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Financial forecasting is an estimation of future financial outcomes for a company, industry, country using historical internal accounting and sales data. We may predict the future outcome of BSE_SENSEX practically by some soft computing techniques and can also optimized using PSO (Particle Swarm Optimization), EA (Evolutionary Algorithm) or DEA (Differential Evolutionary Algorithm) etc. PSO is a biologically inspired computational search & optimization method developed in 1995 by Dr. Eberhart and Dr. Kennedy based on the social behaviors of fish schooling or birds flocking. PSO is a promising method to train Artificial Neural Network (ANN). It is easy to implement then Genetic Algorithm except few parameters are adjusted. PSO is a random & pattern search technique based on populating of particle. In PSO, the particles are having some position and velocity in the search space. Two terms are used in PSO one is Local Best and another one is Global Best. To optimize problems that are like Irregular, Noisy, Change over time, Static etc. PSO uses a classic optimization method such as Gradient Decent & Quasi-Newton Methods. The observation and review of few related studies in the last few years, focusing on function of PSO, modification of PSO and operation that have implemented using PSO like function optimization, ANN Training & Fuzzy Control etc. Differential Evolution is an efficient EA technique for optimization of numerical problems, financial problems etc. PSO technique is introduced due to the swarming behavior of animals which is the collective behavior of similar size that aggregates together.

Financial Forecasting, Neural Network, Particle Swarm Optimization, Global Best, Particle Best

Fakhreddine o. Karray and Clarence de silva, “Soft computing and intelligent systems design”, Pearson Education Limited, First Edition, ISBN: 0321116178, 2004.
[2] A.Andreas, W.Sheng “Introduction optimization, Particle Optimization Algorithm and Engineering Applications,” Springer, Page No (1-4), 2007
[3] Martha Pulido, Patricia Melin, Oscar Castillo “Particle Swarm Optimization of ensemble Neural Networks for time series prediction”, Elsevier, Page No (188-204), 2014

[4] J. Kennedy, R. Eberhart, “Particle swam optimization,”, Proc. Int. Conf. Neural Network (ICNN), IEEE, 1995.
[5] Swagatam Das, Ajith Abraham, and Amit Konar “Particle Swarm Optimization and Differential Evolution Algorithms: Technical Analysis, Applications and Hybridization Perspectives”, Springer Berlin Heidelberg, Page No (1-38), 2008

[6] Kennedy J and Eberhart R , “Particle Swarm Optimization”, In Proceedings of IEEE International Conference on Neural Networks, Future Generation Computer Systems, Page No (1942-1948), 1995

[7] Biswas, A., Lakra, A. V., Kumar, S., & Singh, A. “An Improved Random Inertia Weighted Particle Swarm Optimization”, In Computational and Business Intelligence (ISCBI), International Symposium on IEEE, Page No (96-99), 2013.

[8] G Pereira, “Particle Swarm Optimization?”, INESCID and Institute Superior Techno, Porto Salvo, Portugal, April 15, 2011.
[9] Zhao, Liang, and Yupu Yang. ”PSO-based single multiplicative neuron model for time series prediction.”, Expert Systems with Applications Page No (2805-2812), 2009.
[10] Kolarik, Thomas, and Gottfried Rudorfer.,”Time series forecasting using neural networks.”, ACM SIGAPL APL Quote Quad , Volume-.25. Issue-01, Page No (86-94), 1994.
[11] BSE SENSEX Dataset, www.bseindia.com, 27th September 2014.
[12] Labeling Kaastra and Milton Boyd, “Designing a Neural Network for forecasting financial and economic time series”, Elsevier, 1995.

S.Gopal Krishna Patro, Pragyan Parimita Sahoo, Ipsita Panda, Kishore Kumar Sahu, "Technical Analysis on Financial Forecasting," International Journal of Computer Sciences and Engineering, Vol.3, Issue.1, pp.1-6, 2015.

Static Testing Techniques are widely used throughout SDLC and proved very effective increasing effectiveness of testing by uncovering and preventing defects at early stage. Static Testing techniques for functional aspects of software are well defined and matured compared to static testing techniques for Non functional aspects of software. In this project, we designed Non Functional Static Testing Framework with details such as processes, roles, test scenarios, implementation guidelines. Framework helps to use correct NF static technique at correct place (which technique to use, where to use it in SDLC, how to use). We strongly believe that NF static testing framework is a novel idea and fulfill several goals in increasingly complex non-functional domain, thereby reducing business risks at optimal cost and schedule.

SDLC, Static Test Cases, 3 tier Architecture

[1] Quadri, S.M.K and Farooq,“3W’s of Static Software Testing Techniques”, Global Journal of Computer Science and Technology Volume 11, Issue 5, Version 1.0, April 2011.
[2] Quadri, S.M.K and Farooq, SU, “Software Testing – Goals, Principles, and Limitations”, International Journal of Computer Applications (0975 – 8887) Volume 6– No.9, September 2010.
[3] Vipin Saxena and Santosh Kumar, “Performance Computation Metrics for Object-Oriented Software Systems”, International Journal of Advanced Research in Computer Science, Volume 3, No. 6, Nov. 2012 (Special Issue)
[4] Kamna Gauri and Dipanwita Thakur, “Comparative Performance Evaluation Of Software Architectural Styles With UML”, International Journal of Advanced Research in Computer Science, Volume 3, No. 1, Jan-Feb 2012
[5] Shyam S. Pandeya and Anil K. Tripathi, “Techniques for Developing Testable Component Based Software: Similarities, Differences and Classification”, International Journal of Advanced Research in Computer Science, Volume 2, No. 2, May-June 2010.
[6] James H. Hill, Hamilton A. Turner, James R. Edmondson, and Douglas C. Schmidt, “Unit Testing Non-functional Concerns of Component-based Distributed Systems”, International Conference on Software Testing Verification and Validation April 2009.
[7] Myers, Glenford J., The art of software testing, Publication info: New York: Wiley, c1979. ISBN:0471043281
[8] Hetzel, William C. The Complete Guide to Software Testing, 2nd ed. Publication info: Wellesley, Mass.: QED Information Sciences, 1988. ISBN: 0894352423
[9] Roper, M., “Software Testing”, McGraw-Hill, 1994, 149 pages, ISBN 0-07-707466-1
[10] Somerville, I, “Software Engineering”, Pearson, 2008, 864 pages, ISBN 978-81-317-2461-3
[11] G. Denaro, A. Polini, and W. Emmerich. Early Performance Testing of Distributed Software Applications. ACM SIGSOFT Software Engineering Notes, 29(1):94–103, January 2004
[12] Review process, http://www.testresources.info/iseb-software-testing-reviewprocess.php
[13] IEEE, "IEEE Standard 610.12-1990, IEEE Standard Glossary of Software Engineering Terminology," 1990
[14] Prof. Dr. Cremers, Sascha Alda, “Organizational Requirements Engineering, Chapter 9, Non-functional Requirements”
[15] Dan Bergh Johnson, “ Non-functional requirements- how to get them in shape”, Colorado Software Summit, October 2006.

Priyanka R. Bhuyar and A.D.Gawande, "Analysis using Non-Functional Static Testing Framework," International Journal of Computer Sciences and Engineering, Vol.3, Issue.1, pp.7-14, 2015.

The main objective of image enhancement is to process the input image so that the processed image will look much better. Image enhancement refers to accentuation, or sharpening of image features such as edges, boundaries, or contrast to make a graphic display more useful for display and analysis. It plays a vital role in every field. This paper covers two parts: first part is based to the study of various types of images and image enhancement and second part is based on analysis of various basic intensity transformation functions in image processing. All these analysis is performed in MATLAB software. We get various results in term of output images and their histograms respectively. Here we see that each transformation function is suitable for some particular applications, not for all. So, According to application requirement, we choose a particular transformation function for image enhancement.

Image processing, Image enhancement, Matlab, Hue Transformation, Log transformation, Power Law Transformation

[1] Toet, A. (1992), “Multiscale color image enhancement,” Pattern Recognit. Lett., vol. 13, pp. 167–174.
[2] Trahanias, P. E. and Venetsanopoulos, A. N. (1992), “Color image enhancement through 3-D histogram equalization,” in Proc. 15th IAPR Int. Conf. Pattern Recognition, vol. 1, pp. 545–548.
[3] Mlsna, P. A. and Rodriguez, J. J. (1995), “A multivariate contrast enhancement technique for multispectral images,” IEEE Trans. Geosci. Remote Sensing, vol. 33, no. 1, pp. 212–216.
[4] Yang, C. C. and Rodriguez, J. J. (1995), “Efficient luminance and saturation processing techniques for bypassing color coordinate transformations,” in Proc. IEEE Int. Conf. on Systems, Man, and Cybernetics, vol. 1, pp. 667–672.
[5] Mlsna, P. A., Zhang, Q., and Rodriguez, J. J. (1996), “3-D histogram modification of color images,” Proc. IEEE Intl. Conf. Image Processing, vol. III, pp. 1015–1018.
[6] Zhang, Q., Mlsna, P. A., and Rodriguez, J. J. (1996), “A recursive technique for 3-D histogram enhancement of color images,” Proc. IEEE Southwest Symp. On Image Analysis and Interpretation, pp. 218–223.
[7] Thomas, B. A., Strickland, R. N., and Rodriguez, J. J. (1997), “Color image enhancement using spatially adaptive saturation feedback,” in Proc. IEEE Int. Conf. on Image Processing.
[8] Berns, R. S., Billmeyer, F. W., and Saltzman, M. (2000), “Billmeyer and Saltzman’s Principles of Color Technology”, 3rd ed. New York: Wiley.
[9] Yang, C. C. and Kwok, S. H. (2000), “Gamut Clipping in Color Image Processing”, IEEE, pp. 824-827.
[10] Naik and Murthy, C. A. (2003), “Hue-Preserving Color Image Enhancement Without Gamut Problem” in THE IEEE transactions on image processing, vol. 12, no. 12, december pp.1591-159.
[11] Chen, Soong-Der, and Rahman (2004), “Preserving brightness in histogram equalization based contrast enhancement techniques,” Digital Signal Processing, Vol 12, No.5, pp.413-428.
[12] Sun, C. C. , Ruan, S. J., Shie, M. C., Pai, T. W. (2005), “Dynamic Contrast Enhancement based on Histogram Specification,” IEEE Transactions on Consumer Electronics, Vol. 51,No.4, pp.1300-1305.
[13] Murtaza, Mashhood, Ahmad, Jahanzeb, and Usman (2006), “Efficient Generalized Colored image Enhancement”, IEEE, CIS.
[14] Zhiming, WANG, and Jianhua, TAO (2007) “A Fast Implementation of Adaptive Histogram Equalization” 8th International Conference on Signal Processing.
[15] Taguchi, Akira (2008), “Enhancement of MPEG/JPEG Images Based onHistogram Equalization Without Gamut Problem”, 2008 IEEE Conference on Soft Computing in Industrial Applications (SMCia/08), June 25-27, Muroran, JAPAN.
[16] Kyung, Wang et. al. (2011), “Hue Preservation using Enhanced Integrated Multi-scale Retinex for Improved Color Correction”, Journal of Imaging Science and Technology, 55(1): 010504–010504-10.

Sumit Kushwaha and Rabindra Kumar Singh, "Study and Analysis of Various Image Enhancement Method using MATLAB," International Journal of Computer Sciences and Engineering, Vol.3, Issue.1, pp.15-20, 2015.

Most of the computer applications use digital images. Digital image processing acts an important role in the analysis and interpretation of data, which is in the digital form. Images and videos of outdoor scenes are mainly affected by the bad weather conditions such as haze, fog, mist etc. It will result in poor visibility of the scene caused by the lack of quality. It will make awful impact on applications like object detection, tracking and surveillance system. One of the major challenges is the restoration of actual original image from the degraded image with at most accuracy. This survey aims to study about various existing methods such as polarization, dark channel prior, depth map based method etc. are used for dehazing.

Haze, polarization, depth map, and contrast maximization, dark channel prior, Independent Component Analysis, Anisotropic Diffusion

[1] http://en.wikipedia.org/wiki/Haze
[2] R. T. Tan, “Visibility in bad weather from a single image,” in Proc. IEEE Conf. Comput.Vis. Pattern Recognit.,2008, pp.1-8.
[3] K. He, J. Sun, and X. Tang, “Single image haze removal using dark channel prior,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 33, no. 12, pp. 2341–2353, Dec. 2011.
[4] R. Fattal, “Single image dehazing,” ACM Trans. Graph., vol. 27, no. 3,p. 72, Aug. 2008.
[5] J. Tarel, N. Hauti, “Fast visibility restoration from a single color or gray level image”,Proceedings of IEEE Internation Conference on Computer Vision (ICCV), Kyoto, Japan:IEEE Computer Society,2009, pp. 2201-2208.
[6] T. Treibitz and Y. Y. Schechner, “Polarization: Beneficial for visibility enhancement?” in Proceedings of IEEE Conference Computer Vision Pattern Recognition, 2009.
[7] Narasimhan, Srinivasa G. and Shree K. Nayar, "Contrast restoration of weather degraded images", IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, no.6,pp. 713-724, 2003.
[8] Cosmin Ancuti, and Codruta O. Ancuti,“Effective Contrast-Based Dehazing for Robust Image Matching” IEEE Geoscience And Remote Sensing Letters, Vol. 11, No. 11, Nov 2014.
[9] Schechner, Yoav Y., Srinivasa G. Narasimhan and Shree K. Nayar, "Instant dehazing of images using polarization", The Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition ( CVPR), vol. 1, pp. I-325, 2001.
[10] Nayar, Shree K. and Srinivasa G. Narasimhan, “Vision in bad weather ", Proceedings of the Seventh IEEE International Conference on Computer Vision, vol. 2, pp. 820-827, 1999.
[11] Narasimhan, Srinivasa G. and Shree K. Nayar, " Interactive (de) weathering of an image Using physical models " , IEEE Workshop on Color and Photometric Methods in Computer Vision, vol. 6 , no. 6.4, p. 1., France, 2003.
[12] Tripathi, and S. Mukhopadhyay, "Single image fog removal using anisotropic diffusion.”Image Processing, Vol. 6, no. 7, pp. 966-975, 2012.
[13] http://www.ijcea.com/wpcontent/uploads/2014/06/RUCHIKA_SHARMA_et_al.pdf
[14] Cosmin Ancuti, and Codruta O. Ancuti, “image dehazing by multi scale fusion” IEEE Geoscience And Remote Sensing Letters, Vol. 11, No. 11, Nov 2014.

Amla Thomas and M. Azath, "A Survey on Image and Video Dehazing," International Journal of Computer Sciences and Engineering, Vol.3, Issue.1, pp.21-23, 2015.

Cloud computing is a promising computing paradigm. Load balancing and rebalancing in cloud are important and challenging research area. Distributed file systems is the main building block in cloud computing. The large files will be divided into number of chunks and distributed into different systems. These chunks were allocated to each node to perform map reduce functions parallel over the nodes. Cloud is a dynamic environment, updating, replacing and adding of new nodes to the environment is a normal concern. This will impact the anatomy of the system and the chunk distribution will become uneven among the nodes. To overcome this, reallocate the chunks uniformly in the nodes. Load balancing and re-balancing helps to achieve high user satisfaction and well resource utilization. Emerging distributed systems are strongly depends on a central node for chunk reallocation. In a giant cloud central load balancer is put under significant workload and may lead to a performance bottleneck and single point of failure. This survey aims to study the different algorithms and issues of load balancing in cloud computing.

Allocation;Chunks;Cloud Computing;Distributed File System;Load Balancing

[1] Hung-Chang Hsiao, Member, IEEE Computer Society, Hsueh Yi Chung, HaiyingShen, Member, IEEE, and Yu-Chang Chao, “Load Rebalancing for Distributed File Systems in Clouds”, IEEE Trans. Parallel and Distributed Systems, vol. 24, no.5, May. 2013.
[2] Randles, M., D. Lamb and A. Taleb-Bendiab, “A Comparative Study into Distributed Load Balancing Algorithms for Cloud Computing,” in Proc. IEEE 24th International Conference on Advanced Information Networking and Applications Workshops (WAINA), Perth, Australia, April 2010.
[3] Dhinesh Babu L.D, P. VenkataKrishna, “Honey bee behavior inspired load balancing of tasks in cloud computing environments”, Applied Soft Computing 13 (2013) 2292–2303.
[4] Nitika, Shaveta and Gaurav Raj, “Comparative analysis of Load Balancing Algorithms in Cloud Computing”, International Journal of Advanced Research in Computer Engineering and Technology, vol.1, Issue 3, pp. 120-124, May 2012.
[5] Liu H., Liu S., Meng X., Yang C. and Zhang Y.,International Conference on Service Sciences (ICSS),257-262, 2010.
[6] Sadhasiva,DR.S.Jayarani,”Design and Implementation of an efficient Two-level Scheduler for Cloud Computing Environment.” International Conference on Advances in Recent Technologies in Communication and Computing, vol. 148, pp. 884–886 (2009)
[7] Zenon Chaczko, Venkatesh Mahadevan, ShahrzadAslanzadeh,ChristopherMcdermid, “Availabity and Load Balancing in Cloud Computing”International Conference on Computer and Software Modeling, IPCSIT, volume14, IACSIT Press, Singapore 2011
[8] Ren, X., R. Lin and H. Zou, "A dynamic load balancing strategy for cloud computing platform based on exponential smoothing forecast" in proc. International Conference on. Cloud Computing and Intelligent Systems (CCIS), IEEE, pp: 220-224, September 2011.
[9] Buyya R, R. Ranjan, RN. Calheiros, “InterCloud: Utilityoriented federation of cloud computing environments for scaling of application services”, International Conference on Algorithms and Architectures for Parallel Processing (ICA3PP), Busan, South Korea, 2010.
[10] Stanojevic R. and Shorten R., IEEE ICC, 1-6, 2009.
[11]Vouk, "Cloud Computing- Issues, Research and Implementations," Information Technology Interfaces, pp. 31-40, June 2008.
[12] O. Abu- Rahmeh, P. Johnson and A. Taleb-Bendiab,“A Dynamic Biased Random Sampling Scheme orScalable and Reliable Grid Networks”, INFOCOMP Journal of Computer Science, ISSN 1807-4545,VOL.7, December2008
[13] http://computer.howstuffworks.com/cloud-computing/cloud-computing.htm

Martina Poulose and M. Azath, "A Study of Load Balancing Techniques in Cloud," International Journal of Computer Sciences and Engineering, Vol.3, Issue.1, pp.24-27, 2015.

Biometrics is a method by which a person's authentication information is generated by digitizing measurements of a physiological or behavioral characteristic. Biometric authentication checks user's claimed identity by comparing an encoded value with a stored value of the concerned biometric characteristic. Various biometric authentications are face recognition, fingerprints, hand geometry etc .Among this, the most recent technology is palm vein authentication. Various techniques have been proposed by researchers in the area of palm vein identification. Most of the methods uses various features of palm vein like geometric, cosine similarity, wavelet features etc but lags with the accuracy of identification and authentication. Authentication using hand geometry does not have the same degree of permanence or individuality as other characteristics. Even authentication using Cosine similarity and wavelet features lags in accuracy. Palm vein authentication is highly accurate and secure since the authentication data exists inside the body and it is difficult to forge. It uses vascular patterns as personal identification data. This paper presents the analysis of various methods and algorithms that identifies the vein patterns in palm for authentication purpose.

Feature extraction, matching, Palm print recognition system, ROI

[1] Anil.K.Jain and Arun Ross, “Handbook of biometrics”, Dept of Computerscience and Engg, Michigan state university.
[2] Bincymaniyattu,”Palm vein technology”, software developer on Dec 14,2012.
[3] Sumalatha K.A, Harsha H, “Biometric Palmprint Recognition System:A Review” Dept. of Instrmt Tech,R.V. College Of Engineering, Bangalore, India, Volume 4, Issue 1, January 2014.
[4] G.SBadrinath and Phalguni Gupta, “Stockwell transform based palm- print recognition for palm print recognition”.
[5] Tee Connie, Andrew Teoh et al,”An automated palm print recognition system” ,14 January 2005.
[6] G.Seshokala, Dr.Umakanth Kulkarni, “palm print recognition using multiscale wavelet edge detection”, Aug 14, 2013.
[7] Wei-yu-han et al,”palm vein recognition using adaptive gabor filter”,dec 1,2012.
[8] K Shyr-wu et al,Jen chun Lee et al,”A secure palm vein recognition system”,Nov 1,2013.
[9] Agbaje K. M., Adeniyi A. Yusuf,” Palm Vein Recognition System Using Hybrid Principal Component Analysis and Artificial Network Neural”, Volume 3, Issue 7, July 2013.
[10] Hao Luo, Fa-Xin Yu, Jeng-Shyang Pan, Shu-Chuan Chu and Pei-Wei Tsai, “Survey of Vein Recognition Technique”,2010
[11] Mona A. Ahmedet et al,”Analysis of Palm Vein Pattern,2013

Sibi Sasidharan and M.Azath, "Study on Palm Vein Authentication," International Journal of Computer Sciences and Engineering, Vol.3, Issue.1, pp.28-32, 2015.

Now-a-days, data mining has been used extensively in different domains of application for prediction. Data mining has demonstrated promising results in the field of educational prediction. Artificial Neural Networks in particular, find extensive application for understanding the peculiarities of education field but there is still a lot to be done as far as the Indian universities are concerned. In this paper, it has been verified that various personal and academic attributes of students can be used to predict the percentage of marks in graduation, using real data from the students of a Delhi state university’s affiliates.

Educational Data Mining, Artificial Neural Network, Back Propagation, Academic Performance, Correlation analysis

[1] Kotsiantis S.B. and Pintelas P., A decision support prototype tool for predicting student performance in an ODL environment, International Journal of Interactive Technology and Smart Education, 1(4), p.p. 253-263, 2004.
[2] Kotsiantis S.B., Pierrakeas C. and Pintelas P., Predicting students’ performance in distance learning using machine learning techniques, Journal of Applied Artificial Intelligence, 18(5), p.p. 411-426, 2004.
[3] Folorunsho O., Comparative Study of Different Data Mining Techniques Performance in knowledge Discovery from Medical Database, International Journal of Advanced Research in Computer Science and Software Engineering Research Paper, Volume 3, Issue 3, March 2013 ISSN: 2277 128X
[4] Sivanandam S.N., Sumathi S., Deepa S.N.(2009). Introduction to Neural Networks using Matlab, Tata McGraw Hill Education Private Ltd., 2009.
[5] Kosko B.(2005). Neural Networks and Fuzzy Systems, Prentice Hall of India Ltd., 2005.

Kavita Pabreja, "Data Mining Based on Neural Networks for Education Data Forecasting," International Journal of Computer Sciences and Engineering, Vol.3, Issue.1, pp.33-38, 2015.

Malwares are malicious software, designed to damage computer systems without the knowledge of the owner. Rootkit is also malicious software which hides the existence of certain processes or programs from normal methods of detection and enables continued privileged access to a computer. Now a day the impact of malware and rootkit is getting worst. Their detection is difficult because malicious program may be able to subvert the software that is intended to find it. Detection methods uses an alternative and trusted operating system, signature scanning behavioral-based methods, difference scanning, and memory dump analysis etc. Malware and rootkit detectors are the primary tools in defense against malicious programs. The quality of such a detector is determined by the techniques used by it. There are mainly two types of techniques to detect the malwares, signature based and anomaly based techniques. Signature-based detection is a malware detection approach that identifies a malware instance by the presence of at least one byte code pattern present in a database of signatures from known malicious programs. If a program contains a pattern that already exists within the database, it is deemed. In anomaly based detection malwares are classified according to some heuristics and rules. This survey study about signature based and anomaly based malware detection and list their strengths and limitations. It also compares techniques and helps to choose a desirable technique for secure system.

Anomaly based malware, rootkit, malware detection malcode, malicious code, malicious software, signature-based, behavior based

[1]https://www.cert.gov.uk/wpcontent/uploads/2014/08/An-introduction-to malware.pdf
[2]http://www.ukessays.com/essays/computer-science/the-introduction-to-malicious-software-computer-science-essay.php
[3]http://en.wikipedia.org/wiki/Computer_virus
[4]http://en.wikipedia.org/wiki/Computer_worm
[5]http://en.wikipedia.org/wiki/Trojan_horse_(computing)
[6]” Survey on Malware Detection Methods” Vinod P. Department of Computer Engineering, Malaviya National Institute of Technology, Jaipur, Rajasthan
[7]“A Survey of Malware Detection Techniques”NwokediIdika,AdityaPMathur.Department of Computer Science Purdue University, West Lafayette, IN 47907.
[8]” A Survey on Techniques in Detection and Analyzing Malware Executables” Kirti Mathur M.Tech. Scholar, Department of CSE Rajasthan Technical University, India.
[9]“A Specification-based Intrusion Detection System for AODV” Chin-Yang Tseng, Poornima Balasubramanyam, Calvin Ko,Rattapon Limprasittiporn,Jeff Rowe,Karl Levitt,Computer Security Laboratory University of California, Davis.
[10]http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.138.7174
[11] Greoigre Jacob,Herve Debar,Eric Fillol,”Behavioral detection of malware:from a survey towards an established taxonomy”,Springer-Verlag France 2008

Aswana Lal, M. Azath and Miss Sony, "Survey on Malware and Rootkit Detection," International Journal of Computer Sciences and Engineering, Vol.3, Issue.1, pp.39-42, 2015.

In recent years, WLANs have been developing rapidly and are increasingly being used in many applications. The extensive application of WLAN has been using an authentication framework widely called as Extensible Authentication Protocol (EAP). The requirements for EAP methods (i.e. Authentication mechanisms built on EAP) in WLAN authentication have been defined in RFC 4017 are issues also increasingly receiving widespread attention. To achieve user efficiency and robust security, lightweight computation and forward secrecy, not included in RFC 4017, are also desired in WLAN authentication. However, all EAP methods and authentication protocols designed for WLANs so far do not satisfy all of the above properties. With detailed analysis of all EAP Methods and authentication protocols designed for WLANs, this article pointed out properties of all EAP method.

EAP Method, Authentication, WLAN

[1] Chun-I Fan, Yi-Hui Lin, and Ruei-Hau Hsu “Complete EAP Method: User Efficient and Forward Secure Authentication Protocol for IEEE 802.11 Wireless LANs” Ieee Transactions On Parallel And Distributed Systems, Vol. 24, No. 4, April 2013.
[2] Liang, Jian Qiao, and Xuemin (Sherman) Shen,” PARK: A Privacy-preserving Aggregation Scheme with Adaptive Key Management for Smart Grid”, 2nd IEEE/CIC International Conference on Communications in China (ICCC): QRS: QoS, Reliability and Security,2013.
[3] Kamal Ali Alezabi, Fazirulhisyam Hashim, Shaiful Jahari Hashim and Borhanuddin M. Ali,” A New Tunnelled EAP based Authentication Method for WiMAX Networks”, IEEE 11th Malaysia International Conference on Communications, November 2013.
[4] Ling-wei Zhou, Sheng-ju Sang,” Analysis and Improvements of PEAP Protocol in WLAN”, International Symposium On Information Technology IN Medicine and Education,2012.
[5] Ahmed M. El- Nagar, Dr. Ahmed A. Abd El-Hafez and Prof.Dr. Adel Elhna Wy,” A Novel EAP-Moderate Weight Extensible Authentication Protocol”,IEEE,2012.
[6] Xiaoling Zheng, Jidong Jin,” Research for the Application and Safety of MD5 Algorithm in Password Authentication”, 9th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2012),2012.
[7] DictionaryAttackonCiscoLEAP,http://www.cisco.com/warp/public/707/cisco-sn-20030802-leap.shtml, 2012.
[8] Khidir M. Ali and Ali Al-Khalifah,” A Comparative Study of Authentication Methods for Wi-Fi Networks”, Third International Conference on Computational Intelligence, Communication Systems and Networks,2011.
[9] Alexandra Chiornita, Laura Gheorghe, Daniel Rosner,” A Practical Analysis of EAP Authentication Methods”, 9th RoEduNet IEEE International Conference ,2010.
[10] D. Simon, B. Aboba, and R. Hurst, "The EAP-TLS Authentication Protocol",RFC 5216, March 2008.
[11] H. Hwang, G. Jung, K. Sohn, and S. Park, “A Study on MITM (Man in the Middle) Vulnerability in Wireless Network Using802.1X and EAP,”Proc. Int’l Conf. Information Systems Security, pp. 164-170, 2008.
[12] Flávio O. Silva, João A. A. Pacheco, Pedro F. Rosa, Ph.D.,” A SRP Based Handler for Web Service Access Control” IEEE International Conference on Services Computing (SCC’04),2004.
[13] B. Aboba, L. Blunk, J. Vollbrecht, J. Carlson, and H. Levkowetz, "Extensible Authentication Protocol (EAP)", RFC 3748, June 2004.
[14] A. Palekar, D. Simon, G. Zorn, J. SaloweY,H. Zhou, and S. Josefsson, "Protected EAP Protocol (PEAP) Version 2", work in progress, October 2004.
[15] Ma. Y. and Cao, X., (2003), “How to use EAP-TLS Authentication in PWLAN Environment”, IEEE, Proceedings of the 2003 International conference on neural networks and signal processing, Volume 2, 14-17 Dec, Pages 1677-1680.
[16] Jitesh Zade,Manish Tembhurkar,”Voice Capacity Over wi-fi network”,IJETED-2014.

Yogesh Singare and Manish Tembhurkar, "A Comparitive Analysis of EAP Authentication Mechanism for WLAN," International Journal of Computer Sciences and Engineering, Vol.3, Issue.1, pp.43-48, 2015.

Google’s Android and Apple ios are operating system used primarily in mobile technology, such as tablets and smart phone’s. In these past years, the entire world has witnessed the rise of these two platforms. The iOS platform developed by Apple is the world’s most advanced mobile operating system (OS), continually redefining what people can do with a mobile device. Google’s Android platform developed by Google is an optimized platform for mobile devices with a perfect combination of an application programs, middleware and operating system (OS). This research paper will be extremely helpful to have a comparative overview between these two OS’s and also sheds light on the technical specification, market analysis, development environment aspect of these two largely operating systems. And the Comparison is done on the basis of their performances, their platform and the growth in mobile world. The Salient new key Features introduced in Android and IOS are also described.

Smartphone’s; Android; IOS; iPhone; Mobile operating system; Comparison; Architecture

[1] Definition of smart phone Retrieved from http://searchmobilecomputing.techtarget.com/definition/smartphone
[2] Android source code and its details, Retrieved from http://source.android.com/source/index.html#governance-philosophy
[3] Android Low Level System Architecture, Retrieved from http://source.android.com/devices/index.html
[4] IOS Technologies and its details, Retrieved from https://developer.apple.com/library/ios/documentation/Miscellaneous/Conceptual/iPhoneOSTechOverview/Introduction/Introduction.html#//apple_ref/doc/uid/TP40007898-CH1-SW1
[5] Cocoa Layer and its definition, Retrieved from https://developer.apple.com/library/ios/documentation/Miscellaneous/Conceptual/iPhoneOSTechOverview/iPhoneOSTechnologies/iPhoneOSTechnologies.html#//apple_ref/doc/uid/TP40007898-CH3-SW1
[6] Media Layer and its definition, Retrieved from https://developer.apple.com/library/ios/documentation/Miscellaneous/Conceptual/iPhoneOSTechOverview/MediaLayer/MediaLayer.html#//apple_ref/doc/uid/TP40007898-CH9-SW4
[7] Core Services Layer and its definition, Retrieved from https://developer.apple.com/library/ios/documentation/Miscellaneous/Conceptual/iPhoneOSTechOverview/CoreServicesLayer/CoreServicesLayer.html#//apple_ref/doc/uid/TP40007898-CH10-SW5
[8] Core OS Layer and its definition, Retrieved from
https://developer.apple.com/library/ios/documentation/Miscellaneous/Conceptual/iPhoneOSTechOverview/CoreOSLayer/CoreOSLayer.html#//apple_ref/doc/uid/TP40007898-CH11-SW1
[9] Android versions and its features, Retrieved from http://www.ebay.com/gds/What-s-the-Difference-Between-iOS-and-Android-/10000000177631975/g.html
[10] Android Jelly Bean and its features, Retrieved from http://www.android.com/versions/jelly-bean-4-3/
[11] Android Kitkat and its features, Retrieved from http://www.android.com/versions/kit-kat-4-4/
[12] Android Lollipop and its features, Retrieved from http://www.android.com/versions/lollipop-5-0/
[13] IOS versions and its features, Retrieved from http://www.ebay.com/gds/What-s-the-Difference-Between-iOS-and-Android-/10000000177631975/g.html
[14] IOS 6 and its features, Retrieved from http://www.hongkiat.com/blog/ios-history/
[15] IOS 7 and its features, Retrieved from http://www.trustedreviews.com/opinions/ios-7-features-list
[16] IOS 8 and its features, Retrieved from https://www.apple.com/ios/what-is/
[17] Choosing between IOS and Android, Retrieved from http://www.diffen.com/difference/Android_vs_iOS#UI_Design_for_Android_vs._iOS_7
[18] Advantages and Disadvantages of IOS and Android OS, Retrieved from http://www.cs.ucf.edu/~dcm/Teaching/COP5611Spring2010/Project/JunyaoZhang-Project.pdf
[19] Worldwide Smartphone Forecast by Shipments and Value, 2014 and 2018, Retrieved from http://www.idc.com/getdoc.jsp?containerId=prUS25282214
[20] App downloads on OS Retrieved from http://www.inmobi.com/blog/2014/09/19/asian-countries-dominate-app-downloads-per-capita
[21] Comparison Development Environment, Retrieved from http://www.diffen.com/difference/Android_vs_iOS#UI_Design_for_Android_vs._iOS_7
[22] An overview of major differences, Retrieved from http://pakwired.com/deep-dive-android-vs-ios-comparison-review/

Abdul Haseeb, "Comprehensive and Technical Overview of Android and IOS OS," International Journal of Computer Sciences and Engineering, Vol.3, Issue.1, pp.49-57, 2015.