Refine your search
Collections
Co-Authors
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
Ramakrishnan, B.
- A Novel Approach for Data Privacy Using Attribute Based Scheme Algorithm for Cloud Computing
Abstract Views :314 |
PDF Views:0
Authors
Affiliations
1 S.T. Hindu College, Nagercoil, Tamilnadu, IN
2 Department of Computer Science and Research Centre, S.T. Hindu College, Nagercoil, Tamilnadu, IN
3 Department of Software Engineering, Firat University, 23119, Elazig, TR
1 S.T. Hindu College, Nagercoil, Tamilnadu, IN
2 Department of Computer Science and Research Centre, S.T. Hindu College, Nagercoil, Tamilnadu, IN
3 Department of Software Engineering, Firat University, 23119, Elazig, TR
Source
International Journal of Computer Networks and Applications, Vol 3, No 4 (2016), Pagination: 70-77Abstract
Cloud computing is the mass storage area that helps the user to access the data anywhere. There are so many platforms provided by the cloud service provider. They are SaaS (Software as a Service), PaaS (Platform as a Service) and IaaS (Infrastructure as a Service) etc. Though security is not fully provided by the cloud service provider to reshape the advances in information technology, cloud computing is expected as an updated technology. The data was securely stored in the cloud and if it is corrupted then the proxy is implemented to regenerate the corrupted data in the cloud. Thus security and integrity is successfully achieved. This is further extended by implementing efficient file fetching by the third party user. To maintain efficient file fetching system Multi authority cloud model is proposed. The model is continuing with the proposed entities such as Attribute authority (AA), Certificate Authority (CA) and Third party end user. The data is encrypted by the owner and stored in the cloud server. CA is used to delegate the Secret Key (SK) to AA and Public Kay (PK) to user. After Checking the authentication of the owner CA provides PK to the owner only then the owner is allowed to upload the data in cloud, the data is encrypted and outsource to the cloud server. Using SK the third party user is allowed to view the data from the cloud. If the user enter the wrong key or misuse the data, user will be revoked. If the User needs to download or update or delete the data in the cloud the user need to send a Data Access Privilege (DAP) request to the respective owner. Certificate authority is responsible to generate a key to the entities such as User, Data Owner and attributes.Keywords
Cloud Computing, Security, Third Party Auditor (TPA), Proxy, RSA Algorithm, Regeneration, Multiuser Authentication.Full Text
- A Novel Routing Scheme to Avoid Link Error and Packet Dropping in Wireless Sensor Networks
Abstract Views :302 |
PDF Views:4
Authors
Affiliations
1 S.T. Hindu College, Nagercoil-2, IN
2 Department of Computer Science and Research Centre, S.T. Hindu College, Nagercoil-2, IN
3 Department of Software Engineering, Firat University, 23119, Elazig, TR
1 S.T. Hindu College, Nagercoil-2, IN
2 Department of Computer Science and Research Centre, S.T. Hindu College, Nagercoil-2, IN
3 Department of Software Engineering, Firat University, 23119, Elazig, TR
Source
International Journal of Computer Networks and Applications, Vol 3, No 4 (2016), Pagination: 86-94Abstract
Packet loss is a major issue in Wireless Sensor Network (WSN) data transmission which is caused by malicious packet dropping and link error. In conventional methods, the malicious dropping may result in a packet loss rate that is comparable to normal channel losses, the stochastic processes that characterizes the two phenomena exhibited in different correlation which would affect the network performance i.e. detection accuracy. By detecting the correlations between lost packets, we will decide whether the packet loss is purely due to regular link errors, or is a combined effect of link error and malicious drop. In order to overcome these issues, we have proposed a HLA (Homomorphic Linear Authentication) based on routing protocol which is a collusion proof mechanism and resolves the public auditing problem. Here, the proposed technique will be implemented in OLSR (Optimized Link State Routing) Protocol. The actual status of each packet transmission i.e., the packet loss information can be described by our technique. The network simulation results describe the performance of the proposed method in terms of detection accuracy in low computation complexity. Our HLA based OLSR protocol is compared with existing AODV, RIP and other protocols.Keywords
WSN, AODV, OLSR, HLA, malicious node attack, Link Error.Full Text
- Prioritized and Secured Data Dissemination Technique in VANET Based on Optimal Blowfish Algorithm and Signcryption Method
Abstract Views :219 |
PDF Views:0
Authors
M. Selvi
1,
B. Ramakrishnan
2
Affiliations
1 S.T. Hindu College, Nagercoil-2, IN
2 Department of Computer Science and Research Centre, S.T. Hindu College, Nagercoil-2, IN
1 S.T. Hindu College, Nagercoil-2, IN
2 Department of Computer Science and Research Centre, S.T. Hindu College, Nagercoil-2, IN
Source
International Journal of Computer Networks and Applications, Vol 2, No 4 (2015), Pagination: 165-172Abstract
All the way through cooperative behaviour of the vehicular nodes information dissemination in VANETs occurs. Vital information like traffic jam, emergency brake procedures, road conditions, accident warnings, bad weather conditions, etc are messages transmitted in vehicular network. Misbehaviours and false hindering is a serious issue in VANET if any vehicles maliciously send messages. Deviation from the average behaviour of other vehicular nodes in the Consecutively to make VANET a secure network, detection of misbehaviors and the malicious vehicular nodes concerned in such misconducts is tremendously imperative. In this paper we have proposed a message broadcasting and message priority assignment in vehicular networks to resolve this problem. Initially the messages are prioritized using SMTP metric. we are using the three levels of priority's those are emergency, general request and entertainment request and then finally secured information transaction via., optimal blowfish algorithm based signcryption technique. The performance of the proposed algorithm is analyzed with existing techniques.Keywords
VANET, Data Dissemination, Sybil Attack, DOS, Signcryption, Blowfish, Cuckoo Search.Full Text
- Adaptive Routing Protocol based on Cuckoo Search algorithm (ARP-CS) for secured Vehicular Ad hoc network (VANET)
Abstract Views :253 |
PDF Views:0
Authors
Affiliations
1 Department of Computer Science and Research Centre, S.T. Hindu College, Nagercoil, Tamilnadu, IN
2 S.T. Hindu College, Nagercoil, Tamilnadu, IN
1 Department of Computer Science and Research Centre, S.T. Hindu College, Nagercoil, Tamilnadu, IN
2 S.T. Hindu College, Nagercoil, Tamilnadu, IN
Source
International Journal of Computer Networks and Applications, Vol 2, No 4 (2015), Pagination: 173-178Abstract
A wide range of protocols are used to achieve secured message broadcasting in VANET. Most commonly used protocols are topology based routing and geography based routing protocols. In order to overcome the issues of these 2 protocols, we proposed a design of Adaptive routing protocol based on Cuckoo search algorithm (ARP-CS). The adaptive protocol combines the features of both topology routing and geographic routing protocols which ensures the secured transmission of data with less delay and high packet delivery ratio. ARP-CS provides reliable and secure routes between source and destination node with optimal distance and low routing overheads. ARP-CS uses a local stochastic broadcasting to find routes which reduces the network congestion thereby improving the packet delivery ratio.Keywords
VANET, AODV, GPRS, Routing Protocol, Cuckoo Search.Full Text
- Improved Signcryption Algorithm for Information Security in Networks
Abstract Views :188 |
PDF Views:1
Authors
Affiliations
1 Velammal Engineering College, Chennai-66, IN
2 Department of Computer Science and Research Centre, S.T. Hindu College, Nagercoil, IN
3 S.T. Hindu College, Nagercoil, IN
1 Velammal Engineering College, Chennai-66, IN
2 Department of Computer Science and Research Centre, S.T. Hindu College, Nagercoil, IN
3 S.T. Hindu College, Nagercoil, IN
Source
International Journal of Computer Networks and Applications, Vol 2, No 3 (2015), Pagination: 151-157Abstract
In a Cryptographic primordial, the functions of the digital signature and the public key encryption are concurrently carried out. To safely communicate incredibly large messages, the cryptographic primordial known as the signcryption is effectively employed. Though a lion's share of the public key based mechanism are appropriate for miniature messages, the hybrid encryption (KEM-DEM) offers a proficient and realistic method. In this document, we are cheered to launch an improved signcryption method, which takes cues from the KEM and DEM approaches. The KEM algorithm employs the KDF approach to summarize the symmetric key. The DEM algorithm makes use of the Elliptic curve cryptography technique to encrypt the original message. With an eye on safety aspects, we have testes three attacks and we are cheered to state that the attackers have failed miserably in locating the safety traits of our improved signcryption technique.Keywords
Cryptographic, Signcryption, KEM, DEM, KDF.Full Text
- Analysis of VBF protocol in Underwater Sensor Network for Static and Moving Nodes
Abstract Views :189 |
PDF Views:2
Authors
C. Namesh
1,
B. Ramakrishnan
2
Affiliations
1 Department of Computer Science, S.T. Hindu College, Nagercoil, Tamilnadu, IN
2 Department of Computer Science and Research Centre, S.T. Hindu College, Nagercoil, Tamilnadu, IN
1 Department of Computer Science, S.T. Hindu College, Nagercoil, Tamilnadu, IN
2 Department of Computer Science and Research Centre, S.T. Hindu College, Nagercoil, Tamilnadu, IN
Source
International Journal of Computer Networks and Applications, Vol 2, No 1 (2015), Pagination: 20-26Abstract
Underwater acoustic sensor network is used to monitor the ocean areas with the help of Autonomous Underwater Vehicles, wireless data access points like tools. A number of problems like environmental changes, predicting disaster informations, exploration of mines can be addressed by applying this process in the sea. In this paper, we apply the routing protocol Vector Based Forwarding in two different architecture like static nodes and moving nodes in an underwater architecture. The comparison between the architecture is based on the simulation results, from the comparison the Energy Efficiency, Throughput, PDR are analyzed. Seeing the various graph results, we can conclude that the VBF protocol is significantly beneficial for the underwater architecture with moving nodes than static nodes.Keywords
UWSN, Routing, Vector Based Forwarding, Aqua-Sim, PDR.Full Text
- A Survey of Various Security Issues in Online Social Networks
Abstract Views :267 |
PDF Views:2
Authors
Affiliations
1 Department of Computer Application, St. Jerome’s College, Nagercoil, Tamilnadu, IN
2 Department of Computer Science and Research Centre, S.T. Hindu College, Nagercoil, Tamilnadu, IN
1 Department of Computer Application, St. Jerome’s College, Nagercoil, Tamilnadu, IN
2 Department of Computer Science and Research Centre, S.T. Hindu College, Nagercoil, Tamilnadu, IN
Source
International Journal of Computer Networks and Applications, Vol 1, No 1 (2014), Pagination: 11-14Abstract
The most emerging communication medium for the last decade of years is Online Social Networks (OSNs). Online Social Network makes the communication quicker and cheaper. Facebook, Twitter, Google Plus, MySpace, Orkut, etc are the various existing online social networks. Among all the online social networks very few could turn the attention of the people towards them. However, all these social networks are available on the publicly accessible communication medium called internet. When these social networks are available in the internet, it will lead to various types of security issues. This paper discusses the various security related issues persists in online social networks.Keywords
Online Social Network (OSN), Privacy, Security, Hacking, Malware, Hacking, Spam, Attack.Full Text
- Big Data Analysis for M2M Networks: Research Challenges and Open Research Issues
Abstract Views :221 |
PDF Views:0
Authors
Affiliations
1 Department of Computer Programming, Trakya University, Edirne, 22020, TR
2 Department of Software Engineering, Firat University, Elazig, 23119, TR
3 Department of Computer Science and Research Centre, S.T. Hindu College, Nagercoil, Tamilnadu, IN
4 Department of Computer Engineering, Adnan Menderes University, Aydin, 09010, TR
1 Department of Computer Programming, Trakya University, Edirne, 22020, TR
2 Department of Software Engineering, Firat University, Elazig, 23119, TR
3 Department of Computer Science and Research Centre, S.T. Hindu College, Nagercoil, Tamilnadu, IN
4 Department of Computer Engineering, Adnan Menderes University, Aydin, 09010, TR
Source
International Journal of Computer Networks and Applications, Vol 4, No 1 (2017), Pagination: 27-34Abstract
In recent years, solutions based on machine-to-machine (M2M) communications have started to support us in many areas of our life and work. However, the amount of data collected by M2M has increased tremendously and surpassed our expectations. This makes it necessary to investigate data mining methodologies and machine learning techniques in order to efficiently utilize large amounts of data gathered by M2M devices. In this paper, we first review existing data mining and machine-learning techniques specifically designed and proposed for M2M networks. Then, we discuss Big Data concept, investigate Big Data analysis techniques, and the importance of Big Data for M2M networks. Finally, we investigate research challenges and open research issues in M2M to provide an insight into future research opportunities.Keywords
Machine-to-Machine (M2M), Machine Learning, Data Mining, Big Data.Full Text
References
- J. N. Al-Karaki, K. -C. Chen, G. Morabito and J. de Olieveira, “From M2M communications to the Internet of Things: Opportunities and challenges,” Ad Hoc Networks, vol. 18, pp. 1-2, 2014.
- G. Wu, S. Talwar, K. Johnsson, N. Himayat, and K. D. Johnson, “M2M: From Mobile to Embedded Internet,” IEEE Communications Magazine, vol. 49, no. 4, pp. 36-43, April 2011.
- J. Holler, V. Tsiatsis, C. Mulligan, S. Avesand, S. Karnouskos and D. Boyle, “From Machine-to-Machine to the Internet of Things: Introduction to a New Age of Intelligence,” Academic Press: MA, USA, 2014.
- A. Biral, M. Centenaro, A. Zanella, L. Vangelista, and M. Zorzi, “The challenges of M2M massive access in wireless cellular networks,” Digital Communications and Networks, vol. 1, no. 1, pp. 1-19, February 2015.
- ETSI, “Machine to Machine Communications,” TS 102 689.
- D. Boswarthick, O. Elloumi, and O. Hersent (Eds), M2M Communications: A Systems Approach, Wiley: West Sussex, UK, 2012.
- Z. Fan, Q. Chen, G. Kalogridis, S. Tan, and D. Kaleshi, “The power of data: Data analytics for M2M and smart grid,” 2012 3rd IEEE PES international conference and exhibition on Innovative Smart Grid Technologies (ISGT Europe), pp. 1-8, 2012.
- G. Suciu, A. Vulpe, A. Martian, S. Halunga, and D. N. Vizireanu, “Big Data Processing for Renewable Energy Telemetry Using a Decentralized Cloud M2M System,” Wireless Personal Communications, 2015.
- G. Suciu, V. Suciu, A. Martian, R. Craciunescu, A. Vulpe, I. Marcu, S. Halunga, and O. Fratu, “Big Data, Internet of Things and Cloud Convergence – An Architecture for Secure E-Health Applications,” Journal of Medical Systems, vol. 39, no. 141, 2015.
- A. J. Jara, D. Genoud and Y. Bocchi, “Big Data for Cyber Physical Systems: An Analysis of Challenges, Solutions and Opportunities,” 2014 Eighth International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing, pp. 376-380, 2014.
- I. A. T. Hashem, I. Yaqoob, N. B. Anuar, S. Mokhtar, A. Gani, and S. U. Khan, “The rise of "big data" on cloud computing: Review and open research issues,” Information Systems, vol. 47, pp. 98-115, 2015.
- M.-S. Chen, J. Han, and P. S. Yu, “Data Mining: An Overview from a Database Perspective,” IEEE Transactions on Knowledge and Data Engineering, vol. 8, no. 6, pp. 866-883, December 1996.
- D. Talia, P. Trunfio, and F. Marozzo, “Introduction to Data Mining,” in Data Analysis in the Cloud, pp. 1-25, 2016.
- I. Kononenko and M. Kukar, “Machine Learning Basics,” in Machine Learning and Data Mining, pp. 59-105, 2007.
- T. Kanungo, D. M. Mount, N. S. Netanyahu, C. D. Piatko, R. Silverman, and A. Y. Wu, “An Efficient k-Means Clustering Algorithm: Analysis and Implementation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 7, pp. 881-892, July 2002.
- G. Holmes, A. Donkin, and I. H. Witten, “WEKA: a machine learning workbench,” Proceedings of the 1994 Second Australian and New Zealand Conference on Intelligent Information Systems, pp. 357-371, 1994.
- Z. He, “An overview of data mining,” in Data Mining for Bioinformatics Applications, pp. 1-10, 2015.
- A. Lausch, Andreas Schmidt, and Lutz Tischendorf, “Data mining and linked open data – New perspectives for data analysis in environmental research,” Ecological Modelling, vol. 295, pp. 5-17, 2015.
- C. C. Aggarwal, Data Mining, Springer International Publishing: New Delhi, India, May 2015. ISBN: 978-3-319-14141-1.
- Sapphire: Large Scale Data Mining and Pattern Recognition. Available at: http:// computation.llnl.gov/casc/sapphire/overview/overview.html. Accessed: October 7, 2015.
- S. M. Weiss and C. A. Kulikowski, Computer Systems that Learn. Morgan Kaufmann: San Mateo, CA, 1991.
- R. Duda and P. Hart, Pattern Recognition and Scene Analysis. Wiley, New York, 1973.
- R. E. Abdel-Aal, “Comparison of Algorithmic and Machine Learning Approaches for the Automatic Fitting of Gaussian Peaks,” Neural Computing & Applications, vol. 11, no. 1, pp. 17-29, 2002.
- P. D. Wasserman, Neural Computing: Theory and Practice. Van Nostrand Reinhold, New York, 1989.
- G. Krempl, I. Žliobaite, D. Brzeziński, E. Hüllermeier, M. Last, V. Lemaire, T. Noack, A. Shaker, S. Sievi, M. Spiliopoulou, and J. Stefanowski, “Open challenges for data stream mining research,” SIGKDD Explor. Newsl., vol. 16, no.1, pp. 1-10, September 2014.
- H. Mannila. Methods and Problems in Data Mining. In Proceedings of the 6th International Conference on Database Theory (ICDT '97), F. N. Afrati and P.G. Kolaitis (Eds.). Springer-Verlag, London, UK, 1997. pp. 41-55.
- C. E. Brodley, U. Rebbapragada, K. Small, and B. C. Wallace, “Challenges and Opportunities in Applied Machine Learning,” AI Magazine, vol. 33, no. 1, pp. 11-24, March 2012.
- C. Parker “Unexpected challenges in large scale machine learning,” in Proceedings of the 1st International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications (BigMine '12). ACM, New York, NY, USA, 2012. pp. 1-6.
- R. Genuer, J.-M. Poggi, C. Tuleau-Malot, N. Villa-Vialaneix, “Random forests and big data,” 47`eme Journ´ees de Statistique de la SFdS, Jun 2015, Lille, France. 2015.
- M. I. Jordan, “On statistics, computation and scalability,” Bernoulli, vol. 19, no. 4, pp. 1378-1390, 2013.
- L. Breiman, “Random forests,” Machine Learning, vol. 45, no. 1, pp. 5-32, 2001.
- A. Verikas, A. Gelzinis, and M. Bacauskiene, “Mining data with random forests: a survey and results of new tests,” Pattern Recognition, vol. 44, no. 2, pp. 330-349, 2011.
- A. Ziegler and I. R. König, “Mining data with random forests: current options for real-world applications,” Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, vol. 4, no. 1, pp. 55-63, 2014.
- I. Arel, D. C. Rose, and T. P. Karnowski, “Research frontier: deep machine learning--a new frontier in artificial intelligence research,” IEEE Computational Intelligence Magazine, vol. 5, no. 4, pp. 13-18, November 2010.
- C. L. Philip Chen and Chung-Yang Zhang, “Data-intensive applications, challenges, techniques and technologies: A survey on Big Data,” Information Sciences, vol. 275, pp. 314-347, 10 August 2014.
- Y. Bengio, A. Courville, and P. Vincent, “Representation learning: a review and new perspectives,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 8, pp. 1798-1828, 2013.
- D. Yu and L. Deng, “Deep learning and its applications to signal and information processing,” IEEE Signal Processing Magazine, vol. 28, no. 1, pp. 145-154, 2011.
- J. X. Dong, A. Krzyzak, and C. Y. Suen, “Fast SVM training algorithm with decomposition on very large data sets,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 4, pp. 603-618, 2005.
- P. Pääkkönen and D. Pakkala, “Reference Architecture and Classification of Technologies, Products and Services for Big Data Systems,” Big Data Research, vol. 2, pp. 166-186, 2015.
- X. Jin, B. W. Wah, X. Cheng, and Y. Wang, “Significance and Challenges of Big Data Research,” Big Data Research, vol. 2, pp. 166-186, 2015.
- Y. Ma, H. Wu, L. Wang, B. Huang, R. Ranjan, A. Zomaya, and W. Jie, “Remote sensing big data computing,” Future Generation Computing Systems, vol. 51, pp. 45-60, October 2015.
- J. Cervantes, F. G. Lamont, A. López-Chau, L. R. Mazahua, J. Sergio Ruíz, “Data selection based on decision tree for SVM classification on large data sets,” Applied Soft Computing, vol. 37, pp. 787-798, December 2015.
- L. Bottou, “Large-Scale Machine Learning with Stochastic Gradient Descent,” in Proceedings of COMPSTAT'2010, pp. 177-186, 30 September 2010.
- K. Sopyła and P. Drozda, “Stochastic Gradient Descent with Barzilai–Borwein update step for SVM,” Information Sciences, vol. 316, pp. 218-233, 2015.
- T. D. Kulkarni, P. Kohli, J. B. Tenenbaum, and V. Mansinghka, “Picture: A probabilistic programming language for scene perception,” 2015 IEEE Conference on Computer Vision and Pattern Recognition, pp. 4390-4399, 2015.
- K. Kumar and Yung-Hsiang Lu, “Cloud Computing for Mobile Users: Can Offloading Computation Save Energy?” Computer, vol. 43, no. 4, pp. 51-56.
- A. Botta, W.de Donato, V. Persico, and A. Pescapé, “Integration of Cloud computing and Internet of Things: A survey,” Future Generation Computer Systems, 2015.
- Y. Yang and X. Liu, “A robust semi-supervised learning approach via mixture of label information,” Pattern Recognition Letters, vol. 68, part 1, pp. 15-21, 15 December 2015.
- Y. Zhu, E. Zhong, Z. Lu, and Q. Yang, “Feature engineering for semantic place prediction,” Pervasive and Mobile Computing, vol. 9, no. 6, pp. 772-783, December 2013.
- R. Hu, B. M. Namee, and S. J. Delany, “Active learning for text classification with reusability,” Expert Systems with Applications, vol. 45, pp. 438-449, 1 March 2016.
- H. Rajaona, F. Septier, P. Armand, Y. Delignon, C. Olry, A. Albergel, and J. Moussafir, “An adaptive Bayesian inference algorithm to estimate the parameters of a hazardous atmospheric release,” Atmospheric Environments, vol. 122, pp. 748-762, December 2015.
- N. V. Chawla, Data mining for imbalanced datasets: An overview. In Data Mining and Knowledge Discovery Handbook, Springer US, 2010, pp. 875-886.
- J. Gama, R. Sebastião, and P. P. Rodrigues, “Issues in evaluation of stream learning algorithms,” in Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, June 2009, pp. 329-338.
- A. Ahmad, A. Paul, and M. M. Rathore, “An efficient divide-and-conquer approach for big data analytics in machine-to-machine communication,” Neurocomputing, vol 174, part A, pp. 439-453, 22 January 2016.
- G. Suciu, A. Vulpe, O. Fratu, and V. Suciu, “M2M remote telemetry and cloud IoT big data processing in viticulture,” 2015 International Wireless Communications and Mobile Computing Conference (IWCMC), 2015, pp. 1117-1121.
- R. T. Kouzes, G. A. Anderson, S. T. Elbert, I. Gorton, and D. K. Gracio, “The changing paradigm of data-intensive computing,” Computer, vol. 42, no. 1, pp. 26-34, 2009.