Refine your search
Co-Authors
Journals
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Prabha, K.
- Single-Keyword Pattern Matching Algorithms for Network Intrusion Detection System
Abstract Views :573 |
PDF Views:0
Authors
K. Prabha
1,
S. Sukumaran
2
Affiliations
1 Research Scholar, Erode Arts and Science College, Erode–638009, Tamil Nadu, IN
2 Erode Arts and Science College, Erode–638009, Tamil Nadu, IN
1 Research Scholar, Erode Arts and Science College, Erode–638009, Tamil Nadu, IN
2 Erode Arts and Science College, Erode–638009, Tamil Nadu, IN
Source
International Journal of Computer and Internet Security, Vol 5, No 1 (2013), Pagination: 11-18Abstract
The Network Intrusion Detection System (NIDS) is an important part of any modern network. One of the important processes in NIDS is inspecting of individuals' packets in network traffic, deciding if these packets are infected with any malicious activities. This process, which is called content matching, is done via string matching algorithms. The content matching is considered the heart of NIDS. The content matching phase consumes most of the processing time inside the NIDS and slowed down around 70% of NIDS performance. In this case, it is difficult for NIDS to distinguish between normal network packets and abnormal network packets and consequently drop numbers of network packets. New algorithms are needed to enhance the matching since enormous packets are passing through the network every second. In this paper we presented a survey of single keyword pattern matching algorithms for NIDS.
References
- Anagnostakis K G, Markatos E P, Antonatos S, Polychronakis M, “A domainspecific string matching algorithm for intrusion detection”, Proceedings of the 18th IFIP International Information Security Conference (SEC2003). Athens, Greece: Kluwer Acadamic Pubhishers, 2003.
- Bi Kun., Gu. Nai-jie., Tu Kun., Liu. Xiao-hu. and Liu. Gang, “A Practical Distributed String Matching Algorithm Architecture and Implementation”, Proceeding of world academy of science, engineering and technology, 2005.
- B. Watson, “The performance of single-keyword and multiple-keyword pattern matching algorithms”, Eindhoven University of Technology. Department of Mathematics and Computing Science. 1994.
- Coit C J, Staniford S, McAlerney J, “Towards faster string matching for intrusion detection or exceeding the speed of Snort”, Proceedings of the DARPA Information Survivability Conference and Exposition II (DISCEX’01). Los Alamitos, CA, USA: IEEE Comput. Soc., 2001.
- D. M. Sunday, “A very fast substring search algorithm”, Communications of the Association for Computing Machinery, 1990.
- Fisk M, Varghese G, “An analysis of fast string matching applied to contentbased forwarding and intrusion detection”, Technical Report CS2001-0670. San Diego: University of California, 2002.
- Gaston Gonnet and Ricardo Baeza-Yates, “An analysis of the Karp-Rabin string matching algorithm”, Data Structuring Group. Department of Computer Science. University of Waterloo. May 1989.
- Nathan Tuck, Timothy Sherwood, Brad Calder, George Varghese, “Deterministic Memory-Efficient String Matching Algorithms for Intrusion Detection”. Department of Computer Science and Engineering, University of California, San Diego. Department of Computer Science, University of California, Santa Barbara. 2004.
- Nilsen G, Torresen J, Sorasen O, “A variable word-width content addressable memory for fast string matching”, Proceedings of the 22nd Norchip Conference. Osio, Norway: IEEE, 2004.
- Norton M, Roelker D,“The new Snort”, Computer SecurityJournal, 2003.
- Oddiraju, Sriharsha. “Boyer Moore”. Computer Science Department. Indiana State University. December 2011.
- Plaxton, Greg, “String Matching: Rabin-Karp Algorithm”, Theory in Programming Practice. University of Austin, Texas. Fall 2005.
- P. S. Wheeler, “Techniques for improving the performance of signature based intrusion detection systems,” Master’s thesis, University of California Davis, 2006.
- Qingzhang CHEN, Yibo NIU, Zhehu WANG, Feng DU, “Improved BM Pattern Matching Algorithm for Intrusion Detection”, 2010 Third International Joint Conference on Computational Science and Optimization. 2010.
- R.N. Horspool, “Practical fast searching in strings”, Software-Practiceand Experience, Vol. 10, no. 6, 1980.
- Roesch M,“Snort: Lightweight intrusion detection for networks”, Proceedings of the 13th System Administration Conference.
- R.S. Boyer and J.S.Moore,“A fast string searching algorithm”, Communications of the Association for Computing Machinery (ACM),Vol. 20, no. 10, 1977.
- Wu S, Manber U, “A fast algorithm for multi-pattern searching”,Technical Report TR-94-17, University of Arizona, 1994.
- Yuehui Chen, Abraham, Ajith and Crina Grosan, “Evolution of Intrusion Detection Systems”, School of Computer Science and Engineering, Chung-Ang University, Korea. 2005.
- Motion Capture for Computer 3D Character Animation
Abstract Views :165 |
PDF Views:2
Authors
Affiliations
1 Department of MCA, Excel Business school, Komarapalayam, Tamilnadu, IN
2 Department of Computer Science, Erode Arts and Science college, Erode, Tamil Nadu, IN
3 Department of Computer Science, Erode Arts and Science College, Erode, Tamil Nadu, IN
1 Department of MCA, Excel Business school, Komarapalayam, Tamilnadu, IN
2 Department of Computer Science, Erode Arts and Science college, Erode, Tamil Nadu, IN
3 Department of Computer Science, Erode Arts and Science College, Erode, Tamil Nadu, IN
Source
Networking and Communication Engineering, Vol 3, No 5 (2011), Pagination: 321-325Abstract
The process of recording movement and translating that movement on to a digital model is called motion capture or motion tracking. The technique was originally used for military tracking purposes and in sports as a tool for biomechanical research which focused on the mechanical functioning of the body, like how the heart and muscles work and move. In filmmaking it refers to recording actions of human actors, and using that information to animate digital character models in 2D or 3D computer animation. The information captured can be as general as the simple position of the body in space or as complex as the deformations of the face and muscle masses.
Motion capture for computer character animation involves the mapping of human motion onto the motion of a computer character. The mapping can be direct, such as human arm motion controlling a character’s arm motion, or indirect, such as human hand and finger patterns controlling a character’s skin color or emotional state. When it includes face and fingers or captures subtle expressions, it is often referred to as performance capture.
Motion capture has become an essential tool in the entertainment business, giving computer animators the ability to make non-human characters more life-like. In the entertainment application this technology can reduce the costs of keyframe-based animation. This work presents a detailed study on the techniques used in motion capture for 3D character animation.
Keywords
Optical Systems, Mocap, Rotoscope, Motion Warping, Photogrammetry, Post Processing.- Efficient Keyword Based Document Clustering Using Fuzzy C-Means Algorithm
Abstract Views :182 |
PDF Views:3
Authors
Affiliations
1 Erode Arts and Science College, Erode-638009, Tamil Nadu, IN
1 Erode Arts and Science College, Erode-638009, Tamil Nadu, IN
Source
Data Mining and Knowledge Engineering, Vol 5, No 12 (2013), Pagination: 460-463Abstract
Clustering is an useful technique in the field of textual data mining. Cluster analysis divides objects into meaningful groups based on similarity between objects. The existing clustering approaches face the issues like practical applicability, very less accuracy, more classification time etc. In recent times, inclusion of fuzzy logic in clustering results in better clustering results. In order to further improve the performance of clustering, the Fuzzy C-Means (FCMA) Algorithm is used. The keywords are extracted from the documents using LSA based document extraction. The Fuzzy partition matrix is created for the clustering process and the performance of the document clustering is greater based on the keyword when compared to the Existing K-Means Clustering and EM Algorithm. The proposed technique will be highly useful in the text mining process to increase the accuracy and performance of the text extraction process.Keywords
Document Clustering, Fuzzy Cluster, Fuzzy C-Means, K-Means Clustering.- Mucormycosis epidemic and biosecurity concerns of biocontrol agents in the cropping system
Abstract Views :218 |
PDF Views:87
Authors
Affiliations
1 ICAR-Directorate of Floricultural Research, Shivajinagar, Pune 411 005, IN
1 ICAR-Directorate of Floricultural Research, Shivajinagar, Pune 411 005, IN
Source
Current Science, Vol 122, No 5 (2022), Pagination: 512-512Abstract
No Abstract.Keywords
No keywordsFull Text
References
- Sen, M., Lahane, S., Lahane, T. P., Parekh, R. and Honavar, S. G., Indian J. Ophthalmol., 2021, 69, 244–252.
- Vilela, R. and Mendosa, L., Microbiol. Rev., 2018; https://doi.org/10.1128/CMR.0001418.
- Thambugala, K. M., Daranagama, D. A., Phillips, A. J. L., Kannangara, S. D. and Promputtha, I., Front. Cell. Infect. Microbiol., 2020, 10, 604923; doi:10.3389/fcimb.2020.604923.
- Hatvani, L. et al., FEMS Microbiol. Lett., 2019; doi:10.1093/femsle/fnz246; PMID: 31816013.