusion detection system ,XSS – Cross site scripting, SQL-Sequential query language, DOS- Denial of Services

[1] Monika Sachdeva, Krishan Kumar Gurvinder Singh Kuldip Singh SBS College of Engg. & Technology, Guru Nanak Dev University Indian Institute of Technology Ferozepur, Punjab, India Amritsar, Punjab, India Roorkee, Uttarakhand, Indiamonika.sal(kediffmail.com gzsbawa7 1(yahoo.om kds56fec(&riitr.ernetmin) Performance Analysis of Web Service under DDoS Attacks 2009 IEEE International Advance Computing Conference (IACC 2009) Patiala, India, 6-7 March 2009
[2] Diallo Abdoulaye Kindy1,2 and Al-Sakib Khan Pathan2, A Detailed Survey on Various Aspects of SQL Injection in Web Applications: Vulnerabilities, Innovative Attacks, and Remedies, 1CustomWare, Kuala Lumpur, Malaysia 2Department of
Computer Science, International Islamic University Malaysia, Kuala Lumpur, Malaysia diallo14@gmail.com and sakib@iium.edu.my , 2012

[3] DDoS Attacks in the United Kingdom: 2012 Annual Trends and Impact Survey [4] Joaquin Garcia-Alfaro1 and Guillermo Navarro-Arribas2, Prevention of Cross-Site Scripting Attacks on Current Web Applications_,1 Universitat Oberta de Catalunya,Rambla Poble Nou 156, 08018 Barcelona - Spain, joaquin.garcia-alfaro@acm.org
2 Universitat Autònoma de Barcelona, Edifici Q, Campus de Bellaterra, 08193, Bellaterra - Spain, gnavarro@deic.uab.es
[5] William G.J. Halfond, Jeremy Viegas, and Alessandro Orso College of Computing Georgia Institute of Technology {whalfond|jeremyv|orso}@cc.gatech.edu, A Classification of SQL Injection Attacks and Countermeasures, College of ComputingGeorgia Institute of Technology {whalfond|jeremyv|orso}@cc.gatech.edu,

[6] Mark Curphey The Open Web Application Security Project David Endler iDefense William Hau Steve Taylor Predictive Solutions Tim Smith The Open Web Application Security Project Alex Russell OWASP Filters project Secure Pipe Inc. netWindows.org Gene McKenna Richard Parke Kevin McLaughlin," A Guide to Building Secure Web Applications The Open Web Application Security Project"

Abstract: Red Tacton was one of the advanced Pervasive technology that are genuinely user-friendly to everyone will require technologies that enablecommunication between people and objects in close proximity. Focusing on the naturalness, inevitability,and sense of security conveyed by touching in everyday life, this article describes human area networking technology that enables communication by touching, which we call RedTacton. Here, the human body acts as a transmission medium supporting IEEE 802.3 half-duplex communication at 10Mbit/s. The key component of the transceiver is an electric-field sensor implemented with an electrooptic crystal and laser light.RedTacton uses the minute electric field generated by human body as medium for transmitting the data.The chips which will be embedded in various devices contain transmitter and receiver built to send and accept data in digital format.In this paper we surveyed the red tactontechnology,working principle of red tacton over human area network,application,protocols for data transmission etc.

Keywords: Red Tecton, HAN.

[1] T.G.Zimmerman, "Personal Area Networks: Near-field intrabody communication," IBM systems journal, Vol. 35, Nos. 3&4, pp.609-617, 1996. [2] T.Nagatsuma and M.Shinagawa, "Photonic measurement technologies for high frequency electronics," NTT REVIEW, Vol.14, No.6.pp. 12-24, 2002. [3] M.Shinagawa, "Development of Electro-optic sensors for Intra-body Communication," NTT Technical Review, Vol. 2, No. 2, pp. 6-11, 2004. [4] M.Shinagawa, M. Fukumoto, K. Ochiai, and H. kyuragi, "A near-field-sensing transceiver for intra-body communication based on the electro-optic effect," IEEE Trans.IM, Vol.53,No.6, pp. 1533-1538,2004. [5] M. Mizoguchi, T.Okimura, and A.Matsuda, "Comprehensive commercialization Functions," NTT Technical Review, Vol.3, No. 5, pp. 12-16, 2005.
[6] Scribd (2010) "Human Area Networks- Red Tacton". Available: http://www.scribd.com/doc/55240946/RED-TACTON-REPORT. [7] Anil K. Jain, Patrick Flynn, and Arun Abraham Ross- Handbook of biometrics;FirstEdition;Springer, 2007. [8] Zheru Chi, Hong Yan, and TuânPh?m-Fuzzy algorithms: with applications to image processing and pattern recognition;FirstEdition;World Scientific,1996. [9] Wikipedia.org [10] Sang-KyunIm, Hyung-Man Park, Young-Woo Kim, Sang-Chan Han, Soo-Won Kim, Chul-Hee Kang and Chang-Kyung Chung, "Biometric Identification System by Extracting Hand Vein Patterns", Journal of the Korean Physical Society,Vol.38, No.3,March 2001.

Abstract: Nigeria, a country of more than 160 million people; also, the biggest oil exporter in Africa [1] Nigeria with her oil wealth, food security, and unemployment remains a serious problem. Shortage and increase in food prices has raised serious concerns regarding food and nutrition security across the country. Logical action is required to support vulnerable citizens (about 90%) cope with increases in food prices; assist farmers to quickly respond to the "opportunity" presented in an attempt to mitigate food security. To this end, this paper presents a proactive strategy for food security. An ontology-driven information retrieval system for agriculture cross languages search engine is proposed, a Nigeria Agricultural Ontology (NAO). Ontology, not only to structure and standardize agricultural terminology, but to provide information which would assist farmers to facilitate agricultural production, reduce dependence on food imports, revitalized agricultural sector, create employment andthereby attained food security for Nigeria.

Keywords: Agriculture, food security, thesaurus, ontology

[1] World Bank, (1986), Poverty and Hunger: Issue and Options for Food Security inDeveloping Countries Washington D.C.

[2] FAO (1998) "Urgent Action Needed to Combat Hunger as Number ofUndernourished in the World Increases" available on line @ www.fao.org.Retrieved on 15th December, 2012

[3] P. Papajorgji and F.Pinet (2012).New technologies for constructing complex agricultural and environment systems. Hershey, PA: Information Science Reference.

[4] C. Calero, F. Ruiz and M. Piattini (2006).Ontologies for software engineering and software technology. Berlin: Springer.

[5] L. Jain, R. Tedman and D. Tedman (2007).Evolution of teaching and learning paradigms in intelligent environment. Berlin New York: Springer.

[6] Sicilia. &M. Lytras (2009).Metadata and semantics. New York: Springer.

[7] T. Benson (2004), Assessing Africa's Food and Nutrition Security Situation2020Africa Conference Brief 1 IFPRI

[8] J. A. Akinwumi (1989), Cooperatives: The Answer to Nigeria's ProductionConsumer Dilemma, Faculty Lecture Series No. 2. Faculty of Agriculture,University of Ibadan

[9] AGROVOC (2013) Agriculture Thesaurus. Retrieved June 2013 from http://www.fao.org/aims/ag_intro.htm

[10] P. Papajorgji andP.Pardalos (2008).Advances in modeling agricultural systems. New York London: Springer.

Abstract: Efficient Privacy Preserving association rule mining has emerged as a latest research issue. In this thesis work, existing algorithms, Increase Support of Left Hand Side and Decrease Support of Right Hand Side are implemented successfully on the real data for Privacy Preserving Association Rule Mining. To provide privacy to sensitive data we also propose and implement a new algorithm .The performance of new algorithm is also compared with existing algorithms on the basis of number of rule pruned. The result show that proposed algorithm is more efficient as it performs privacy preserving mining by pruning more rules. Securing these against unauthorized access to the long-term goal of the database security research base community and the government statistical agencies. Whether data is personal or corporate data, data mining offers the potential to reveal what other regard as sensitive (private). In some cases, it may be of mutual benefit for two parties' even competitors to be share their data for analysis task. They would like to it will be ensure their own data remains private. In other good words, there is a need to protect sensitive knowledge during a data mining process. For Experimental work, we have used a realistic database of Doctor Patient Evaluation is taken from Medical College.

Keywords: Association rule, Apriori Algorithm, Clustering, k-Mean, Neural Network.

[1] N.V. Muthu & K. Sandhaya Rani, "Privacy Preserving association rule mining in vertically partitioned data", International journal of computer applications, Feb 2012.
[2] Fosca Giannotti, Laks V. S. Lakshmanan, Anna Monreale, Dino Pedreschi, and Hui (Wendy) Wang, "Privacy-Preserving Mining of Association Rules From Outsourced Transaction Databases", IEEE 2012.
[3] Yogendra kumar Jain, Vinod kumar yadav & Geetiks S. Pandey, "An Efficient Association Rule Hiding Algorithm for Privacy Preserving Data Mining", International Journal on Computer Science and Engineering (IJCSE), 7 July 2011.
[4] Krishnamoorthy Siva Kumar, " Spectral Filtering Technique Method", Proceedings of the Third IEEE International Conference on Data Mining, pages 40-48, 2003.
[5] Shyue-Liang Wang,Ayat Jafari, Department of Computer Science, New York Institute of Technology, New York, USA, "Hiding Sensitive predictive Association Rule", 2005.
[6] Kasthuri S and Meyyappan T, "Hiding Sensitive Association Rule Using Heuristic Approach", International Journal of Data Mining & Knowledge Management Process (IJDKP), Vol.3, No.1, January 2013.
[7] M.Mahendran, Dr.R.Sugumar, K.Anbazhagan, R.Natarajan, "An Efficient Algorithm for Privacy Preserving Data Mining Using Heuristic Approach", International Journal of Advanced Research in Computer and Communication Engineering Vol. 1, Issue 9, November 2012.
[8] Janakiramaiah Bonam, Dr.RamaMohan Reddy A, Kalyani G, " An Approach for Privacy Preserving in Association Rule Mining Using Data Restriction", International Journal of Engineering Science Invention, January 2013.
[9] Cornelia Gyõrödi, Robert Gyõrödi, Dr. Stefan Holban, "A Comparative Study of Association Rules Mining Algorithms", IJSCE, January 2013.
[10] Yogendra Kumar Jain, "An Efficient Association Rule Hiding Algorithm for Privacy Preserving Data Mining", IJSCE, July 2011.

Abstract: This paper contains information on Cloud computing and various services provided by it. Services provided are SaaS, PaaS&IaaS. We will mainly focus on SaaS in which customer is not needed to pay for the entire software; the customer could pay for only those services which they need.

Keywords: Cloud, IaaS, SaaS, Soft serve, PaaS.

[1] Keuth Jeffery. "The future of cloud computing". Papers:

[2] "Cloud Computing- A brief introduction" by LAD Enterprizes, Inc is a Management and Information Technology firm Paper

[3] Ojala, A. (2012)".Software Renting in the Era of Cloud Computing". In IEEE (Ed.), IEEE Fifth International Conference on Cloud Computing (pp.662-669). Yhdysvallat: IEEE. doi:10.1109/CLOUD.2012.71

[4] Ojala, A. (2013)."Software-as-a-Service Revenue Models." IT Professional 15(3), 54-59. doi:10.1109/MITP.2012.73[4] "SaaS Architecture" Progress software.

Websites: [5] http://www.infoworld.com

[6] http://www.searchcloudcomputing.techtarget.com

[7] Wikipedia (on SaaS and cloud computing)

Abstract: Real life sequential databases are usually not static. They tend to grow incrementally. So after every update a frequent pattern may no longer remains frequent while some infrequent patterns may appear as frequent in updated database. It is not a good idea to mine sequential database from scratch every time as the update occurs. It would be better if we can use the knowledge of already mined sequential patterns to find the complete set of sequential patterns for updated database. An incremental mining algorithm does the same thing. The main goal of an incremental mining algorithm is to reduce the time taken to find out the frequent patterns significantly i.e. it should mine the set of frequent patterns in significantly less time than a non-incremental mining algorithm. In this work we have approached using weight constraints, in time and space, of an idea of already existing algorithm called WSM.

Keywords: Frequent patterns, incremental mining, Sequential databases, sequential patterns, weight constrains

[1] Agrawal R., Srikant R., "Mining sequential patterns" , Proc. 11th IEEE Int. Conf. on Data Engineering (ICD E‟95), 1995.

[2] Srikant R., Agrawal R., "Mining sequential patterns: Generalizations and performance improvements", Proc. 5th IEEE Int. Conf. on Extending Database Technology (IDBT‟96).

[3] WeiCui, Haizhong An "Discovering Interesting Sequential Pattern in Large Sequence Database", 2009 Second Asia-Pacific Conference on Computational Intelligence and Industrial Applications.

[4] Cheng H., Yan X., Han J., "Inc Span: Incremental Mining of Sequential Patterns in Large Database", Proc. ACM KDD Conf. on Knowledge Discovery in Data, Washington (KDD‟04), 2004.

[5] F. Masseglia, P. Ponce let, and M. Teisseire, "Incremental mining of sequential patterns in large data bases", Data Knowledge Eng., 46:97–121, 2003.

Abstract: Providing a better performance is the key in IP network systems.An Adaptive Multipath Routing(AMR) system is introduced to handle the unpredicted traffic dynamics. The proposed system consists of Weight Computation component that sets the link weights and producing the maximum paths in the virtual routing multi topology. Based on the produced paths, an adaptive Traffic Splitting mechanism performing the traffic splitting across for every destination in the network within a short period. The system is using the Multiple Routing Configurations method to handle the node and link failures in the network ,allowing packet forwarding to carry on pre-configured alternative next-hops immediately after the recognition of the failure .The proposed mechanism giving the better quality of service and network performance.

Keywords: AMR,ATC,IGP,TE

[1]. N. Wang, K-H. Ho, and G. Pavlou, "Adaptive Multi- topology IGP Based Traffic Engineering with Near-Opti- mal Performance," Proc. IFIP Networking 2008.
[2]. S. Uhlig et al., "Providing Public Intradomain Traffic Matrices to the Research Community," ACM Sigcomm Comp. Commun. Rev. (CCR), vol. 36, no. 1, Jan. 2006, pp. 83–86.
[3]. B. Fortz and M. Thorup, "Optimizing OSPF/IS-IS Weights in a Changing World," IEEE JSAC, vol. 20, no. 4, May 2002, pp. 756–67.
[4]. D. Xu, M. Chiang, and J. Rexford, "Link-State Routing With Hop-By-Hop Forwarding Can Achieve OptimalTraffic Engineering," Proc. IEEE INFOCOM, Apr. 2008.
[5]. M. Caesar et al., "Dynamic Route Computation Considered Harmful," ACM Comp. Commun. Rev. (CCR), vol.40, no. 2, Apr. 2010, pp. 66–71.

Abstract: Digital image processing is an ever expanding and dynamic area with applications reaching out into our daily life such as digital signature, authentication, surveillance, medicine, space exploration, automated industry inspection and many others areas. Theseapplications are involved in different processes like image enhancement, object detection, features extraction, colour imaging etc. Implementation of such applications on a general purpose computer can be easier, but every time it is not efficient due to additional constraints on memory and other peripheral devices. Out of the five senses – sight, hearing, touch, smell and taste, humans use to perceive their environment. Among all,sight of images is the most powerful. More than 99% of the activity of the human brain is involved in processing images from the visual cortex. A visual image is rich in information. There is an efficient yet simple method to extract text regions from video sequences or static images. The speed of Haar discrete wavelet transform (DWT) operates the fastest among all wavelets because its coefficients are either 1 or -1. It is one of the reasons that Haar DWT is used to detect edges of candidate text regions. Image sub bands contain both text edges and non-text edges. The intensity of the text edges is also different from that of the non-text edges. Therefore, thresholdingis used to preliminary remove the non-text edges. Text regions of colour images are composed of horizontal edges, vertical edges and diagonal edges. Morphological dilation operators as AND, OR are applied to connect isolated text edges of each detail component sub-band in a transformed binary image. The simulation is carried out on MATLAB 2012 image processing tool.

Keywords: Discrete Wavelet Transform (DWT), HAAR Transform, Mathematical Morphology

[1] Xiaoqing Liu and JagathSamarabandu, An Edge-based text region extraction algorithm for Indoor mobile robot navigation, Proceedings of the IEEE, July 2005.

[2] Xiaoqing Liu and JagathSamarabandu, Multiscale edge-based Text extraction from Complex images, IEEE, 2006.

[3] JulindaGllavata, Ralph Ewerth and Bernd Freisleben, A Robust algorithm for Text detection in images, Proceedings of the 3rd international symposium on Image and Signal Processing and Analysis, 2003.

[4] Keechul Jung, KwangIn Kim and Anil K. Jain, Text information extraction in images and video: a survey, The journal of the Pattern Recognition society, 2004.

[5] Kongqiao Wang and Jari A. Kangas, Character location in scene images from digital camera, The journal of the Pattern Recognition society, March 2003.

[6] K.C. Kim, H.R. Byun, Y.J. Song, Y.W. Choi, S.Y. Chi, K.K. Kim and Y.K Chung, Scene Text Extraction in Natural Scene Images using Hierarchical Feature Combining and verification, Proceedings of the 17thInternational Conference on Pattern Recognition (ICPR ‟04), IEEE.

[7] Victor Wu, RaghavanManmatha, and Edward M. Riseman, TextFinder: An Automatic System to Detect and Recognize Text in Images, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 21, No. 11, November 1999.

[8] Xiaoqing Liu and JagathSamarabandu, A Simple and Fast Text Localization Algorithm for Indoor Mobile Robot Navigation, Proceedings of SPIE-IS&T Electronic Imaging, SPIE Vol. 5672, 2005.

[9] Qixiang Ye, Qingming Huang, Wen Gao and Debin Zhao, Fast and Robust text detection in images and video frames, Image and Vision Computing 23, 2005.

[10] Rainer Lienhart and Axel Wernicke, Localizing and Segmenting Text in Images and Videos, IEEE Transactions on Circuits and Systems for Video Technology, Vol.12, No.4, April 2002.

Abstract: This paper is proposed to investigate Web Services (WSs) performance on nomadic device like the Apple iPod touch. Because of the reduced capabilities of the mobile devices, clients residing in these terminals should be as simple as possible. So, we propose to move part of the business logic to an intermediate service provider. A WS can share a conceptual functionality between various WS, although they could be implemented in different locations and with different characteristics. A WS provider makes the mobile client independent of the implementation details as well as of their availability. A WS basically developed using two methods SOAP and REST. In this paper we measured the performance of these two Web Service's on the mobile device and analyzed which is efficient.

Keywords: Web Services, Service Oriented Architectures, RESTFul Services, Simple Object Access Protocol.

[1] F. Curbera, M. Duftler, R. Khalaf, W. Nagy, N. Mukhi and S. Weerawarana. "Unraveling the Web Services Web. An Introduction to SOAP, WSDL, and UDDI". IEEE Internet Computing, vol. 6, no. 2, pp. 86-93, 2002.

[2] "Web Services Description Language (WSDL) 1.1", Mar.2001. http://www.w3.org/TR/wsdl.

[3] "Universal Description, Discovery and Integration (UDDI) v3.0", Feb. 2005. http://www.uddi.org/

[4] "Simple Object Access Protocol (SOAP) Version 1.2", Jun, 2003. Available: http://www.w3.org/TR/soap/.

[5] K. Barry, "Web Services and Service-Oriented Architectures", Morgan Kauffman, 2003.

[6] Roy T. Fielding and Richard N. Taylor. Principled Design of the Modern Web Architecture. ACM Transactions on Internet Technology, 2(2):115{150, May 2002.

[7] T. Pilioura, S. Hadjiefthymiades, A. Tsalgatidou, M Spanoudakis, "Using Web Services for supporting the users of wireless devices". Department of Informatics andTelecommunications, National and Kalodistrian University of Athens, Panepistimopolis, TYPA building,Olisia, 157 84, Athens, Greece, June 2005.

[8] Thomas Erl, Service-Oriented Architecture: A Field Guide to Integrating XML and Web Services, Prentice Hall, April 2004.
[9] Market Share http://www.gartner.com/it/.

[10] DVB Project, "Digital Video Broadcasting (DVB); Transmission System for Handheld Terminals (DVB-H), ETSI EN 302 304 V1.1.1 (2004-11)". European Telecommunications Standards Institute, Nov. 2004. Available: http://www.dvb-h-online.org.

Abstract: Wireless Sensor Networks are those networks which include many sensors, sensors have many sensor nodes that are spread all over the world. A wireless sensor network (WSN) normally has many sensor nodes which are very modest, having minimum cost dispersed over the world having high-powered sink nodes which collect the readings of the sensor nodes. These nodes are comprised with high sensing power, processing and wireless communication abilities. A sensor network involves large number of sensors, collecting and communicating the sensor measurements of observing physical world scenarios. The main problem in WSN is outlier. Basically outlier is an element of a data set that falls in an abnormal range. For the capabilities of WSNs, it is clear that we need an accurate and robust technique which can be used for multivariate data too and can give the optimum results in output threshold, and that technique should help to increase the special characteristics of WSNs such as node mobility, network topology change and making distinction between errors and events. Among all the techniques KERNEL FUNCTIONS is the best technique because it can be used for multivariate data also. Except it FTDA and LSH are also produce optimum results.

Keywords: fault-tolerant data aggregation scheme, Kernel Density , LSH, Outlier Detection , Wireless Sensor Network

[1] Mohammad ilyas and Imad mahgoub , handbook of sensor networks: compact wireless and wired sensing systems , CRC Press

[2] S. Theodoridis, K. Koutroumbas, "pattern Recognition", 4th edition, Academic Press Reference for an article: [1] John, G. H. (1995), Robust linear discriminant trees, in "Fifth International Workshop on Artificial Intelligence and Statistics", Ft. Lauderdale, FL, pp. 285-291.

[2] Charu C. Aggarwal , Philip S, Yu, Outlier detection for high dimensional data, ACM SIGMOD Record Volume 30 Issue 2, June 2001 Pages37-46
[3] E. Elnahrawy and B. Nath. 2003. Cleaning and Querying Noisy Sensors. In Proc. of the 2nd ACM International conference on WSNA'03, pages 78-87.

[4] Victoria J. Hodge & Jim Austin 2004, a survey of techniques for outlier detection , Artificial Intelligence Review Volume 22, Issue 2 , pp 85-126

[5] Christoph Heinz and Bernhard Seeger. 2006. Statistical Modeling of Sensor Data and its application to Outlier Detection. Technical Report 2006/07, University of Stuttgart; 5. GI/ITG KuVS Fachgespräch "Drahtlose Sensornetze", Stuttgart.

[6] S. Subramaniam, T. Palpanas, D. Papadopoulos, V.Kalogeraki and D. Gunopulos. 2006. Online Outlier Detection in Sensor Data Using Non•Parametric Models. VLDB'06, September 12- 15, Seoul, Korea, pages 187-198.

[7] Yang Zhang, Nirvana Meratnia, Paul Havinga(2008) Outlier detection Techniques for wireless sensor networks: A survey, pp .11-20.

[8] Blohsfeld, B., Heinz, C.,2005, Seeger, B.: Maintaining Nonparametric Estimators over Data Streams. In: Proc. of BTW.

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[10 ]Suat Ozdemir : FTDA: outlier detection-based fault-tolerant data aggregation for wireless sensor networks , Application of Information and Communication Technologies (AICT), 2011 5th International Conference on Baku , ISBN 978-1-61284-831-0 , pp 1-5

Abstract: In biomedical field, the classification of disease using data mining is the critical task. The prediction accuracy plays a vital role in disease data set. More data mining classification algorithms like decision trees, neural networks, Bayesian classifiers are used to diagnosis the diseases. In decision tree Random Forest, Initially a forest is constructed from ten tress. The accuracy is measured and compared with desired accuracy. If the selected best split of trees matched the desired accuracy the construction terminates. Otherwise a new tree is added with random forest and accuracy is measured. The fitting criteria of random forest are accuracy and correlation. The accuracy is based on the mean absolute percentage error (MAPE) and the mean absolute relative error (MARE).In proposed system to refine the termination criteria of Random Forest, Binomial distribution, multinomial distribution and sequential probability ratio test (SPRT) are used. The proposed method stops the random forest earlier compared with existing Random Forest algorithm. The supervised learning model like support vector machine takes a set of inputs and analyze the inputs and recognize the desired patterns. The disease data sets are supplied to SVM and prediction accuracy is measured. The comparison is made between Random Forest and SVM and best class labels are identified based on disease.

Keywords: SVM, Random Forest, Disease classification.

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[2] Evanthia E. Tripoliti et al, "Automated Diagnosis of Diseases Based on Classification: Dynamic Determination of the Number of Trees in Random Forests Algorithm" IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE, VOL. 16, NO. 4, JULY 2012.

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Abstract: These days privacy preservation topic is based on one of the heated topics of data mining today. With the development of data mining technology, an increasing number of data can be mined out to reveal some potential information about user. While this will lead to a severe problem, which is users' privacy may be violated easily. The goal of privacy preserving is to mine the potential valuable knowledge without leakage of sensitive records, in other words, use non-sensitive data to infer sensitive data. There are many research and branches in this area. Most of them analyze and optimize the technologies and algorithms of privacy preserving data mining. Privacy Preserving Data Mining (PPDM) is used to extract relevant knowledge from large amount of data and at the same time protect the sensitive information from the data miners. The problem in privacy-sensitive domain is solved by the development of the Multi-Level (Multi-Party) Trust Privacy Preserving Data Mining (MLT-PPDM) where multiple differently perturbed copies of the same data is available to data miners at different trusted levels. In MLT-PPDM data owners generate perturbed data by various techniques like Parallel generation, Sequential generation and On-demand generation. MLT-PPDM is robust against the diversity attacks.

Index Terms: Data mining, Data Perturbation, Multiparty Privacy Preserving.

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Abstract: Text mining is nothing but the discovery of interesting knowledge in text documents. But there is a big challenging issue that how to guarantee the quality of discovered relevant features. And that are in the text documents for describing user preferences because of the large number of terms, patterns and noise. For text mining there are basically two types of approaches; one is term based approach and another is phrase based approach. But term based approach suffered with the problem of polysemy and synonymy. And phrase based approach suffered with low frequency occurrence. But phrase based approaches are better than the term based approaches. But pattern based approach is better than the term based and phrase based approach. The proposed method utilize pattern approach with the set of keywords, which is an innovative and effective pattern discovery technique by which research articles, news articles classification of different field are done and more than 80 percent of documents are successfully identified.

Index Terms: Computer Networks, Network Security, Anomaly Detection, Intrusion Detection.

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Abstract: Ad hoc networks are a new wireless networking paradigm for mobile hosts. Unlike traditional mobile wireless networks, ad hoc networks do not rely on any fixed infrastructure, Instead, hosts rely on each other to keep the network connected. One main challenge in the design of these networks is their vulnerability to security attacks. In this Survey, we study the threats an ad hoc network faces and the security goals to be achieved. We identify the new challenges and opportunities posed by this new networking environment and explore new approaches to secure its communication. Routing protocols, which act as the binding force in these networks, are a common target of these nodes. Ad-hoc On Demand Distance Vector (AODV) is one of the widely used routing protocols that is currently undergoing extensive research and development. AODV is based on distance vector routing, but the updates are shared not on a periodic basis but on an as per requirement basis. The control packets contain a hop count and sequence number field that identifies the freshness of routing updates. As these fields are mutable, it creates a potential vulnerability that is frequently exploited by malicious nodes to advertise better routes. Similarly, transmission of routing updates in clear text also discloses vital information about the network topology, which is again a potential security hazard. This research addresses the problem of securing Mobile Ad Hoc Networks routing protocols. In this survey we examine different routing protocols and various types of routing security attacks. We also perform a survey in search for different routing security schemes that have been proposed to prevent and/or detect these attacks, and point out their advantages and drawbacks.

Keywords: Ad-hoc Network, (AODV) Ad-hoc On Demand Distance Vector, ARN (Authenticated Routing for Ad-hoc Networks).

[1] Monis Akhlaq, M Noman Jafri, Muzammil A Khan, Baber Aslam, Addressing Security Concerns of Data Exchange in AODV Transactions on Engineering, Computing and Technology, Volume 16 November 2006 ISSN 1305-5313, pp. 29-33.
[2] Davide Cerri and Alessandro Ghioni, "Securing AODV: The A-SAODV Secure Routing Prototype "IEEE Communications Magazine • February 2008.
[3] Durgesh Wadbude, Vineet Richariya, An Efficient Secure AODV Routing Protocol in MANET., International Journal of Engineering and Innovative Technology (IJEIT)Volume 1, Issue 4, April 2012 274
[4] Songbai Lu1, Longxuan Li and Kwok-Yan Lam, Lingyan Jia, "SAODV: A MANET Routing Protocol that can Withstand Black Hole Attack", IEEE 2009 International Conference on Computational Intelligence and Security, pp 421-425.
[5] Seung Yi, Prasad Naldrug, Robin Kravets, A Security Aware Routing Protocol for Wireless Ad hoc Networks, In the proceedings of 3rd ACM international of mobile ad hoc networking and computingpp 226-236, 2002.
[6] Yih-chunhu, Adrianperrig, "A Survey of Secure Wireless Ad Hoc Routing", PUBLISHED BY THE IEEE COMPUTER SOCIETY, 1540-7993/04.
[7] Asad Amir Pirzada and Chris McDonald "Secure Routing with the AODV Protocol" 2005 Asia-Pacific Conference on Communications, Perth, Western Australia, 3 - 5 October 2005
[8] Kimaya Sanzgiri, Bridget Dahilly, Brian Neil, Elizabeth m. Belding-royer "A Secure Routing Protocol For Adhoc Networks" Proceedings of the 10th IEEEInternational Conference on Network Protocols(ICNP‟02).
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[10] A. A. Pirzada and C. McDonald, "Kerberos assisted authenticationin mobile ad-hoc networks," Proceedings of the 27th Australasian Computer Science Conference (ACSC),vol.26,no.1,pp.41–46,2004.

Abstract: There are billions of web pages available on the World Wide Web (WWW). So there are lots of search results corresponding to a user's query out of which only some are relevant. The relevancy of a web page is calculated by search engines using page ranking algorithms. Most of the page ranking algorithm use web structure mining and web content mining to calculate the relevancy of a web page. In this paper, the standard Weighted PageRank algorithm is being modified by incorporating Visits of Links(VOL).The proposed method takes into account the importance of both the number of visits of inlinks and outlinks of the pages and distributes rank scores based on the popularity of the pages. So, the resultant pages are displayed on the basis of user browsing behavior.

Keywords: inlinks, outlinks, search engine, web mining, World Wide Web (WWW).

[1] Sergey Brin and Lawrence Page, "The Anatomy of a Large-Scale Hypertextual Web Search Engine", Computer Science Department, Stanford University, Stanford, CA 94305.
[2] Wenpu Xing and Ali Ghorbani, "Weighted PageRank Algorithm", Faculty of Computer Science, University of New Brunswick,Fredericton, NB, E3B 5A3, Canada.
[3] Gyanendra Kumar, Neelam Duhan, A. K. Sharma, "Page Ranking Based on Number of Visits of Links of Web Page", Department of Computer Engineering, YMCA University of Science & Technology, Faridabad, India.
[4] Naresh Barsagade, "Web Usage Mining And Pattern Discovery: A Survey Paper", CSE 8331, Dec.8, 2003.
[5] Dell Zhang, Yisheng Dong, "A novel Web usage mining approach for search engines", Computer Networks 39 (2002) 303–310
[6] Neelam Duhan, A. K. Sharma, Komal Kumar Bhatia, "Page Ranking Algorithms: A Survey" Advance Computing Conference, 2009. IACC 2009 IEEE International.
[7] R.Cooley, B.Mobasher and J.Srivastava, "Web Mining: Information and Pattern Discovery on the World Wide Web". In Proceedings of the 9th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'97), 1997.
[8] Companion slides for the text by Dr. M. H. Dunham, "Data Mining: Introductory and Advanced Topics", Prentice Hall, 2002.
[9] Neelam tyagi, Simple Sharma, "Weighted Page Rank Algorithm Based on Number of Visits of Links of Web Page", International Journal of Soft Computing and Engineering (IJSCE) ISSN: 2231-2307, Volume-2, Issue-3, July 2012