Precision Clustering Based on Boundary Region Analysis for Share Market Database
Research Paper | Journal Paper
Vol.7 , Issue.4 , pp.113-118, Apr-2019
CrossRef-DOI: https://doi.org/10.26438/ijcse/v7i4.113118
Abstract
In many research areas it’s always found that it is very difficult to cluster the databases which come under the close region of clusters. When the database has a unique cluster then it is faster to make the cluster in lesser times but when it is coming to closer region of two or more clusters then the time taken for the clustering is high and need to be clustered very carefully by examining each attributes. In this paper clustering is done using the partitioning method and complex regions are selected which are closed to two or more cluster and this selected database is again carefully examined by each of the attribute and then finally clustered to produce more accuracy than the partitioning method.
Key-Words / Index Term
Boundary region analysis, Precision Clusters, Share market database, Large database, Reduced Dataset, Attribute Selection, etc
References
[1] Dingsheng, W, Xiang, R, & Yuting, H 2010, ‘Data mining algorithmic research and application based on information entropy’, Pattern Recognition, vol. 43, no. 1, pp. 5–13.
[2] Gheyas, I & Smith, L 2010, ‘Feature subset selection in large dimensionality domains’, Pattern Recognition, vol. 43, no. 1, pp. 5-13.
[3] V.Srinivasan ‘Feature Selection Algorithm using Fuzzy Rough Sets for Predicting Cervical Cancer Risks’ International Journal Modern Applied Science Vol. 4 Issue 9 10 sep 2010.
[4] Guyon, I & Elisseeff, A 2003, ‘An introduction to variable and feature selection’, Journal of Machine Learning Research, vol. 3, no. 7, pp. 1157-1182.
[5] Halperin, E & Karp, RM 2005, ‘The minimum-entropy set cover problem’, Theoretical Computer Science, vol. 348, no. 2, pp. 240-250.
[6] Indian Stock Exchange 2003, Stock Board information. Available from
[7] Indian Stock Exchange 2005, Share Market Information. Available from
[8] V.Srinivasan ‘Classify the student with missing value to calculate future semester result for placement record using knowledge acquisition’ National Journal Vol. 3 Issue No.3 2010.
[9] Jiawei, H & Micheline, K 2006, ‘Data Mining: Concepts and Techniques, 2nd edition’, Morgan Kaufmann Publishers.
[10] Kedarnath, JB. & Nur, AT 2007, ‘Relationship between entropy and test data compression’, IEEE Transaction on Computer Aided Design of Integrated Circuits and Systems, vol. 26, no. 2, pp. 386-395.
[11] V.Srinivasan ‘A fuzzy fast classification for share market database with lower and upper bounds’ American journal of Applied Science, vol.12, sep 2012, PP.1934-1939.
[12] Liu, H 2005, ‘Evolving feature selection’, IEEE Intelligence System, vol. 20, no. 6, pp. 64-76.
[13] Neelima, B, Jha, CK & Sandeep KB 2012, ‘Application of Neural Network in Analysis of Stock Market Prediction’, International Journal of Computer Science & Engineering Technology, vol. 3, no. 4, pp. 61-68.
[14] Richard, J & Qiang, S 2007, ‘Fuzzy-Rough Sets Assisted Attribute Selection’, IEEE Transactions on Fuzzy Systems, vol. 15, no. 1, pp. 73-89.
[15] V.Srinivasan ‘Fuzzy Classification to Classify the Income Category Based on Entropy’ Journal of Computer science and Technology Vol 11 No.2, 2011.
[16] Slezak, D 2002, ‘Approximate Entropy Reducts’, Fundamenta Informaticae, vol. 53, no. 3, pp. 365-390.
[17] Turiel, A & Vicente, CJP 2003, ‘Multifractal geometry in stock market time series’, Physica A: Statistical Mechanics and its Applications, vol. 322, no. 1, pp. 629-649.
[18] V.Srinivasan ‘Fuzzy Fast Classification Algorithm with Hybrid of ID3 and SVM’ International journal of Intelligence and Fuzzy System, Vol.24, May 2013, pp.556-561.
[19] Yucel, S, Arnold R & YunTong, W 2004, ‘Value of Information Gained From Data Mining in the Context of Information Sharing’, IEEE Engineering Management, vol. 51, no. 4, pp. 441-450.
[20] Zabir, HK, Tasnim, SA & Md, AH 2011, ‘Price Prediction of Share Market using Artificial Neural Network’, International Journal of Computer Applications, vol. 22, no. 2, pp. 0975–8887.
[21] V.Srinivasan ‘A Fuzzy Approach to Replace the Missing Data in Large Dataset’, International Journal of Applied Engineering and Research, Vol.10, N0.38, May 2015, pp.28312-28317.
[22] M. Setnes, ‘Supervised Fuzzy Clustering for Rule Extraction,’ IEEE Trans. Fuzzy Systems, vol. 8, pp. 416-424, 2000.
[23] Sathyamoorthy. S, ‘Data Mining and Information Security in Big Data,’ International Journal of Scientific Research in Computer Science and Engineering, vol. 5, No.3, pp. 86-91, June 2017.
[24] Mantripatjit Kaur, Anjum Mohd Aslam, ‘Big Data Analytics on IOT: Challenges, Open Research Issues and Tools”, International Journal of Scientific Research in Computer Science and Engineering, vol. 6, No.3, pp. 81-85, June 2018.
[25] Manju Bhardwaj, ‘Faculty Link Detection in Cluster based Energy Efficient Wireless Sensor Networks”, International Journal of Scientific Research in Network Security and Communications, vol. 5, No.3, pp. 81-85, June 2017.
[26] Bhupendra Kumar Jain, Manish Tiwari, ‘Prediction Analysis Technique based on Clustering and Classification’, International Journal of Computer Sciences and Engineering, Vol.6, No.6, June 2018.
Citation
M. Aruna, S. Sugumaran, V. Srinivasan, "Precision Clustering Based on Boundary Region Analysis for Share Market Database," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.113-118, 2019.
Effective Governance through Big Data: Computerized Revolution of Open Agencies
Research Paper | Journal Paper
Vol.7 , Issue.4 , pp.119-127, Apr-2019
CrossRef-DOI: https://doi.org/10.26438/ijcse/v7i4.119127
Abstract
Big Data is field that get ways examine, efficiently extricate data from, or generally manage informational index that are excessively vast or complex. There are along discussion and exchange on the utilization of huge information for the change of customary open organization to current and keen open organization in the academician, scientists and strategy producers. Writing demonstrates that various models have been created to clarify shrewd administration nut deliberate research on the reasonableness and pertinence huge information for savvy administration of open offices is as yet missing for information security. The discoveries recommend that each open part office ought to be brought under shrewd administration which ought to be a completely advanced under huge information advances for simple access, straightforward and responsible ,and bother free open offices yet absence of security for the information. In proposed successful administration has a huge job in convenient ,mistake free, proper, and savvy administration conveyance to natives which prompts the reasonable monetary advancement of a nation and which ought to be a completely advanced under enormous information innovations for simple access ,straightforward and responsible and bother free open offices by utilizing MapReduce calculation. Essentially giving security of information utilizing a few calculations like encryption and decoding on information .In this proposed framework information will be encoded while putting away into the dataset. At the point when client looking through the, information will be encode and decode and show for client .For encryption and unscrambling we are utilizing ECC calculation. We are sending details to client by means of email.
Key-Words / Index Term
Hadoop, Big data, MapReduce, Security. Encryption, ECC
References
[1] Yueqian Xu, “E-governament and governance in china ,” in IEEE , 978-1-4244-5326-9,2010.
[2] R.D.Pathak,Gurmeet Singh,Rakesh Belwal,REL Smith, “Governance and corruption -Developments and Issues in Ethopia.” Springer , volume 7,2007.
[3]. Alfredo Cuzzocrea, “Privacy Preserving Big data Stream, mining: Opportunities, Challenges, Directions”.IEEE,2017.
[4]. Huai Jinmei, “Quality Evaluation of E-Government Public Service” IEEE 2011.
[5] Nuno vasco Lupes , “ Smart Governance : a key factor for Smart cities Implementation”, IEEE , 978-1-5386-0504-2/17,2017
[6] K. Schedler, A. A. Guenduez, and R. Frischknecht, “How smart canthe government be? – discussing the barriers to smart governmentadoption,” in IPMN Conference, 2017, pp. 1–17.
[7] S. Mellouli, L. F. Luna-Reyes, and J. Zhang, “Smart government,citizen participation and open data,” Inf. Polity, vol. 19, no. 1–2, pp.1–4, 2014.
[8] H. J. Scholl and M. C. Scholl, “Smart Governance: A Roadmap forResearch and Practice,” in iConference 2014 Proceedings, 2014, no.1.
[9] M. N. I. Sarker, Y. Bingxin, A. Sultana, and AZM S. Prodhan,“Problems and challenges of public administration in Bangladesh: apathway to sustainable development,” Int. J. Public Adm. Policy Res.,vol. 3, no. 1, pp. 16–25, 2017.
[10] E. Ogbuju, I. Aminu, and A. M. Peter, “Towards a Data-driven SmartGovernance in Nigeria,” IEEE,2016 .
[11] K. C. Desouza and B. Jacob, “Big Data in the Public Sector: Lessonsfor Practitioners and Scholars,” Adm. Soc., vol. 49, no. 7, pp. 1043–1064, 2017.
[12] J. Manyika et al., “Big data: The next frontier for innovation,competition, and productivity,” IEEE,2011.
[13] Shankar M.Patil,Praveen Kumar, “Data Mining Model for Effective Data Analysis of Higher Education Students Using MapReduce”,IJERMT,ISSN:2278-9359,Vol.6,Issue 4,April 2017.
[14] Madhavi Vaidya,"Parallel Processing of cluster by MapReduce",IJDPS,Vol.3,No.1,2012.
[15] Rakesh.S.Shirsath,Vaibhav A.Desale,Amol D.Potgantwar, “Big Data Analytical Architecture for Real-Time Applications” IJSRNSC, ISSN: 2321-3256 ,Volume-5, Issue-4, August 2017
[16] V. Kapoor , “A New Cryptography Algorithm with an Integrated Scheme to Improve Data Security” IJSRNSC, ISSN: 2321-3256 ,Volume-1, Issue-2, June- 2013.
[17] Anitya Kumar Gupta, Srishti Gupta, “Security Issues in Big Data with Cloud Computing”,IJSRCSE, E-ISSN: 2320-7639 Vol.5, Issue.6, pp.27-32, December -2017 .
[18] K. Parimala, G. Rajkumar, A. Ruba, S. Vijayalakshmi, “Challenges and Opportunities with Big Data” IJSRCSE, E-ISSN: 2320-7639 Vol.5, Issue.5, pp.16-20, October-2017.
Citation
Rashmi G.B, Malatesh S.H, "Effective Governance through Big Data: Computerized Revolution of Open Agencies," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.119-127, 2019.
A Survey on Medical Image Encryption
Survey Paper | Journal Paper
Vol.7 , Issue.4 , pp.128-133, Apr-2019
CrossRef-DOI: https://doi.org/10.26438/ijcse/v7i4.128133
Abstract
The medical industry has proceeded into the digital age thanks to the development of science and technology. Medicinal pictures which are utilized in their digital form play an important role in every modern hospital. This significant contribution is especially visible in the field of diagnosis and in the treatment of patients. The problem, however, may arise during the application of these digital data. Digital images are linked to image transmission and sharing, which bring about concerns regarding data security. The Digital Image and Communication on Medicine (DICOM) standard was created so as to encourage protected and reliable transmissions and communications. In this paper it has been surveyed about various encryption techniques that can be used for secure transmission of medical images through non-secure medium along with its advantages and disadvantages.
Key-Words / Index Term
Encryption, Medical images, DICOM, Key Sensitivity, Known Plaintext attack, Quaternion
References
[1]. Panigrahy S., Acharya B., Jen D. Image encryption using self-invertible key matrix of hill cipher algorithm. In: 1st International Conference on Advances in Computing, 21-22 February, Chikhli, India, . 2008; pp: 1-4.
[2]. Sokouti M., Pashazadeh S., Sokouti B. Medical image encryption using genetic-based random key generator (GRKG).; In: National Joint Conference on Computer and Mechanical Eng. (NJCCEM2013); May 8; Miandoab, Iran. 2013. pp. 1–6.
[3]. Sokouti M., Sokouti B., Pashazadeh S., Feizi-Derakhshi M-R., Haghipour S. Genetic-based random key generator (GRKG): a new method for generating more-random keys for one-time pad cryptosystem. Neural Comput. Appl. 2013;22(7-8):1667–1675. doi: 10.1007/s00521-011-0799-8.
[4]. Younes MAB, Janatan A. ”An image encryption approach using a combination of permutation technique followed by encryption”. IJCSNS. 2008;8(4):191–7.
[5]. Seyedzade S.M., Atani R.E., Mirzakuchaki S. Novel image encryption algorithm based on hash function.; 6th Iranian Conference on Machine Vision and Image Processing; October 27-28; 2010. pp. 1–6.
[6]. Ismail I.A., Amin M., Diab H. Digital image encryption algorithm based on composition of two chaotic logistic maps’2010. Int. J. Netw. Secur. 2010;11(1):1–5.
[7]. Kamali SH, Shankeria R, Hedayati M, Rahmani M. New modified version of advance encryption standard based algorithm for image encryption.; In: International Conference on Electronics and Information Engineering (ICEIE); August 1-3, 2010. pp. v1-141–v1-5.
[8]. Indrakanti S.P., Avadhani P.S. Permutation based image encryption technique. Int. J. Comput. Appl. 2011;28(8):45–47.
[9]. Enayatifkr R., Abdullah A.H. Image Security via genetic algorithm.; In: International Conference on Computer and Software Modeling IPCSIT;
[10]. Singh K., Kaur K. Image encryption using chaotic maps and DNA addition operation and noise effect on it. Int. J. Comput. Appl. 2011;23(6):17–24.
[11]. Alsafasfeh Q.H., Arfoa A.A. Image encryption based on the general approach for multiple chaotic system. J Signal Inform Process. 2011;2(3):238–244. doi: 10.4236/jsip.2011.23033.
[12]. Abuhaiba I.S., Hassan M.A. Image encryption using differential evolution approach in security Domain. Signal Image Process. Int. J. 2011;2(1):51–69. [SIPIJ].
[13]. Patel K.D., Belani S. Image encryption using different techniques: A review. Int. J. Emerg. Technol. Adv. Eng. 2011;1(1):30–4.
[14]. Abugharsa A.B., Basari A.S., Almangush H. A new image encryption approach using the integration of a shifting technique and the AES algorithm. Int. J. Comput. Appl. 2012;42(9):38–45.
[15]. Pareek N.K.“Design and Analysis of a novel digital image encryption scheme”. Int J Net secure Appl. 2012;4(2):95–108.doi: 10.5121/ijnsa.2012.4207.
[16]. Agarwal A. Secret key encryption algorithm using genetic algorithm. Int J Adv Res Comp Sci Soft Eng. 2012; 2(4): 216-8
[17]. Bhatt V. Implementation of new advance image encryption algorithm to enhance security of multimedia component. Int J Adv Technol Eng Res. 2012;2(4):13–20.
[18]. M. Dzwonkowski, M. Papaj, and R. Rykaczewski, “A new quaternion based encryption method for DICOM images,” IEEE Trans. Image Process., vol. 24, no. 11, pp. 4614–4622, Nov. 2015.
[19]. M. Dridi, M. A. Hajjaji, B. Bouallegue, and A. Mtibaa, “Cryptography of medical images based on a combination between chaotic and neural network,” IET Image Process., vol. 10, no. 11, pp. 830–839, 2016.
[20]. W. Cao, Y. Zhou, C. L. P. Chen, and L. Xia, “Medical image encryption using edge maps,” Signal Process., vol. 132, pp. 96–109, Mar. 2017.
[21]. J. Chandrasekaran and S. J. Thiruvengadam, “A hybrid chaotic and number theoretic approach for securing DICOM images,” Secur. Commun. Netw., vol. 2017, Jan. 2017, Art. no. 6729896, doi: 10.1155/2017/ 6729896.
[22]. M. Dzwonkowski and R. Rykaczewski, “Quaternion feistel cipher with an infinite key space based on quaternion Julia sets,” J. Telecommun. Inf. Technol., vol. 4, pp. 15–21, Dec. 2015.
Citation
Garima Mathur, "A Survey on Medical Image Encryption," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.128-133, 2019.
Network Intrusion Detection Using Genitic Algorithm: A Comparision
Research Paper | Journal Paper
Vol.7 , Issue.4 , pp.134-136, Apr-2019
CrossRef-DOI: https://doi.org/10.26438/ijcse/v7i4.134136
Abstract
The network intrusion detection system is used to detect and analyze the network traffic and all possible network threats that may affect the system. When the threats are identified the network intrusion detection system immediately takes action such as alerting the administrator or blocking the source of ip address from accessing the network. Various research activities are already conducted to find a efficient and effective solution to prevent intrusions in the network in order to ensure the network security and privacy .machine learning is the one of the efficient and effective techniques to detect network intrusion. Due to high traffic flow, the traditional signature based intrusion detection system is inefficient one to detect anomalies the machine learning techniques is the solution for this. In this paper a combination of two machine learning algorithm is proposed to classify any anomalous behavior in the network traffic. The overall efficiency of the proposed method is dignified recall. However using area under the Receiver operating curve (ROC) metric, we find that genetic algorithm is the best among the two algorithm proposed in this work.
Key-Words / Index Term
Intrusion detection,Genetic Algorithm, Rbf algorithm, Roc metrics calculation
References
[1] Syarif I, Prugel Bennett A, Wills G., “Unsupervised clustering approach for network anomaly detection”, Networked Digital Technologies Communications in Computer and Information Science, vol. 293. Berlin Heidelberg: Springer, 2012, pp.135–45.
[2] S. Novakov, C.-H. Lung, I. Lambadaris, Ioannis N. Seddigh, “Studies in applying PCA and wavelet algorithms for network traffic anomaly detection”, Proc. of IEEE 14th International Conference on High Performance Switching and Routing, 2013, pp. 185-190.
[3]Intrusion Detection using an Ensemble of Classification Methods, M.Govindarajan and RM.Chandrasekaran, Proceedings of the World Congress on Engineering and Computer Science 2012 Vol I WCECS 2012, October 24-26, 2012, San Francisco, USA
[4] S. Novakov, C.-H. Lung, I. Lambadaris, Ioannis N. Seddigh, “Combining statistical and spectral analysis techniques in network traffic anomaly detection”, Proc. of IEEE Conf. on Next Generation Networks and Services, 2012, pp. 94-101.
[5] S.A. Mulay, P. R. Devale, G.V. Garje, “Intrusion Detection System using Support Vector Machine and Decision Tree”, International Journal of Computer Applications, vol. 3, no. 3, 2010.
[6] J. Cannady, “Artificial neural networks for misuse detection,” in Proceedings of the 1998 National Information Systems Secu- rity Conference, pp. 443–456, Arlington, VA, USA, 1998.
[7] S. Pan, T. Morris, and U. Adhikari, “Developing a hybrid intrusion detection system using data mining for power sys- tems,” IEEE Transactions on Smart Grid, vol. 6, no. 6, pp. 3104–3113, 2015.
Citation
Sayi Sruthi.k, Liston Deva Glinds, Saran Raj, "Network Intrusion Detection Using Genitic Algorithm: A Comparision," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.134-136, 2019.
Dialect detection of Apatani language of Arunachal Pradesh
Research Paper | Journal Paper
Vol.7 , Issue.4 , pp.137-139, Apr-2019
CrossRef-DOI: https://doi.org/10.26438/ijcse/v7i4.137139
Abstract
the aim this paper to detect similar and dissimilar patterns of different Apatani dialects through extraction of relevant acoustic parameters such as pitch, formant analysis, Cepstral analysis. Considering all the linguistic and non linguistic features of the languages, it is expected that the study reveal some hidden characteristics of the languages which would strongly advocate its identity and uniqueness with respect to other tribal language of Arunachal Pradesh.
Key-Words / Index Term
Formant, Pitch, cepstral cofficients
References
[1] Mark W. Post and Tage Kanno “Apatani phonology and lexicon, with a special focus on tone” 2013 ISSN 1544-7502
[2] Mark W. Post and Tage Kanno, “Himalayan Linguistics”, Universität Bern and Future Generations
[3] Bruce Hayes Colin Wilson "A Maximum Entropy Model of Phonotactics and Phonotactic Learning" August 2007
[4] Thomas F. Quatieri, “ Discrete-Time Speech Signal Processing: Principles and Practice”, Pearson Education, 2002.
[5] M. J. Roberts, “Signal and Systems: Analysis Using Transform methods and MATLAB”, TATA McGraw-Hill, 2003
[6] Hamid Behravan , “Dialect and Accent Recognition”, University of Eastern Finland School of Computing December, 2012
[7] Hamid Behravan "Dialect and Accent Recognition"December, 2012
[8] Shashidhar G.Koolagudia, Deepika Rastogi, Sreenivasa Rao "Identification of Language using Mel-Frequency Cepstral Coefficients (MFCC)"
procedia engg 2012
[9] Popi Sarmin Society’s book series
Citation
Marpe Sora, Tage Yasing, "Dialect detection of Apatani language of Arunachal Pradesh," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.137-139, 2019.
Studies on Uniaxial Compressive Strength, Uniaxial Tensile Strength, Young’s Modulus, Poisson’s Ratio of Iron Ore in relation their Influence in Optimum Rock Blasting Process…….Central Afghanistan
Research Paper | Journal Paper
Vol.7 , Issue.4 , pp.140-151, Apr-2019
CrossRef-DOI: https://doi.org/10.26438/ijcse/v7i4.140151
Abstract
The aim of this study to find out some mechanical properties of iron ore in Haji Gak iron ore mine in order to design the optimal blasting parameters during mining activities. Totally 10 boulder surface samples of iron ore with more than 40kg weight were collected in 8 stations of different coordinates and altitudes limited between 3437.2 M till 3695 M above sea level. The Haji Gak iron mine reserve has estimated approximately 1.8 Billion tonnes iron ore with concentration of 67% of Fe and 96.15% of Fe2O3. The measurement of mechanical properties of iron ores were done with ASTM standard in high level technological and modern laboratory. The laboratory experiments reveal the uniaxial compressive strength value of collected ore sample 98.09 Mpa with elastic modules of 79.113 Mpa and 0.12 Poisson ratios in sample no. 18 shown the maximum amount of uniaxial compressive strength and Elastic modules. The point load test shown the maximum value of IS(50), 18.18 Mpa and 22.73 Mpa of uniaxial tensile strength in sample no.3. These parameters directly affected the blasting parameters, blasting pattern and explosive type in optimal mining activities. As this mine is not extracted yet, thus it is the first time to design the blasting pattern and parameters base on measured properties of rocks.
Key-Words / Index Term
Uniaxial compressive strength, Point load strength, Young’s modulus, Piosson’s Ratio, Blasting Pattern
References
[1] A.S. Sahak, “Underground Extraction Technology of Metallic and Non Metallic Mines”, Ministry of higher education Publisher, Kabul Polytechnic University. Kabul. pp. 37-64, 2011.
[2] P.K. Singh, M.P. Roy, R.K. Paswan, Md. Sarim, Suraj. Kumar, Ranjan. Rakesh. Jha,” Rock fragmentation control in opencast blasting”, Journal of rock Mechanics and Geochemical Engineering, Vol. 8, Issue. 2, pp. 225-237, November 2016.https://doi.org/10.1016/j.jrmge.2015.10.005.
[3] S.P. Singh, R. Narendrula, D. Duffy, “Influence of blasted muck on the productivity of the loading equipment”, Proceedings of the 3rd EFEE Conference on Explosives and Blasting. India pp. 347-353, 2005.
[4] K. Žarko, Č. Miodrag, T. Dražena,” Calculation of Drilling and Blasting Parameters for Quarry, Dobrnja, near Banja luka”, Professional paper UDC: 622.332 (497.6 Banja Luka). November 2013, Doi: 10.7251/afts.2013.0509.035K.
[5] S. Bhandari,” Engineering Rock Blasting Operation”, Publisher - A.A. Balkema, Rotterdam, Netherland, ISBN- 905410658 1. pp. 135-137. 375, 1997.
[6] B. Adebayo, E.C. Umeh, “Influence of Some Rock Properties on Blasting Performance – A case study”, Journal of Engineering and Applied Sciences. pp. 41-44, 2007.
[7] H. Alehossein, J. N. Boland,“ Strength, Toughness, Damage and Fatigue of rock”, Conference of Structural Integrity and Fracture in Brisbane, Australia, ISSN/ISBN: 1864997605, p.7, 2004. http://eprint.uq.edu.au/archive/00000836. Identifier: procite: d599ebdc-9263-4f3c-ba4b-8edf895ca0b7.
[8] J.M.F. Clout, J.R. Manuel,” Iron Ore; Mineralogy, Processing and Environmental Sustainability”, Science direct publisher, pp. 45-84. 2015. https://doi.org/10.1016/B978-1-78242-156-6.00002-2.
[9] H. A. Malistani, “Geology and Genesis of Haji Gak Iron Ore Deposit, Bamyan, Central Afghanistan”, PhDs Dissertation, Bonn, p.189, 2016.
[10] “Minerals in Afghanistan”, Afghanistan Geological Survey (AGS) and British Geological Survey (BGS) project, 2008, https://www.bgs.ac.uk/downloads/start.cfm-id=3208.
[11] I.K. Kusov, M.S. Smirnov, V.V. Reshetnyak,” Report on the Results of Geological Prospecting, Survey and Exploration of Iron Deposits and Occurrences in Central Afghanistan with the data on iron ore reserves estimation of the Haji-Gak deposit carried out in 1963–1964: Kabul, Afghanistan”, Afghanistan Geological and Mineral Survey USSR v/o Techno export contract no. 640, Vol. 3, 1965 b.
[12] Fatima. Rezaye, Shivanna,” Studies On Chemical and Mineralogical Composition of Iron Ore In Related the Economic Potential of Mining Activities in Haji Gak Iron Ore Mine, Bamyan Province, Central Afghanistan”, International Journal of Research and Analytical Reviews (IJRAR), E-ISSN 2348 – 1269, P-ISSN 2349-5138, Vol.5, Issue. 4, pp. 591- 603, October 2018. http://www.ijrar.org/IJRAR1904196.pdf. Doi.one/10.1729/Journal.18575.
[13] H.A. Malistani,“ Express Report on Haji gak Iron Ore Deposit in Central Iron Belt of Afghanistan”, Department of geology. Bamyan University, p. 8, 2011.
https://www.researchgate.net/publication/236972843.
[14] S. Peng, J. Zhang, “Engineering geology for underground rocks”, Springer – Verlag. Berlin Heidelberg, p. 10-125, 319, 2007. ISBN 978-3-540-73294-5
[15] M. Alitalesh, M. Yazdani, M. Molaali, “Correlation between uniaxial strength and point load index of rocks”, Conference Paper, the 15th Asia Conference on Soil Mechanic and Geotechnical Engineering, 2016. Doi: 10.3208/jgssp.IRN-08.
[16] J.C. Jaeger, N.G.W. Cook, W. Zimmerman,” Fundamentals of Rock Mechanics,” 4th edition, Blackwell Publishing, pp. 81. 474, 2007. ISBN-13: 978-0-632-05759-7.
[17] J. A. Hudson, J.P. Harrison, “Engineering rock mechanics, an introduction to the principles”, First edition, Pergamon, Publisher Elsevier Science Ltd, pp. 70- 71. 444, 1997. ISBN: ISBN: 0 08 04 19 12 7
[18] M.R. Kiyoumarsi, S. Javadi, “Mining Engineering book”, Jihad e Danish Publisher, Tehran. pp. 57-128, 2006.
ISBN: 978-964-8737-39-9.
[19] Z.T. Bieniawski,” The point load test in geotechnical practice”, Engineering Geology, Elsevier Publisher, Vol. 9, Issue.1, pp. 1-11, 1975. doi: 10.1016/0013-7952(75)90024-1
[20] Anon, 1980,” The effect of rock properties on blasting”, Part Ι and ΙΙ, Noble Notes, 26.
[21] K. Sassa, I. Ito, “On the relation the strength of a rock and the Pattern of breakage by blasting. Proc,” 3rd Int. Conference, Rock mechanic, Denver, Vol. ΙΙ-B, pp. 1501-1505, 1974.
[22] Gang. Han, Maurice. Dusseault, B. Emmanuel. Detournay, Bradley. J. Thomson, Kris. Zacny,” Drilling in Extreme Environments; Chapter 2- Principles of Drilling and Excavation”, Publisher - WILEY –VCH. Germany, pp. 31-129. 755, 2009. ISBN- 978-3-527-40852-8. https://www.bu.edu/remotesensing/files/2015/09/Han_Extraterrestrial_Drilling_Ch2_2009.pdf
[23] А. S. Tanaino, “Rock Drill ability Classification. Part II: Canonical Representation of Rock Properties in the Rock Classification by Fracture Resistance”, Journal of Mining Science. Vol. 44, No. 6, pp. 600-601, 2008.
[24] A.H. Wahidi, “Mining Machineries. Ministry of higher education publisher”, Kabul Polytechnic University, Kabul, pp.10. 435, 2018.
[25] L. S. Burshtein, “Effect of Moisture on the Strength and Deformability of Sandstone” Soviet Mining Science, Vol.5, Issue. 5, pp. 573-576, 1969. Doi: 10.1007/bf02501278.
[26] A.B. Hawkins, B.J. Mc Connnell, “Sensitivity of Sandstone Strength and Deformability to Changes in Moisture Content”, Quarterly Journal of Engineering Geology. 25. pp. 115-130, 1992.
[27] S. D. Priest, S. Selvakumar, “The failure characteristics of selected British rocks. Report to the transport and road research laboratory”, Imperial College, London, 1982.
[28] M. Mellor, “Strength and Deformability of Rocks at Low Temperatures”, Project No- DA Task 1T062112A13001, Hanover, New Hampshire 03755, Research Report 294, pp. 1-55. 73, 1971.
[29] E. Papamichos, M. Brignoli, F. J. Santarelli, “An Experimental and Theoretical Study of a Partially Saturated Collapsible Rock. Mechanics of Cohesive-Frictional Material”, Vol. 2, pp.251-278, 1997.
[30] Fatima. Rezaye, Shivanna, “Studies on Physical and Mechanical Properties of Iron Ore in Relation to Blasting Process in Mining Activities of Haji Gak Iron Ore Mine, Bamyan Province, Central Afghanistan”, International Journal of Research and Analytical Reviews (IJRAR), Vol. 6, Issue. 1, pp.5-19, Mar 2019.
E-ISSN 2348-1269. P- ISSN 2349-5138,http://www.ijrar.org/IJRAR19J3149.pdf.
[31] R. Gholami, N. Fakhari,” Handbook of Neural Computation; Chapter 26, Vector Machine: Principles, Parameters, and Applications”, Academic Press. pp. 515-535, 2017.
ISBN: 978-0-12-811318-9. Doi.org/10.1016/C2016-0-01217-2.
Citation
Fatima Rezaye, Shivanna, "Studies on Uniaxial Compressive Strength, Uniaxial Tensile Strength, Young’s Modulus, Poisson’s Ratio of Iron Ore in relation their Influence in Optimum Rock Blasting Process…….Central Afghanistan," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.140-151, 2019.
A Privacy Preserving Anonymization Approach using Scalable Top Down Specialization and Randomization for Big Data Security
Research Paper | Journal Paper
Vol.7 , Issue.4 , pp.152-156, Apr-2019
CrossRef-DOI: https://doi.org/10.26438/ijcse/v7i4.152156
Abstract
There are different analysis centers all around the world. These centers makes inferences that are beneficial to the society. From where did they get the data for doing such kind of analysis?. Is the data that is published for such kind of analysis is secure from privacy breach?. In America, different kind of records especially the medical records which contains disease information as a sensitive attribute are publishing publicly. This has to be taken as a serious issue. Taking into account these facts, different anonymization methods are developed. In this paper an approach that is different from all other approaches is proposed. The main concern is to manage background knowledge attack that most of the algorithms failed to heed. Although no algorithm is able to achieve 100 percent security without sacrificing information loss, a balance can be maintained between these two. This paper introduces an efficient method for handling the outlier tuples using a randomization algorithm.
Key-Words / Index Term
Background knowledge attack, IGPL metric , Quasi- identifier, K- anonymity, Randomization, Taxonomy tree, Top down specialization
References
[1] L. Xu, C. Jiang, J. Wang, J. Yuan, and Y. Ren, “Information security in big data: Privacy and data mining”, pp. 1149-1176, 2014.
[2] B.C.M. Fung, K.Wang, R. Chen and P. S. Yu, “Privacy preserving data publishing: A survey of recent developments”, ACM Computing surveys, pp. 1-53, 2010.
[3] Xuyun Zhang, Laurence T.Yang, Chang Liu, and Jinjun Chen, “A scalable top down specialization approach for data anonymization using Map Reduce on cloud”, IEEE Transactions on parallel and distributed systems, pp. 363-373, 2014.
[4] Benny Pinkas ,“Cryptographic techniques for privacy preserving data”, SIGKDD Explorations, pp. 12-19.
[5] L. Sweeney,“K-anonymity, A model for protecting privacy”,International Journal on Uncertainty, Fuzziness and Knowledge-based Systems, pp. 557-570, 2012.
[6] Ashwin Machanavajjhala, Johannes Gehrke, Daniel Kifer, “l- Diversity: Privacy beyond k- anonymity”, 2005.
[7] Ahmed Ali Mubark, Hatem Abdulkader, “Semantic anonymization in publishing categorical sensitive attributes”, IEEE International. Conference, pp. 89-95, 2016.
[8] B.C.M. Fung, K. Wang, and P. S. Yu, “Anonymizing classification data for privacy preservation”, IEEE Transactions Knowledge and Data Engineering, pp. 711-725, 2007.
[9] Savita Lohiya, LataRagha, “Privacy preserving in data mining using hybrid approach”, IEEE International Conference on Computational Intelligence and Communication Networks, pp. 743-746, 2012.
Citation
Athiramol S, "A Privacy Preserving Anonymization Approach using Scalable Top Down Specialization and Randomization for Big Data Security," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.152-156, 2019.
Machine Learning DDoS Detection Using Stochastic Gradient Boosting
Research Paper | Journal Paper
Vol.7 , Issue.4 , pp.157-166, Apr-2019
CrossRef-DOI: https://doi.org/10.26438/ijcse/v7i4.157166
Abstract
DDoS (Distributed Denial of service) attacks emerge as the most devastating attacks of all time for organizations and ISPs of all sizes. The increasing availability of DDoS-for-hire services and the proliferation of billions of Unsecured IoT devices and botnets contributed to a significant increase in DDoS attacks. These attacks continue to grow in magnitude, frequency, and sophistication. The legacy methods like signature-based detection and scrubbing are challenged, as attacks are growing smarter day by day and evading IDS. The next-generation security technologies also cannot keep pace with the scale of attacks targeting organizations. Even anomaly-based detection is suffering from many limitations with accuracy and false positives by demanding human intervention. This is our attempt to obviate manual analysis in anomaly-based DDoS detection by achieving perfect accuracy with zero misclassifications. In this paper, we demonstrated DDoS anomaly detection on the open CIC datasets using Stochastic Gradient Boosting (SGB) machine learning (ML) model. Using this ML model and by meticulously tuning hyperparameters, we achieved maximum accuracy and compared the results with other machine learning algorithms.
Key-Words / Index Term
DDOS attacks, anomaly detection, machine learning, stochastic gradient boosting, scikit-learn, XGBOOST
References
[1].M. Antonakakis, T. April, M. Bailey, M. Bernhard, E. Bursztein, J. Cochran, Z. Durumeric, J. A. Halderman, L. Invernizzi, M. Kallitsis, D. Kumar, C. Lever, Z. Ma, J. Mason, D. Menscher, C. Seaman, N. Sullivan, K. Thomas, and Y. Zhou, "Understanding the mirai botnet," in Proc. of USENIX Security Symposium, 2017.
[2].Iman Sharafaldin, Arash Habibi Lashkari, and Ali A. Ghorbani, “Toward Generating a New Intrusion Detection Dataset and Intrusion Traffic Characterization”, 4th International Conference on Information Systems Security and Privacy (ICISSP), Portugal, January 2018
[3].Hossein Hadian Jazi, Hugo Gonzalez, Natalia Stakhanova, and Ali A. Ghorbani. "Detecting HTTP-based Application Layer DoS attacks on Web Servers in the presence of sampling." Computer Networks, 2017
[4]. A. Shiravi, H. Shiravi, M. Tavallaee, A.A. Ghorbani, Toward developing a systematic approach to generate benchmark datasets for intrusion detection, Comput.
Security 31 (3) (2012) 357–374.
[5].Z. He, T. Zhang, and R. B. Lee, “Machine Learning Based DDoS Attack Detection from Source Side in Cloud,” in Proceedings of the 2017 IEEE 4th International Conference on Cyber Security and Cloud Computing (CSCloud), pp. 114–120, New York, NY, USA, June 2017
[6].R. Doshi, N. Apthorpe and N. Feamster, "Machine Learning DDoS Detection for Consumer Internet of Things Devices," 2018 IEEE Security and Privacy Workshops (SPW), San Francisco, CA, 2018, pp. 29-35.
[7].Jerome H. Friedman, (2002), Stochastic gradient boosting, Computational Statistics & Data Analysis, 38, (4), 367-378
[8].Friedman, Jerome H. Greedy function approximation: A gradient boosting machine. Ann. Statist. 29 (2001), no. 5, 1189--1232.
Citation
M Devendra Prasad, Prasanta Babu V, C Amarnath, "Machine Learning DDoS Detection Using Stochastic Gradient Boosting," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.157-166, 2019.
Improvement in Security Architecture for Hybrid Networks in Wireless and Wired Devices for End to End Security
Survey Paper | Journal Paper
Vol.7 , Issue.4 , pp.167-173, Apr-2019
CrossRef-DOI: https://doi.org/10.26438/ijcse/v7i4.167173
Abstract
Security for wireless devices is turning into vital day by day. When internet connection is available for mobile devices all the communication pass through some intermediates. The end to end security is the major issues in wireless security contrivances like mobile phone and PDA(Personal Digital Assistant).When mobile contrivance wants to communicate to the web server through internet the all the communication pass through the Wireless Application Protocol gateway. This Wireless Application gateway protocols interprets all the convention utilized in Wireless Application gateway to the protocols utilized in the cyber world. The Wireless Application Protocol proxy server use encoding and decoding technique for the content to reduce the size of the data that has been sent through the wireless link. The communication between the mobile phones and wireless application protocol is secured by utilizing the safety protocol is termed Wireless Transport layer security. The communication between the WAP entrance and net server is secured through the TLS/SSL security protocols. This paper presents an evaluation study of wireless and wired network utilizing OPNET simulation implement. This paper simulated 2 different scenarios comparing wireless mobile client communication utilizing Wireless Transport Layer Security gateway with MD5_RSA encryption and Firewall gateway TLS encryption utilizing MD5_RSA.The analysis results shows that how to provide the point to point security between wireless client to web server by proper utilizing the hybrid security protocol.
Key-Words / Index Term
Opnet, Security, WAP, Gateway
References
[1] Rehunathan D, Bhatti S. Application of virtual mobile networking to real-time patient monitoring. InTelecommunication Networks and Applications Conference (ATNAC), 2010 Australasian 2010 Oct 31 (pp. 124-129). IEEE
[2] Gustafsson E, Jonsson A. Always best connected. Wireless Communications, IEEE. 2003 Feb;10(1):49-55.
[3] Tanenbaum A.S. "Computer Networks," Prentice Hall India (PHI), November 1998.
[4] Tuladhar SR, Caicedo CE, Josh JB. Inter-domain authentication for seamless roaming in heterogeneous wireless networks. InSensor Networks, Ubiquitous and Trustworthy Computing, 2008. SUTC`08. IEEE International Conference on 2008 Jun 11 (pp. 249-255). IEEE.
[5] Tuladhar SR, Caicedo CE, Josh JB. Inter-domain authentication for seamless roaming in heterogeneous wireless networks. InSensor Networks, Ubiquitous and Trustworthy Computing, 2008. SUTC`08. IEEE International Conference on 2008 Jun 11 (pp. 249-255). IEEE..
[6] IEEE Computer Society LAN MAN Standards Committee. Wireless LAN medium access control (MAC) and physical layer (PHY) specifications..
[7] FON. (2012). Fon Passes 7 Million Hotspots. Available: www.fon.com,Access date: 22/02/2013.
[8] WAP Forum, Wireless Application Protocol Architecture Specification, WAP-210-WAPArch-200100712-a, 12-July- 2001 version, latest version is available at http://www.wapforum.com
[9] Inwhee Joe; Jaehyung Lee, "An Enhanced TCP Protocol for Wired/Wireless Networks," in INC, IMS and IDC, 2009. NCM `09. Fifth International Joint Conference on , vol., no., pp.531-533, 25-27 Aug. 2009
[10] Filho, T.A.S.; da Silva, A.C.R.; Grout, I.A.; Rossi, S.R., "Network node with wireless and wired interfaces: Nios II processor and uClinux to development of a NCAP embedded (IEEE 1451.1) with two interfaces, wireless (IEEE 1451.5) and wired (IEEE p1451.2)," in Instrumentation and Measurement Technology Conference (I2MTC), 2011 IEEE , vol., no., pp.1-6, 10-12 May 2011
[11] Fuertes JA, Philipp M, Baccelli E. Routing across wired and wireless mesh networks: Experimental compound internetworking with OSPF. InWireless Communications and Mobile Computing Conference (IWCMC), 2012 8th International 2012 Aug 27 (pp. 739-745). IEEE.
[12] Dellutri, F.; Gianluigi Me; Strangio, M.A., "Local Authentication with Bluetooth enabled Mobile Devices," in Autonomic and Autonomous Systems and International Conference on Networking and Services, 2005. ICAS-ICNS 2005. Joint International Conference on , vol., no., pp.72-72, 23-28 Oct. 2005
[13] Karthikeyan, Sindhu, and Mikhail Nesterenko. "RFID security without extensive cryptography." Proceedings of the 3rd ACM workshop on Security of ad hoc and sensor networks. ACM, 2005
[14] Diffie Hellman Key Exchange –A Non –Mathematician’s Explanation. Global Knowledge-Expert reference series of white papers. Link available at http://www.recursosvoip.com/docs/english/WP_Palmgren_DH.pdf
[15] Complete WAP Security from Certicom pages 5-12
[16] Csernai, M`rton; Gulyas, A., "Wireless Adapter Sleep Scheduling Based on Video QoE: How to Improve Battery Life When Watching Streaming Video?," in Computer Communications and Networks (ICCCN), 2011 Proceedings of 20th International Conference on , vol., no., pp.1-6, July 31 2011-Aug. 4 2011
[17] Shie-Yuan Wang; Chin-Liang Chou, "The Effects of Using Roadside Wireless Repeaters on Extending Path Lifetime in Vehicle-Formed Mobile Ad Hoc Networks on Highways," in Systems, Man and Cybernetics, 2006. SMC `06. IEEE International Conference on , vol.3, no., pp.2069-2074, 8-11 Oct. 2006
[18] Rikure, Tatiana, and Alexey Jurenoks. "WIRELESS NETWORK TECHNOLOGIES IN TRANSPORT AREA: SECURITY AND E-LEARNING APPLICATIONS." Wireless technologies, security, wireless enabled teaching, application, IEEE 802 (2005).
[19] Arbaugh, W.A.; Shankar, N.; Wan, Y.C.J.; Kan Zhang, "Your 80211 wireless network has no clothes," in Wireless Communications, IEEE , vol.9, no.6, pp.44-51, Dec. 2002
[20] Gupta, Er Anuj K., B. Lonia, and Er Vikas Gupta. "WIRELESS TECHNOLOGIES–AN OVERVIEW."
Citation
Kamini, Rajiv Mahajan, Ravinder Singh, "Improvement in Security Architecture for Hybrid Networks in Wireless and Wired Devices for End to End Security," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.167-173, 2019.
Trust Computation in Online Social Networks
Review Paper | Journal Paper
Vol.7 , Issue.4 , pp.174-178, Apr-2019
CrossRef-DOI: https://doi.org/10.26438/ijcse/v7i4.174178
Abstract
online Social Network (OSN) is a network, where an individual can communicate with each other through their mobile phones or any social networking sites such as Facebook, twitter etc. The trust is useful in the online social network to enable the users to exchange their information securely. In OSN, the information might be misused by propagating wrong news virally through photos, videos, and audios. A user in social network may collect personal information of others and can launch different attacks. Hence it could draw the researchers focus in proposing trust related methods to provide secure environment in social network. In trust management, each user is observed and rated based on his activities. So that only trustworthy users sare only allowed to share to the activities. In this paper, the trust is defined in terms of social network terminology. Different trust computational methods are discussed in social network. All the recent methods are compared by studying their advantages and disadvantages.
Key-Words / Index Term
Social Networks
References
[1] Yefeng Ruany, “A Survey of Trust Management Systems for Online Social Communities –Trust Modelling, Trust Inference and Attacks”: Department of computer & information science ,In USA(2016).
[2] Wang Yuji, “ The Trust Value Calculating for Social Network Based on Machine Learning”: Conference on Intelligent Human-Machine Systems and Cybernetics ,USA (2017).
[3] Ying He, CHENGCHAO Liang, F. Richard Yu, and Zhu Han: “Trust-based Social Networks with Computing, Caching and Communications: A Deep Reinforcement Learning Approach” IEEE transactions on Network Science and Engineering 2018.
[4] Wang Yuji ,“A Trust Prediction Method for Recommendation System”, Conference on Human-Machine Systems and Cybernetics, USA(2017).
[5] Kang Zhao, and Li Pan : “A Machine Learning Based Trust Evaluation Framework for Online Social Networks” IEEE 13th International Conference on Trust, Security and Privacy in Computing and Communications 2014.
[6] NageswaraRaoSirisala and C.ShobaBindu , “A Novel Q o S Trust Computation in MANETs Using Fuzzy Petri Nets”, International Journal of Intelligent Engineering and Systems, Vol.10, No.2, (2017), pp 116-125.
[7] NageswaraRaoSirisala and C.Shoba Bindu. Uncertain Rule Based Fuzzy Logic QoS Trust Model in MANETs, International Conference on Advanced Computing and Communications -ADCOM, (IITM PhD forum), (2015), pp.55-60.1
[8] Pasquale De MEO, Emilio Ferrara, “Trust and Compactness in Social Network Groups” IEEE TRANSACTIONS ON CYBERNETICS 2015.
[9]SHUIGUANG DENG, “On Deep Learning for Trust-Aware Recommendations in Social Networks”, IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2017.
[10] YADONG ZHOU1 , DAE WOOK KIM2, JUNJIE ZHANG2,“ Pro Guard: Detecting Malicious Accounts in Social-Network-Based Online Promotions”, ON TRUST MANAGEMENT IN PERVASIVE SOCIAL NETWORKING,2017 .
[11] JIAN SHEN, “Hierarchical Trust Level Evaluation for Pervasive Social Networking”, in 2017.
Citation
C.Kavitha, Nageswararao Sirisala, "Trust Computation in Online Social Networks," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.174-178, 2019.