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Patel, Bankim
- Biological Data Integration Using Virtual Database
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National Journal of System and Information Technology, Vol 3, No 1 (2010), Pagination: 89-95Abstract
Biological data integration is considered to be one the most important and challenging tasks in bioinformatics. The scientific achievements greatly depend on the integrated view of largely diverse set of data. Biological data reside in hundreds of database and there is no single database providing an integrated view of data. It greatly invokes the need of data integration. Though they are different approaches for data integration like data warehouse, federation, webservices; each has its own pros and cons and challenges of implementation. In this research paper, we have proposed a framework using virtual database to integrate different biological data sources.Keywords
Data Integration, Data Warehouse, Data Federation, Webservice, Virtual Database
References
- Critchlow Terence and Lacroix Zoe (2004) Bioinformatics- Managing scientific data, Morgan Kaufmann Publishers, San Francisco. pp. 1-441
- M.Y. Galperin (2007), The Molecular Biology Database Collection: 2008 Update, Nucleic Acid Research.
- C.A. Goble and R. Stevens (2001), Transparent Access to Multiple Bioinformatics Information Sources, IBM Systems Journal 40, Vol 2. pp. 532-552
- W. Zhong and P.W. Sternberg (2007), Automated Data Integration for Developmental Biological Research, Development, Vol 133, pp. 3227-38
- L.D. Stein (2003) Integrating biological databases, Nat Rev Genet, Vol 4, pp. 337-45
- M.Y. Galperin (2007) The molecular biology database collection, Nucleic Acid Research
- J. Arrais, B. Santos, J. Ferandes, L.Carreto, M.A.S. Santos and J.L. Oliveira (2007) GeneBrowser : an approach for integration and functional classification of genomic data, Journal of Integrative Bioinformatics, Vol 4
- L. Wong (2002) Technologies for integrating biological data, Brief Bioinform, Vol 3, pp. 389-404
- Joel Arrais, Joao E. Pereira, Joao Fernandes and Jose Luis Oliveira (2009) GeNs: a Biological Data Integration Platform, World Academy of Science, Engineering and Technology
- Best Practices for Adaptation of Data Mining Techniques in Education Sector
Abstract Views :392 |
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National Journal of System and Information Technology, Vol 3, No 2 (2010), Pagination: 186-192Abstract
Best practices help to make the business processes smooth. As best practices are always recommended and not forced, the authors have recommended few best practices to be followed in Educational institutes so that the activities related to educational data mining becomes easy to implement. The best practices suggested are with the objective to gain and maintain data quality; as quality data leads to correct analysis.Keywords
Educational Data Mining, Best Practices, Data QualityReferences
- Anjewierden, A., Kolloffel B., and Hulshof, C. (2007). Towards educational data mining: Using data mining methods for automated chat analysis to understand and support inquiry learning processes, In Proceeding of International Workshop on Applying Data Mining in e-Learning (ADML’07), pp.23-32.
- Baker, R. and Carvalho, D. (2008). Labeling Student Behavior Faster and More Precisely with Text Replays, In Proceedings of the 1st International Conference on Educational Data Mining, pp.38-47.
- Ben-Zadoki, G., et. al. (2009). Examining online learning processes based on log files analysis: A case study, In Research, Reflections and Innovations in Integrating ICT in Education (Ed. A. Méndez-Vilas,et.al.), FORMATEX, pp.55-59.
- Chapman, P., et. al. CRISP-DM 1.0, Step-by-step data mining guide, SPSS, CRISPDM Consortium, http://www.crisp-dm.org/download.htm, accessed on Oct 2009.
- Eckerson, W., Data Quality and the Bottom Line, TDWI Report Series, 2002.
- Eckerson, W., Excerpt from TDWI’s Research Report - Data Quality and the Bottom Line, Business Intelligence Journal, Dec 2001,
- http://www.tdwi.org/research/display.aspx?ID=6589 accessed on Dec 2009. 7. Examination Reforms and Continuous and Comprehensive Evaluation (CCE) in CBSE, http://www.cbse.nic.in/cce/index.html, accessed on Dec 2009.
- Han, J. and Kamber M. (2001). Data Mining: Concepts and Techniques, San Francisco, Morgan Kaufmann.
- Jeong, H., and Biswas, G.(2008). Mining Student Behavior Models in Learning by- Teaching Environments, In Proceedings of the 1st International Conference on Educational Data Mining, pp.127-136.
- Lloyd, N., Heffernan, N. and Ruiz C. (2007). Predicting student engagement in intelligent tutoring systems using teacher expert knowledge, Supplementary Proceedings of the 13th International Conference of Artificial Intelligence in Education.pp.40-49.
- Mavrikis, M. (2008). Data-driven modelling of students' interactions in an ILE, In Proceedings of the 1st International Conference on Educational Data Mining, pp.87- 96.
- Sacin, C., Agapito J., et.al.(2009). Recommendation in Higher Education Using Data Mining Techniques, Proceedings of 2nd International Conference on Educational Data Mining, Spain
- Sheth, J., Patel B., and Bhatti, D. (2010). Improper Internet Usage: Controlling through Policy Model and Identifying through Data Mining, National Journal of Computer Science & Technology, Vol. 02(1), pp.16-21
- Srivastava, J., Cooleyz, R., et. al. (2000). Web Usage Mining: Discovery and Applications of Usage Patterns from Web Data, ACM SIGKDD, Vol. 01(2), pp.12-22. 15. Tang, Z., Maclennan, J. (2005). Data mining with SQL Server 2005, Wiley Publications.
- Challenges in Genetic Algorithm Based Intrusion Detection
Abstract Views :357 |
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National Journal of System and Information Technology, Vol 4, No 1 (2011), Pagination: 109-116Abstract
Intrusion detection is the technique of detecting malicious traffic on a network or a device. It is one of the critical network security components against emerging intrusions techniques and attacks. In this paper we present a survey of different intrusion detection approaches. Intrusion Detection Systems based on Genetic Algorithm are currently attracting researchers due to its inherent potential. Intrusion detection faces various challenges like reliably detect malicious activity and perform efficiently to cope with the large amount of network traffic. Here we have analyzed the present research challenges and issues in Genetic Algorithm based intrusion detection. Finally we carry out our experiments based on our sample Genetic Algorithm using KDD Cup 99 data set. The main contribution of the implementation is the understanding of challenges in Genetic Algorithm based intrusion detection.Keywords
Security, Challenges, Genetic Algorithm, Intrusion DetectionReferences
- Sumit A. Khandelwal, Shoba. A. Ade, Amol A. Bhosle and Radha S. Shirbhate, "A Simplified Approach to Identify Intrusion in Network with Anti Attacking Using .net Tool", International Journal of Computer and Electrical Engineering, Vol. 3, No. 3, June 2011
- Z. Muda, W. Yassin, M. N. Sulaiman, and N. I. Udzir, "A K-Means and Naive Bayes Learning Approach for Better Intrusion Detection", Information Technology Journal 10(3): 648-655, 2011
- 3Fatin Norsyafawati Mohd Sabri, Norita Md.Norwawi, and Kamaruzzaman Seman, "Identifying False Alarm Rates for Intrusion Detection System with Data Mining", IJCSNS International Journal of Computer Science and Network Security, VOL.11 No.4, April 2011
- Ghanshyam Prasad Dubey, Prof. Neetesh Gupta, Rakesh K Bhujade, "A Novel Approach to Intrusion Detection System using Rough Set Theory and Incremental SVM", International Journal of Soft Computing and Engineering (IJSCE) ISSN: 2231- 2307, Volume-1, Issue-1, March 2011
- Ritu Ranjani Singh, Neetesh Gupta, Shiv Kumar, "To Reduce the False Alarm in Intrusion Detection System using self Organizing Map", International Journal of Soft Computing and Engineering (IJSCE) ISSN: 2231-2307, Volume-1, Issue-2, May 2011
- P.Rajapandian, Dr.K.Alagarsamy, "Intrusion Detection in Dos Attacks", International Journal of Computer Applications (0975 – 8887) Volume 15– No.8, February 2011
- Shailendra Kumar Shrivastava, Preeti Jain, "Effective Anomaly based Intrusion Detection using Rough Set Theory and Support Vector Machine", International Journal of Computer Applications (0975 – 8887) Volume 18– No.3, March 2011
- S. Selvakani Kandeeban, R. S. Rajesh, "A Mutual Construction for IDS Using GA", International Journal of Advanced Science and Technology Vol. 29, April, 2011
- Kunjal Mankad, Priti Srinivas Sajja, and Rajendra Akerkar, "EVOLVING RULES USING GENETIC FUZZY APPROACH - AN EDUCATIONAL CASE STUDY", International Journal on Soft Computing ( IJSC ), Vol.2, No.1, February 2011
- VEGARD ENGEN, "MACHINE LEARNING FOR NETWORK BASED INTRUSION DETECTION", PhD thesis, Bournemouth University, June 2010
- R. Shanmugavadivu, Dr.N.Nagarajan, "NETWORK INTRUSION DETECTION SYSTEM USING FUZZY LOGIC", Indian Journal of Computer Science and Engineering (IJCSE)
- S.Sethuramalingam, Dr.E.R. Naganathan, "HYBRID FEATRUE SELECTION FOR NETWORK INTRUSION", International Journal on Computer Science and Engineering (IJCSE)
- Ahmed AHMIM, Nacira GHOUALMI, Noujoud KAHYA, "Improved Off-Line Intrusion Detection Using A Genetic Algorithm And RMI", (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 2, No.1, January 2011
- M. Sadiq Ali Khan, "Rule based Network Intrusion Detection using Genetic Algorithm", International Journal of Computer Applications (0975 – 8887) Volume 18– No.8, March 2011
- Ian Stewart, "A Modified Genetic Algorithm and Switch-Based Neural Network Model Applied to Misuse-Based Intrusion Detection", Master of Science Thesis, Queen's University, Kingston, Ontario, Canada, February 2009
- Zorana Bankovic,"A Genetic Algorithm-based Solution for Intrusion Detection", Journal of Information Assurance and Security, (2009) 192-199
- Ian Stewart, "A Modified Genetic Algorithm and Switch-Based Neural Network Model Applied to Misuse-Based Intrusion Detection", MS Thesis, Queen's University, Kingston, Ontario, Canada, February 2009
- S. SELVAKANI and R.S.RAJESH, "Escalate Intrusion Detection using GA - NN", Int. J. Open Problems Compt. Math., Vol. 2, No. 2, June 2009
- Zorana Bankovic, José M. Moya, Álvaro Araujo, Slobodan Bojanic and Octavio Nieto-Taladriz, "A Genetic Algorithm-based Solution for Intrusion Detection", Journal of Information Assurance and Security 4 (2009) 192-199
- H. Günes Kayacik, A. Nur Zincir-Heywood, Malcolm I. Heywood, "Selecting Features for Intrusion Detection: A Feature Relevance Analysis on KDD 99 Intrusion Detection Datasets", the NIMS Laboratory, http://www.cs.dal.ca/projectx, 2006
- Russell Meyer, “Challenges of Managing an Intrusion Detection System (IDS) in the Enterprise”, As part of Information Security Reading Room, SANS Institute, http://www.sans.org, June 2011
- Valerie Vogel, "Network and Host Security Implementation". Retrieved June 2011 from https://wiki.internet2.edu/confluence/display/secguide/Network+and+Host +Security+Implementation+(Stage+1)
- Context and Lexicon based Gender Identification of Noun Phrase for Gujarati Text using Hybrid Approach
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Authors
Affiliations
1 Computer Science at SRIMCA Bardoli Gujarat., IN
1 Computer Science at SRIMCA Bardoli Gujarat., IN