Volume-1 ~ Issue-5
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Paper Type | : | Research Paper |
Title | : | Color Based Image Retrieval System |
Country | : | India |
Authors | : | Pawandeep Kaur || Sakshi Thakral || Mandeep Singh |
: | 10.9790/0661-0150105 | |
ABSTRACT : Advances in the data storage and image acquisition technologies have enabled the creation of large datasets. It is necessary to develop appropriate information systems to efficiently manage these collections. The most common approaches use Color-Based Image Retrieval (CBIR) system. The goal of CBIR system is to support image retrieval based on color. In a color based image retrieval system querying can be done by a query image. The goal is to find the images most resembling the query. In this work we mainly focused on color histogram-based method.
Keywords - Color based image retrieval system (CBIR), Color histogram, Query image
Keywords - Color based image retrieval system (CBIR), Color histogram, Query image
Journal Papers:
[1] P.S.Suhasini ,Dr. K.Sri Rama Krishna, Dr. I. V. Murli Krishna, CBIR using color hitogram processing, Journal of Theoretical and Applied Information Technology 6(1), 116 - 122
[2] Neetu Sharma, Paresh Rawat and jaikaran Singh, Efficient CBIR Using Color Histogram Processing, Signal & Image Processing , An International Journal(SIPIJ) ,2(1), 2011
Theses:
[3] Ole Andreas Flaaten Jonsgård, Improvements on colour histogram based CBIR, Department of Computer Science and Media Technology Gjøvik University College, 2005
[4] Shengjiu Wang, A Robust CBIR Approach Using Local Color Histograms,University of Alberta, October 2001.
[1] P.S.Suhasini ,Dr. K.Sri Rama Krishna, Dr. I. V. Murli Krishna, CBIR using color hitogram processing, Journal of Theoretical and Applied Information Technology 6(1), 116 - 122
[2] Neetu Sharma, Paresh Rawat and jaikaran Singh, Efficient CBIR Using Color Histogram Processing, Signal & Image Processing , An International Journal(SIPIJ) ,2(1), 2011
Theses:
[3] Ole Andreas Flaaten Jonsgård, Improvements on colour histogram based CBIR, Department of Computer Science and Media Technology Gjøvik University College, 2005
[4] Shengjiu Wang, A Robust CBIR Approach Using Local Color Histograms,University of Alberta, October 2001.
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Paper Type | : | Research Paper |
Title | : | Cryptography Using Genetic Algorithms (GAs) |
Country | : | India |
Authors | : | Sonia Goyat |
: | 10.9790/0661-0150608 | |
ABSTRACT: Cryptography is essential for protecting information as the importance of security is increasing day by day with the advent of online transaction processing and e commerce. Public key cryptography is one of the most important types of cryptography. In public key cryptography the key has to be unique. There are two ways of key production, the first one is mathematical like AES, DES and the other one is based on the theory of natural selection. The work explores the different techniques of cryptography in order to prove that the natural selection based techniques are as good as the rigorous mathematical techniques. 12 papers and theses have been studied in order to reach the conclusion.
Keywords: Cryptography, Genetic Algorithms, Natural Selection, Public Key Cryptography, Vernam Ciphers
Keywords: Cryptography, Genetic Algorithms, Natural Selection, Public Key Cryptography, Vernam Ciphers
[1] ABDELSALAM ALMARIMI et al, A NEW APPROACH FOR DATA ENCRYPTION USING GENETIC ALGORITHMS, Published in: · Proceeding CERMA '10 Proceedings of the 2010 IEEE Electronics, Robotics and Automotive Mechanics Conference
[2] Harsh Bhasin, Nakul Arora, Reliability Infocom Technology and Optimization 2010, Conference Proceedings pages 226- 230.
[3] Bethany Delman, Genetic Algorithms in Cryptography, MS Thesis 2004.
[4] Cellular automata computations and secret key cryptography Franciszek Seredynski, Pascal Bouvry, Albert Y. Zomaya Parallel Computing May 2004, Elsiver
[5] Corpuscular Random Number Generator. Harsh Bhasin, IJIEE 2012, Vol.2 (2): 197-199 ISSN: 2010-3719.
[6] Modified Genetic Algorithms Based Solution to. Subset Sum Problem. Harsh Bhasin computergrad.com. Faridabad, India. Neha Singla, IJARAI Vol1 (1).
[7] Menezes, A., van Oorschot, P., & Vanstone, S. (1997). Handbook of Applied Cryptography Boca Raton: CRC Press
[8] Norman D. Jorstad, CRYPTOGRAPHIC ALGORITHM METRICS, January 1997
[9] H. Bhasin and S. Bhatia, "Application of Genetic Algorithms in Machine learning", IJCSIT, Vol. 2 (5), 2011.
[10] Pisinger D (1999). "Linear Time Algorithms for Knapsack Problems with Bounded Weights". Journal of Algorithms, Volume 33, Number 1, October 1999, pp. 1–14
[11] Harsh Bhasin, "Use of Genetic Algorithms for Finding Roots of. Algebraic Equations", IJCSIT, Vol. 2, Issue 4.
[12] S. Thrun, "Learning to Play the Game of Chess", In G. Tesauro, D. Touretzky, and T. Leen, editors, Advances in Neural Information Processing Systems(NIPS) 7, Cambridge, MA, 1995. MIT Press
[2] Harsh Bhasin, Nakul Arora, Reliability Infocom Technology and Optimization 2010, Conference Proceedings pages 226- 230.
[3] Bethany Delman, Genetic Algorithms in Cryptography, MS Thesis 2004.
[4] Cellular automata computations and secret key cryptography Franciszek Seredynski, Pascal Bouvry, Albert Y. Zomaya Parallel Computing May 2004, Elsiver
[5] Corpuscular Random Number Generator. Harsh Bhasin, IJIEE 2012, Vol.2 (2): 197-199 ISSN: 2010-3719.
[6] Modified Genetic Algorithms Based Solution to. Subset Sum Problem. Harsh Bhasin computergrad.com. Faridabad, India. Neha Singla, IJARAI Vol1 (1).
[7] Menezes, A., van Oorschot, P., & Vanstone, S. (1997). Handbook of Applied Cryptography Boca Raton: CRC Press
[8] Norman D. Jorstad, CRYPTOGRAPHIC ALGORITHM METRICS, January 1997
[9] H. Bhasin and S. Bhatia, "Application of Genetic Algorithms in Machine learning", IJCSIT, Vol. 2 (5), 2011.
[10] Pisinger D (1999). "Linear Time Algorithms for Knapsack Problems with Bounded Weights". Journal of Algorithms, Volume 33, Number 1, October 1999, pp. 1–14
[11] Harsh Bhasin, "Use of Genetic Algorithms for Finding Roots of. Algebraic Equations", IJCSIT, Vol. 2, Issue 4.
[12] S. Thrun, "Learning to Play the Game of Chess", In G. Tesauro, D. Touretzky, and T. Leen, editors, Advances in Neural Information Processing Systems(NIPS) 7, Cambridge, MA, 1995. MIT Press
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ABSTRACT: In the field of RS and GIS, automated information extracted from topographic sheet or reference map plays a pivotal role in assisting researchers in performing various inferential analyses that aids in making qualitative as well as quantitative assessment of the features. Such automated procedures tremendously reduces time and effort requirement compared to that of traditional manual techniques. This work aims at associating stream order for vector hydrograph using spiral traversal technique that reduces time complexity from O(n2) to o(n). Keywords: Digitization, Horton's stream order, Spiral Traversal, Stream, Vector hydrograph.
[1]. Hortan, R. E., Erosional Development of Streams and their drainage basins: Hydrophysical Approach to quantitative Morphology, The Geological society of America Bulletin, Vol. 56, No. 03, pp. 273-370, 1945.
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[3]. Amin, T. and Kasturi, R., Map Data Processing: Recognition of Lines and symbols, Optical engineering, vol. 26, No. 4, pp. 354-358, 1987.
[4]. Cho, H. et al., Extraction of streams channels in High- Resolution Terrain Images using Morphology, in proc. of International conference on Geoscience and Remote Sensing symposium, pp. 1078-1081,2006.
[5]. Alexander Gleyzer, Michael Denisyuk, Alon Rimmer, Yigal Salingar, A Fast Recursive GIS Algorithm for Computing Strahler Stream Order in Braided and Nonbraided Networks, Volume 40, Issue 4, pp. 937–946, JAWRA Journal of the American Water Resources Association, 2004.
[6]. Ratika Pradhan et al., Automatic Extraction of Drainage Pattern from Topographical Sheets and Initialization of Stream Orders, International Journal of Computer Applications in Engineering, Technology and Sciences, Vol. 3, Issue 1, pp. 72-77, 2010.
[7]. Suzanne M. Pierson, Barbara J. Rosenbaum, Lucinda D. McKay, and Thomas G. Dewald, Technical paper on Strahler Stream Order and Strahler Calculator Values in NHDPlus, 2008. [available online at ftp://ftp.horizon- systems.com/NHDPlusExtensions/SOSC/SOSC_technical_paper.pdf]
[2]. Strahler A.N., Quantitative Analysis of Watershed Geomorphology, American Geophysical Union Transactions Volume 38, pp. 913-920, 1957.
[3]. Amin, T. and Kasturi, R., Map Data Processing: Recognition of Lines and symbols, Optical engineering, vol. 26, No. 4, pp. 354-358, 1987.
[4]. Cho, H. et al., Extraction of streams channels in High- Resolution Terrain Images using Morphology, in proc. of International conference on Geoscience and Remote Sensing symposium, pp. 1078-1081,2006.
[5]. Alexander Gleyzer, Michael Denisyuk, Alon Rimmer, Yigal Salingar, A Fast Recursive GIS Algorithm for Computing Strahler Stream Order in Braided and Nonbraided Networks, Volume 40, Issue 4, pp. 937–946, JAWRA Journal of the American Water Resources Association, 2004.
[6]. Ratika Pradhan et al., Automatic Extraction of Drainage Pattern from Topographical Sheets and Initialization of Stream Orders, International Journal of Computer Applications in Engineering, Technology and Sciences, Vol. 3, Issue 1, pp. 72-77, 2010.
[7]. Suzanne M. Pierson, Barbara J. Rosenbaum, Lucinda D. McKay, and Thomas G. Dewald, Technical paper on Strahler Stream Order and Strahler Calculator Values in NHDPlus, 2008. [available online at ftp://ftp.horizon- systems.com/NHDPlusExtensions/SOSC/SOSC_technical_paper.pdf]
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Paper Type | : | Research Paper |
Title | : | Detection of SQL Injection Attack in Web Applications using Web Services |
Country | : | India |
Authors | : | V.Shanmughaneethi || S.Swamynathan |
: | 10.9790/0661-0151320 | |
Abstract : Among the various types of software vulnerabilities, command injection is the most common type of threat in web applications. In command injection, SQL injection type of attacks are extremely prevalent, and ranked as the second most common form of attack on web. SQL injection attacks involve the construction of application's input data that will result in the execution of malicious SQL statements. Most of the SQL injection detection techniques involve the code to be written along with the actual scripting code. These techniques do not detect errors in SQL statements. Hence, this paper proposes a mechanism to identify invalid SQL statements, to analyze the query for invalid non SQL key words, and to customize the captured errors. This mechanism is different from others by means of separation of the main scripting code and SQL injection code.
Key Words: Web security, SQL injection, Web Service, Tautology, Query Engine, XML Schema, Piggybacking.
Key Words: Web security, SQL injection, Web Service, Tautology, Query Engine, XML Schema, Piggybacking.
[1]. "Internet Crime Complaint Centre" http://www.ic3.gov
[2]. IBM Internet Security Systems X-Force® 2008 Trend Statistics, www.ibm.com/ services/us/ iss/xforce/trendreports
[3]. Jung-Ying Lai, Jain-Shing Wu, Shih-Jen Chen, Chia-Huan Wu and Chung-Huang Yang, "Designing a Taxonomy of Web Attacks", International Conference on Convergence and Hybrid Information Technology 2008.
[4]. OWASP. "OWASP Top 10 2007." http://www.owasp.org/index.php/Top_10_2007, April 2008.
[5]. CVE. "Common Vulnerabilities and Exposures" http://cve.mitre.org/, April 2008.
[6]. J.V. William G.J.Halfond and A.Orso, "A Classification of SQL injection attacks and countermeasures" ISSSE 2006 – March 14th, 2006.
[7]. C. Anley, "Advanced SQL Injection in SQL server Application", Technical Report, NGSSoftware Insight Security Research (NISR) 2002.
[8]. Anyi Liu, Yi Yuan, Duminda Wijesekera, " SQLProb: A Proxy-based Architecture towards Preventing SQL Injection Attacks", Honolulu, Hawaii, U.S.A. SAC‟09 March 8-12, 2009
[9]. R. Ezumalai, G. Aghila"Combinatorial Approach for Preventing SQL Injection Attacks" , 2009 IEEE International Advance Computing Conference (IACC 2009) Patiala, India, 6-7 March 2009
[10]. Jaroslaw Skaruz, Franciszek Seredynski "Detecting Web Application Attacks With Use of Gene Expression Programming" IEEE Congress on Evolutionary Computation , 2009
[2]. IBM Internet Security Systems X-Force® 2008 Trend Statistics, www.ibm.com/ services/us/ iss/xforce/trendreports
[3]. Jung-Ying Lai, Jain-Shing Wu, Shih-Jen Chen, Chia-Huan Wu and Chung-Huang Yang, "Designing a Taxonomy of Web Attacks", International Conference on Convergence and Hybrid Information Technology 2008.
[4]. OWASP. "OWASP Top 10 2007." http://www.owasp.org/index.php/Top_10_2007, April 2008.
[5]. CVE. "Common Vulnerabilities and Exposures" http://cve.mitre.org/, April 2008.
[6]. J.V. William G.J.Halfond and A.Orso, "A Classification of SQL injection attacks and countermeasures" ISSSE 2006 – March 14th, 2006.
[7]. C. Anley, "Advanced SQL Injection in SQL server Application", Technical Report, NGSSoftware Insight Security Research (NISR) 2002.
[8]. Anyi Liu, Yi Yuan, Duminda Wijesekera, " SQLProb: A Proxy-based Architecture towards Preventing SQL Injection Attacks", Honolulu, Hawaii, U.S.A. SAC‟09 March 8-12, 2009
[9]. R. Ezumalai, G. Aghila"Combinatorial Approach for Preventing SQL Injection Attacks" , 2009 IEEE International Advance Computing Conference (IACC 2009) Patiala, India, 6-7 March 2009
[10]. Jaroslaw Skaruz, Franciszek Seredynski "Detecting Web Application Attacks With Use of Gene Expression Programming" IEEE Congress on Evolutionary Computation , 2009
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Paper Type | : | Research Paper |
Title | : | Impact of Personal Software Process on Software Quality |
Country | : | India |
Authors | : | Abdul Kadir Khan |
: | 10.9790/0661-0152125 | |
Abstract : Today, concern for quality has become an international movement. Even though most industrial organizations have now adopted modern quality principles, the software community has continued to rely on testing as the principal quality management method. Different decades have different trends in software engineering. The Personal Software Process (PSP) is an evolutionary series of personal software engineering techniques that an engineer learns and practices. A software process is nothing without the individual programmer. PSP a data-driven process customized to teaching individuals about their programming styles, helping software engineers further develop their skills in developing quality software. In this paper I explain the Personal Software Process definition, principles, design, advantages and opportunities apart from discussing about PSP as a framework of techniques to help engineers and their organizations improve their performance while simultaneously increasing product quality focusing on the incorporation of PSP concepts in software development practice.
Key words: Personal Software Process, Software Development, Engineers, PSP Concepts.
Key words: Personal Software Process, Software Development, Engineers, PSP Concepts.
[1]. Boehm, B. Software Engineering Economics. Englewood Cliffs, NJ: Prentice-Hall, 1981.
[2]. Fagan, M. "Design and Code Inspections to Reduce Errors in Program Development." IBM Systems Journal, 15, 3 (1976).
[3]. Fagan, M. "Advances in Software Inspections." IEEE Transactions on Software Engineering, SE-12, 7, (July 1986).
[4]. Humphrey, W. Managing the Software Process. Reading, MA: Addison-Wesley, 1989.
[5]. Paulk, M.; Curtis, B.; & Chrissis, M. B. Capability Maturity Model for Software, Version 1.1 Pittsburgh, Pa.:Software Engineering Institute, Carnegie Mellon University, 1995.
[6]. ]Humphrey, W. PSP: A Self-Improvement Process for Software Engineers. Addison-Wesley, Upper Saddle River, NJ, 2005.
[7]. Pressman, R. Software Engineering: A Practitioner's Approach. NewYork: McGraw-Hill, 1992.
[8]. Humphrey,W."The Software Quality Index," Software Quality Professional,1,1 Dec 98
[9]. Humphrey,W. Winning with Software:An Executive Strategy.Addison-Wesley, Boston, 2002.
[2]. Fagan, M. "Design and Code Inspections to Reduce Errors in Program Development." IBM Systems Journal, 15, 3 (1976).
[3]. Fagan, M. "Advances in Software Inspections." IEEE Transactions on Software Engineering, SE-12, 7, (July 1986).
[4]. Humphrey, W. Managing the Software Process. Reading, MA: Addison-Wesley, 1989.
[5]. Paulk, M.; Curtis, B.; & Chrissis, M. B. Capability Maturity Model for Software, Version 1.1 Pittsburgh, Pa.:Software Engineering Institute, Carnegie Mellon University, 1995.
[6]. ]Humphrey, W. PSP: A Self-Improvement Process for Software Engineers. Addison-Wesley, Upper Saddle River, NJ, 2005.
[7]. Pressman, R. Software Engineering: A Practitioner's Approach. NewYork: McGraw-Hill, 1992.
[8]. Humphrey,W."The Software Quality Index," Software Quality Professional,1,1 Dec 98
[9]. Humphrey,W. Winning with Software:An Executive Strategy.Addison-Wesley, Boston, 2002.
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Abstract- In Ad-Hoc networks the mobile nodes communicate with each other using multi-hop wireless links. The main drawback of such network is that there are no stationary infrastructures to route the packets. Hence, routing protocols have to adapt quickly and elegantly to frequent and unpredictable changes in network technology and they have to do so while conserving the memory, power and bandwidth resource. The Ant Colony Optimization technique implemented upon such networks have helped the nodes in finding the routes to different nodes in an optimized way same as ants find the optimum route to its food. The techniques provided so far have considered the search space utilized by a node as the space occupied by all the nodes that are present in the network which requires message passing among all the nodes that are present in the network consuming plenty of bandwidth and power only to find the routes to different nodes. If we divide the search space among the nodes forming clusters then the number of messages communicating will be reduced thereby reducing the bandwidth occupied and power consumed conserving the important resources of mobile communication in wireless medium.
Key words: Mobile Ad-hoc Networks, Optimal Route Searching ,Ant Colony Optimization.
Key words: Mobile Ad-hoc Networks, Optimal Route Searching ,Ant Colony Optimization.
[1] Jan Komorowski, Zdzislaw Pawlak, L. P. A. S. Rough sets: a tutorial, 1998.
[2]. Pawlak, Z. Rough sets: Theoretical aspects of reasoning about data. Kluwer Dordrecht,1991.[
[3]. Adrzej Skowron, J. S. Tolerance approximation spaces. Fundamental Informaticae 27, 2-3 (1996), 245 -253.
[4] Saori Kawasaki, Ngoc Binh Nguyen, T. B. H. Hierarchical document clustering based on tolerance rough set model. In Principles of Data Mining and Knowledge Discovery, 4th European Conference, PKDD 2000, Lyon, France, September 13-16, 2000,Proceedings (2000), D. A. Zighed, H. J. Komorowski, and J. M. Zytkow, Eds., vol. 1910 of Lecture Notes in Computer Science, Springer.
[5] Tu Bao Ho, N. B. N. Nonhierarchical document clustering based on a tolerance rough set model. International Journal of Intelligent Systems 17, 2 (2002),199{212.
[6]. Jiawei Han, M. K. Data Mining: Concepts and Techniques, 1st ed. Morgan Kaufmann,2000.
[7] Porter, M. F. An algorithm for su±x stripping. In Readings in Information Retrieval, P. W. Karen Sparck Jones, Ed. Morgan Kaufmann, San Francisco, 1997, pp. 130{137.
[8]. Weiss, D. A clustering interface for web search results in polish and english, 2001.
[9]. Osinski, S. An algorithm for clustering of web search result. Master's thesis, Poznan University of Technology, Poland, June 2003.
[10] Zamir, O., and Etzioni, O. Grouper: a dynamic clustering interface to web search results. Computer Networks (Amsterdam, Netherlands: 1999) 31, 11-16 (1999), 1361-1374.
[11]. Smadja, F. A. From n-grams to collocations: An evaluation of xtract. In 29th Annual Meeting of the Association for omputational Linguistics, 18-21 June 1991, University of California, Berkeley, California, USA, Proceedings (1991), pp. 279-284.
[12]. D. Terzopoulos, X. Tu, and R. Grzeszczuk, Artificial fishes with autonomous locomotion, perception, behavior, and learning in a simulated physical world," in Artificial life 1, p. 327 ,MIY Press, 1994.
[13]. K. Sims, Evolving 3rd morphology and behavior by competition, " in Artificial Life III, p.353, MIT Press,1994.
[14]. Infoseek. http://infoseek.com.
[2]. Pawlak, Z. Rough sets: Theoretical aspects of reasoning about data. Kluwer Dordrecht,1991.[
[3]. Adrzej Skowron, J. S. Tolerance approximation spaces. Fundamental Informaticae 27, 2-3 (1996), 245 -253.
[4] Saori Kawasaki, Ngoc Binh Nguyen, T. B. H. Hierarchical document clustering based on tolerance rough set model. In Principles of Data Mining and Knowledge Discovery, 4th European Conference, PKDD 2000, Lyon, France, September 13-16, 2000,Proceedings (2000), D. A. Zighed, H. J. Komorowski, and J. M. Zytkow, Eds., vol. 1910 of Lecture Notes in Computer Science, Springer.
[5] Tu Bao Ho, N. B. N. Nonhierarchical document clustering based on a tolerance rough set model. International Journal of Intelligent Systems 17, 2 (2002),199{212.
[6]. Jiawei Han, M. K. Data Mining: Concepts and Techniques, 1st ed. Morgan Kaufmann,2000.
[7] Porter, M. F. An algorithm for su±x stripping. In Readings in Information Retrieval, P. W. Karen Sparck Jones, Ed. Morgan Kaufmann, San Francisco, 1997, pp. 130{137.
[8]. Weiss, D. A clustering interface for web search results in polish and english, 2001.
[9]. Osinski, S. An algorithm for clustering of web search result. Master's thesis, Poznan University of Technology, Poland, June 2003.
[10] Zamir, O., and Etzioni, O. Grouper: a dynamic clustering interface to web search results. Computer Networks (Amsterdam, Netherlands: 1999) 31, 11-16 (1999), 1361-1374.
[11]. Smadja, F. A. From n-grams to collocations: An evaluation of xtract. In 29th Annual Meeting of the Association for omputational Linguistics, 18-21 June 1991, University of California, Berkeley, California, USA, Proceedings (1991), pp. 279-284.
[12]. D. Terzopoulos, X. Tu, and R. Grzeszczuk, Artificial fishes with autonomous locomotion, perception, behavior, and learning in a simulated physical world," in Artificial life 1, p. 327 ,MIY Press, 1994.
[13]. K. Sims, Evolving 3rd morphology and behavior by competition, " in Artificial Life III, p.353, MIT Press,1994.
[14]. Infoseek. http://infoseek.com.
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Abstract: Today traditional intrusion detection systems are unable to detect intrusion attacks. Huge number of
false alarm generated by the system results in financial loss of an organization. The unique features of artificial
immune system encourage and motivate the researchers to employ this technique in variety of applications and
especially in intrusion detection systems. Recently Artificial immune system (AIS) has been applied for anomaly
based intrusion detection in computer networks. Artificial immune system is a new technique which is applied
for solving various problems in the field of information security. In this paper we presents a intrusion detection
system based on one of the algorithm of artificial immune system called the Dendritic Cell Algorithm (DCA)
and Dempster–Belief Theory (DBT) in order to minimise the rate of the generation of intrusion detection
system , false positive rate and improve correlation factor in the designed intrusion detection system. With the
help of Dempster–Belief theory we calculate the degree of uncertainty and with the help of event gathering
calculate the entropy, which help us to determine the intrusion in the given system. Data having higher entropy
is regarded as the "intruder" and generate the alarm. Thus with the help of this dual detection technique we
can not only minimize the false positive and false negative rate but also improved the correlation technique and
decrease the intrusion rate in the system.
Keywords- Artificial Immune System, intrusion detection system, human immune system, danger theory,
negative selection algorithm, DCA, Dempster–Belief theory.
false alarm generated by the system results in financial loss of an organization. The unique features of artificial
immune system encourage and motivate the researchers to employ this technique in variety of applications and
especially in intrusion detection systems. Recently Artificial immune system (AIS) has been applied for anomaly
based intrusion detection in computer networks. Artificial immune system is a new technique which is applied
for solving various problems in the field of information security. In this paper we presents a intrusion detection
system based on one of the algorithm of artificial immune system called the Dendritic Cell Algorithm (DCA)
and Dempster–Belief Theory (DBT) in order to minimise the rate of the generation of intrusion detection
system , false positive rate and improve correlation factor in the designed intrusion detection system. With the
help of Dempster–Belief theory we calculate the degree of uncertainty and with the help of event gathering
calculate the entropy, which help us to determine the intrusion in the given system. Data having higher entropy
is regarded as the "intruder" and generate the alarm. Thus with the help of this dual detection technique we
can not only minimize the false positive and false negative rate but also improved the correlation technique and
decrease the intrusion rate in the system.
Keywords- Artificial Immune System, intrusion detection system, human immune system, danger theory,
negative selection algorithm, DCA, Dempster–Belief theory.
[1] Farhoud Hosseinpour, Kamalrulnizam Abu Bakar, Amir Hatami Hardoroudi, Nazaninsadat Kazazi, "Survey on
Artificial Immune System as a Bio-inspired Technique for Anomaly Based Intrusion Detection Systems" 2010
International Conference on Intelligent Networking and Collaborative Systems, pp 158-189.
[2] P. Matzinger, "Tolerance, danger and the extended family," Annual Review in Immunology, vol. 12, pp. 991–1045,
1994.
[3] Debar H, Wespi A (2001), Aggregation and Correlation of Intrusion-Detection Alerts, the Fourth workshop on the
Recent Advances in Intrusion Detection, LNCS 2212, pp 85-103.
[4] Dasgupta, "Immunity-based intrusion detection system: a general framework, Proceeding of the 22nd National
Information Systems Security Conference (NISSC)", Arlington, Virgina, pp.147-160, 1999
[5] Matzinger. P, (1994) "Tolerance, Danger and the Extended Family", Annual Review in Immunology, vol.12, 2004, pp.
991-1045.
[6] G. Shafer, A Mathematical Theory of Evidence, Princeton, University Press, Princeton, NJ, 1976
[7] Aickelin U, Cayzer S (2002), "The Danger Theory and Its Application to AIS", 1st International Conference on AIS,
2002, pp. 141-148..
[8] Dasgupta and Gonzalez, "An Immunity-Based Technique to Characterize Intrusions in Computer Networks", IEEE
Trans on Evolutionary Computation, pp.281-291, 2002.
[9] D. Barbara, N. Wu, and S. Jajodia, "Detecting novel network intrusions using bayes estimators," in Proceedings of the
First SIAM International Conference on Data Mining (SDM 2001), Chicago, USA, Apr. 2001.
[10] Guo Chen ,Peng Shuo ,Jiang Rong ,Luo Chao, "An anomaly detection system based on dendritic cell algorithm", 2009
Third International Conference on Genetic and Evolutionary Computing,pp192-195.
Artificial Immune System as a Bio-inspired Technique for Anomaly Based Intrusion Detection Systems" 2010
International Conference on Intelligent Networking and Collaborative Systems, pp 158-189.
[2] P. Matzinger, "Tolerance, danger and the extended family," Annual Review in Immunology, vol. 12, pp. 991–1045,
1994.
[3] Debar H, Wespi A (2001), Aggregation and Correlation of Intrusion-Detection Alerts, the Fourth workshop on the
Recent Advances in Intrusion Detection, LNCS 2212, pp 85-103.
[4] Dasgupta, "Immunity-based intrusion detection system: a general framework, Proceeding of the 22nd National
Information Systems Security Conference (NISSC)", Arlington, Virgina, pp.147-160, 1999
[5] Matzinger. P, (1994) "Tolerance, Danger and the Extended Family", Annual Review in Immunology, vol.12, 2004, pp.
991-1045.
[6] G. Shafer, A Mathematical Theory of Evidence, Princeton, University Press, Princeton, NJ, 1976
[7] Aickelin U, Cayzer S (2002), "The Danger Theory and Its Application to AIS", 1st International Conference on AIS,
2002, pp. 141-148..
[8] Dasgupta and Gonzalez, "An Immunity-Based Technique to Characterize Intrusions in Computer Networks", IEEE
Trans on Evolutionary Computation, pp.281-291, 2002.
[9] D. Barbara, N. Wu, and S. Jajodia, "Detecting novel network intrusions using bayes estimators," in Proceedings of the
First SIAM International Conference on Data Mining (SDM 2001), Chicago, USA, Apr. 2001.
[10] Guo Chen ,Peng Shuo ,Jiang Rong ,Luo Chao, "An anomaly detection system based on dendritic cell algorithm", 2009
Third International Conference on Genetic and Evolutionary Computing,pp192-195.
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Paper Type | : | Research Paper |
Title | : | Web Based Solution for Thermal Printing of Bar Code |
Country | : | India |
Authors | : | Manpreet Singh || Baljinder Singh |
: | 10.9790/0661-0154446 | |
ABSTRACT: A barcode is an optical machine-readable representation of data, which shows the relevant data about the material to which it is attached or fixed. With the help of bar code, the information can be assessed automatically, quickly and accurately. It is the fastest means of gathering data and can be generated easily and inexpensively by using state to art technologies. They can be printed dot-matrix, laser, and thermal transfer printers depending on the quality and demand. Thermal printing i.e. direct and ribbon-less, is comparably more consistent and generates clear image compatible with latest scanning technologies. Moreover, it is long-lasting, inexpensive and provides the flexibility of generating bar codes for different kinds of labels of different sizes and utility. The software available in the market are not supporting the thermal printing by integrating the barcode as well as picking the production status from software. In the present work, integrated code-generator utility is implemented to generate Bar-code image and then print is taken on Taffeta paper using integrated printing facility which in turn reflects production status of the product.
Keywords: barcode, code-generator, production status, scanning, thermal printing.
Keywords: barcode, code-generator, production status, scanning, thermal printing.
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[5] T. Han, C. Cheong, N. Lee, and E. Shin. Machine readable code image and method of encoding and decoding the same, U.S. Patent 7020327, 2000.
[6] http://www.percon.com/whitepapers/Bar_Code_in_manufacturing.pdf
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