Volume-6 , Issue-4 , Aug 2018, ISSN 2320-7639 (Online) Go Back

• Open Access   Article

  Image Encryption using Linear Cryptanalysis and different Fuzzy operations

  Dipankar Dey

  Research Paper | Isroset-Journal (IJSRCSE)

  Vol.6 , Issue.4 , pp.1-8, Aug-2018

  CrossRef-DOI:   https://doi.org/10.26438/ijsrcse/v6i4.18

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  Abstract
  Nowadays, the security of digital image is one of the most essential terms. In this article, a piecewise linear chaotic map based image encryption has been proposed. For implementation protection from different types of attacks, the Linear Cryptanalysis system is applied to encrypt the image. The irreducible polynomial is implemented to design the confusion - diffusion architecture that scrambles the pixel position of the image. To alter pixel intensity, the Fuzzy operations have been applied which makes the scheme more secure. The proposed encryption scheme is applied to resist some cryptanalysis method such as plaintext or cipher text attacks. The different theoretical and statistical analysis shows that the scheme has high -level security. The two main statistical factors, the number of pixel change rate and the information entropy, gives the better result in this article.

  Key-Words / Index Term
  Encryption, PWLC map, irreducible polynomial, Fuzzy operations, Linear Cryptanalysis

  References
  [1] Thang Manh Hoang and Hoang Xuan Thanh, “Cryptanalysis and security improvement for a symmetric color image encryption algorithm”, Optik International Journal for Light and Electron Optics, Vol. 155, pp. 366-383, 2018.
  [2] David Arroyo, Chengqing Li, Shujun Li, Gonzalo Alvarez, Wolfgang A. Halang, “Cryptanalysis of an image encryption scheme based on a new total shuffling algorithm”, Chaos, Solitons and Fractals, Vol. 41, pp. 2613-2616, 2009.
  [3] G. Alvarez, Shujun Li, “Cryptanalyzing a nonlinear chaotic algorithm NCA) for image encryption”, Commun Nonlinear Sci Numer Simulat, Vol. 14, pp. 3743-3749, 2009.
  [4] https://en.wikipedia.org/wiki/Irreducible polynomial
  [5] Chao-Lin Kuo, Lung-Chuan Huang, Shun-Jih Wang, Jui-Sheng Lin, Cheng-Chi Wang and Jun-Juh Yan, “Image Encryption Based on Fuzzy Synchronization of Chaos Systems”, IEEE 37th Annual Computer Software and Applications Conference, Vol. 23, pp. 153-154, 2013.
  [6] Hinal M. Mudia and P. V. Chavan, “Fuzzy logic based image encryption for confidential data transfer using (2, 2) secret sharing scheme-review”, International Conference on Advances in Computer Engineering and Applications, Vol. 10, pp. 404 - 408, 2015.
  [7] Xingyuan Wang and Dahai Xu, “A novel image encryption scheme using chaos and Langtons Ant cellular automaton”, Nonlinear Dynamics, Vol. 79, pp. 2449-2456, 2015.
  [8] M. Ghebleh, A. Kanso and D. Stevanovic, “A novel image encryption algolorithm based on piecewise linear chaotic maps and least squares approximation”, Springer Science+Business Media New York, Vol. 77, pp. 7305-7326, 2018.
  [9] Wun-She Yap, Raphael C.-W. Phan, Wei-Chuen Yau and Swee-Huay Heng, “Cryptanalysis of a new image alternate encryption algorithm based on chaotic map”, Nonlinear Dynamics, Vol. 80, pp. 483-1491, 2015.
  [10] Eun-Jun Yoon and Kee-YoungYoo, “Cryptanalysis of a modulo image encryption scheme with fractal keys”, Optics and Lasers in Engineering, Vol. 48, pp. 821826, 2010.
  [11] Xiao Jun Tong, ZhuWang , Miao Zhang , Yang Liu , Hui Xu and Jing Ma, “An image encryption algorithm based on the perturbed high-dimensional chaotic map”, Nonlinear Dynamics, Vol. 80, No. 3, pp. 1493-1508, 2015.
  [12] X. Deng and W. Wen, “Optical multiple-image encryption based on fully phase encoding and interference”, Optik-International Journal for Light and Electron Optics, Vol. 126(21), pp. 3210-3214, 2015.
  [13] N.K.Pareek, VinodPatidar and K.K.Sud, “Image encryption using chaotic logistic map”, Image and Vision Computing, Vol. 24, pp. 926-934 ,2006.
  [14] Kwok-Wo Wong, “Image Encryption Using Chaotic Maps”, Intelligent Computing Based on Chaos, Vol. 184, pp. 333-354, 2009.
  [15] Yasaman Hashemi, “Design a new image encryption using fuzzy integral permutation with coupled chaotic maps”, International Journal of Research in Computer Science, Vol. 3, pp. 27- 34, 2013.
  [16] Jun-xin Chen Zhi-liang Zhu, Chong Fu and Hai Yu, “Optical image encryption scheme using 3-D chaotic map based joint image scrambling and random encoding in gyrator

  Citation
  Dipankar Dey, "Image Encryption using Linear Cryptanalysis and different Fuzzy operations," International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.4, pp.1-8, 2018

• Open Access   Article

  An Empirical Proposition to Load Balancing Effectuate on AWS Hybrid Cloud

  A.A. Ekre, N.M. Nimbarte, S.V. Balamwar

  Research Paper | Isroset-Journal (IJSRCSE)

  Vol.6 , Issue.4 , pp.9-17, Aug-2018

  CrossRef-DOI:   https://doi.org/10.26438/ijsrcse/v6i4.917

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  A proliferate demand of supply for communication systems in the current scenario are based on their performance and advancements in the field of technology paves a way for the cloud computing based services. The tractate cynosures counterpoise consignment over servers in a private cloud which is a small practical example of Big Data. As the number of computing devices increases in a network, Countervail freightage is an argumenta concern in Cloud Computing. To make the network merchandise equally balanced trim to provide hi speed connectivity of devices with effective resource utilization; several load balancing algorithms have been implemented wherein Round Robin Routing Algorithm seemed effective while hosting Applications over the web. Round Robin Routing Algorithm plays a vital role in balancing of Load over servers in a virtual network cluster. The hosting of Web Applications was done on private Cloud services offered by Amazon Web Services.

  Key-Words / Index Term
  Cloud, Load Balancing, Virtual Network, Servers, Amazon Web Services, Elastic Cloud Compute, Instance, Resources, Web Applications, Virtual Machine, Amazon Elastic Map Reduce, Virtual Machine Monitor, Equal Spread Current Execution Algorithm, Service Level Agreement

  References
  [1] A. Verma, S.K. Sharma, B. Kaur, “Comparative Analysis on Load Balancing Techniques in Cloud Computing”, Indian Journal of Science and Technology, Vol.9, No.11, 2016.
  [2] A. Akusok, K.M. Bjork, Y. Miche, A. Lendasse, “High-Performance Extreme Learning Machines: A Complete Toolbox for Big Data Applications”, http://www.apache.org/licenses/, (accessed 23 February 2015).
  [3] D.C. Devi and V.R. Uthariaraj, “Load Balancing in Cloud Computing Environment Using Improved Weighted Round Robin Algorithm for Nonpreemptive Dependent Tasks”, The Scientific World Journal, 2016.
  [4] D. Saranya, L.S. Maheshwari, “Load Balancing Algorithms in Cloud Computing: A Review”, International Journal of Advanced Research in Computer Science and Software Engineering, Vol.5, No.7, 2015.
  [5] D. Kashyap, J. Viradiya, “A Survey of Various Load Balancing Algorithms in Cloud Computing”, International Journal of Scientific & Technology Research, Vol.3, No.11, 2014.
  [6] D.P. L, “Load Balancing Algorithms in Cloud Computing”, International Journal of Advanced Computing and Mathematical Sciences, Vol.4, No.3, pp.229-233, 2013.
  [7] M. Katyal, A. Mishra, “A Comparative Study of Load Balancing Algorithms in Cloud Computing Environment’, www.publishingindia.com, Vol.1, No.2, 2013.
  [8] Ms. NIKITA, “Comparative Analysis of Load Balancing Algorithms in Cloud Computing”, International Journal of Engineering and Science, Vol.1, No.1, 2014.
  [9] N. Sran, N. Kaur, “Comparative Analysis of Existing Load Balancing Techniques in Cloud Computing”, International Journal of Engineering Science Invention, Vol.2, No.1, pp.60-63, 2013.
  [10] N. Sran, N. Kaur, “Comparative Analysis of Existing Dynamic Load Balancing Techniques”, International Journal of Computer Applications, Vol.70, No.26, 2013.
  [11] N. Pasha, A. Agarwal, R. Rastogi, “Round Robin Approach for VM Load Balancing Algorithm in Cloud Computing Environment”, International Journal of Advanced Research in Computer Science and Software Engineering, Vol.4, No.5, 2014.
  [12] N. Haryani, D. Jagli, “Dynamic Method for Load Balancing in Cloud Computing”, IOSR Journal of Computer Engineering (IOSR-JCE), Vol.16, No.4, pp.23-28, 2014.
  [13] R. Kaur, P. Luthra, “Load Balancing in Cloud Computing”, International Conference on Recent Trends in Information, Telecommunication and Computing, 2014.
  [14] R. Gao, J. Wu, “Dynamic Load Balancing Strategy for Cloud Computing with Ant Colony Optimization”, Future Internet, Vol.7, pp.465-483, 2015.
  [15] R. Panwar, “Comparative Study of Various Load Balancing Techniques in Cloud Computing”, International Journal of Engineering Research & Technology (IJERT), Vol.3, No.9, 2014.
  [16] R. Panwar, B. Mallick, “A Comparative Study of Load Balancing Algorithms in Cloud Computing”, International journal of Computer Applications, Vol.117, No.24, 2015.
  [17] R. Gupta, “Review on Existing Load Balancing Techniques of Cloud Computing”, International Journal of Advanced Research in Computer Science and Software Engineering, Vol.4, No.2, 2014.
  [18] S. Lamba, D. Kumar, “A Comparative Study on Load Balancing Algorithms with Different Service Broker Policies in Cloud Computing”, International Journal of Computer Science and Information Technologies, Vol.5, No.4, 2014.
  [19] S. Ray, A.D. Sarkar, “Execution Analysis of Load Balancing Algorithms in Cloud Computing Environment”, International Journal on Cloud Computing: Services and Architecture (IJCCSA), Vol.2, No.5, 2012.
  [20] S. Kaur, S. Kinger, “Review on Load Balancing Techniques in Cloud Computing Environment”, International Journal of Science and Research (IJSR), Vol.3, No.6, 2012.
  

  Citation
  A.A. Ekre, N.M. Nimbarte, S.V. Balamwar, "An Empirical Proposition to Load Balancing Effectuate on AWS Hybrid Cloud," International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.4, pp.9-17, 2018

• Open Access   Article

  Fast and Real Life Object Detection System Using Simple Webcam

  Md.T. Akhtar, S.T. Razi, K.N. Jaman, A. Azimusshan, Md.A. Sohel

  Research Paper | Isroset-Journal (IJSRCSE)

  Vol.6 , Issue.4 , pp.18-23, Aug-2018

  CrossRef-DOI:   https://doi.org/10.26438/ijsrcse/v6i4.1823

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  The project is designed to detect a real-life object using a simple webcam. The image captured by the webcam is to be enhanced and magnified and then is compared with similar type of image in our database to detect the type of image. The data being compared is binary string, a unique attribute is compared which has been acquired by feature extraction. The following technology can be used in face recognition system, defence system, production line or removal of defective product. We are using MATLAB to develop our project. The experiments conducted in accordance with proposed methods which are suitable for real-time surveillance system [1].

  Key-Words / Index Term
  Fast Object, Moving Object, Real-life Detection, Web Cam, MATLAB, Image Processing

  References
  [1]. Li Fang, School of Computer Science Nanyang Technological University, Singapore and Zhang Meng, Dunman High School Claire Chen Qian Hui River Valley High School, Singapore. “Smart Motion Detection Surveillance System”. Published by IEEE on 21 July 2009.
  [2]. Yash Gupta, Samriddhi Mishra, Deepti Sharma, "Smart Surveillance System", International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.780-784, 2018.
  [3]. David Houcque, “Introduction to Matlab for Engineering Students”, Northwestern University, 2005.
  [4]. Matworks, “Getting Started with MATLAB”, Matlb Primer.
  [5]. Byron S Gottfried, “Schaum`s Outline Of Programming With C”, McGraw Hill Publication, 2010.
  [6]Kala Chandrashekhar L, Manasa B S, "A Study on Segmentation of Moving Objects Under Dynamic Conditions", International Journal of Computer Sciences and Engineering, Vol.4, Issue.6, pp.173-179, 2016.
  [7]. Vikramsingh R. Parihar, Anagha P. Dhote, "A Novel Approach to Real Time Face Detection and Recognition", International Journal of Computer Sciences and Engineering, Vol.5, Issue.9, pp.62-67, 2017.
  

  Citation
  Md.T. Akhtar, S.T. Razi, K.N. Jaman, A. Azimusshan, Md.A. Sohel, "Fast and Real Life Object Detection System Using Simple Webcam," International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.4, pp.18-23, 2018

• Open Access   Article

  Face Identification through Learned Image High Feature Video Frame Works

  Boya Akhila, Burgubai Jyothi

  Research Paper | Isroset-Journal (IJSRCSE)

  Vol.6 , Issue.4 , pp.24-29, Aug-2018

  CrossRef-DOI:   https://doi.org/10.26438/ijsrcse/v6i4.2429

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  Abstract
  The affluence and possibility of video taking devices, such as mobiles and security cameras have inspired the search for video on face recognition which is very relevant in police applications. Whereas current paths have reported high accuracy errors, performance at lower prices for wrong acceptance requires important advancement. The choice of frames is followed by drawing features based on the representation of learning ,where three hand-outs are represented 1) Deep learning architecture, which is a mixture of low stacking automatic encoder (SDAE) and deep machine Boltzmann (DBM) 2) formulation for joint illustration in an automatic encoder 3) Improve the DBM loss function, including low range modification. At last a multilayer neural network is used as a classifier to get the verification decision. The results are shown in two public databases on hand, YouTube Faces,Point and Shoot Challenge. The new study suggests that 1) frame selection based on the quality of the proposed features gets extraordinary and steady performance compared to the front frame, casual frames or plot selection using perceptual image quality dimensions without reference and 2) SDAE features of Common learning and low DBM and low regularization range helps get better facial confirmation.

  Key-Words / Index Term
  Deep Learning, Auto Encoder, Deep Boltzmann, Machine, Face Perception, Frame Selection

  References
  [1] Facial recognition technology safeguards Beijing Olympics, accessed on Mar. 10, 2017 [Online]. Available: http://english.cas.cn/ resources/archive/china_archive/cn2008/200909/t20090923_42959.shtml
  [2] J. Beveridge et al., “The challenge of face recognition from digital point-and-shoot cameras,” in Proc. IEEE Conf. Biometrics Theory, Appl. Syst., Oct. 2013, pp. 1–8.
  
  [3] L. Wolf, T. Hassner, and I. Maoz, “Face recognition in unconstrained videos with matched background similarity,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Jun. 2011, pp. 529–534.
  
  [4] L. Wolf and N. Levy, “The SVM-minus similarity score for video face recognition,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Jun. 2013, pp. 3523–3530.
  
  [5] H. Li, G. Hua, Z. Lin, J. Brandt, and J. Yang, “Probabilistic elastic matching for pose variant face verification,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Jun. 2013, pp. 3499–3506.
  
  [6] Z. Cui, W. Li, D. Xu, S. Shan, and X. Chen, “Fusing robust face region descriptors via multiple metric learning for face recognition in the wild,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Jun. 2013,
  
  pp. 3554–3561.
  
  [7] H. Méndez-Vázquez, Y. Martínez-Díaz, and Z. Chai, “Volume struc-tured ordinal features with background similarity measure for video face recognition,” in Proc. Int. Conf. Biometrics (ICB), Jun. 2013,
  
  pp. 1–6.
  
  [8] H. S. Bhatt, R. Singh, and M. Vatsa, “On recognizing faces in videos using clustering-based re-ranking and fusion,” IEEE Trans. Inf. Forensics Security, vol. 9, no. 7, pp. 1056–1068, Jul. 2014.
  
  [9] J. Y. Junlin Hu, J. Lu, and Y.-P. Tan, “Large margin multi-metric learning for face and kinship verification in the wild,” in Proc. Asian Conf. Comput. Vis., 2014, pp. 252–267.
  
  [10] J. Hu, J. Lu, and Y. Tan, “Discriminative deep metric learning for face verification in the wild,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Jun. 2014, pp. 1875–1882.
  
  [11] Y. Taigman, M. Yang, M. Ranzato, and L. Wolf, “DeepFace: Closing the gap to human-level performance in face verification,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Jun. 2014, pp. 1701–1708.
  
  [12] W. Wang, R. Wang, Z. Huang, S. Shan, and X. Chen, “Discriminant analysis on riemannian manifold of Gaussian distributions for face recognition with image sets,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Jun. 2015, pp. 2048–2057.
  
  [13] N. M. Khan, X. Nan, A. Quddus, E. Rosales, and L. Guan, “On video based face recognition through adaptive sparse dictionary,” in Proc. IEEE Int. Conf. Workshops Autom. Face Gesture Recognit., May 2015,
  
  pp. 1–6.
  
  [14] H. Li, G. Hua, X. Shen, Z. Lin, and J. Brandt, “Eigen-PEP for video face recognition,” in Proc. Asian Conf. Comput. Vis., 2014, pp. 17–33.
  
  [15] H. Li and G. Hua, “Hierarchical-PEP model for real-world face recog-nition,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Jun. 2015,
  
  pp. 4055–4064.
  

  Citation
  Boya Akhila, Burgubai Jyothi, "Face Identification through Learned Image High Feature Video Frame Works," International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.4, pp.24-29, 2018

• Open Access   Article

  A Comparative Study on AES 128 BIT AND AES 256 BIT

  Toa Bi Irie Guy-Cedric , Suchithra. R.

  Research Paper | Isroset-Journal (IJSRCSE)

  Vol.6 , Issue.4 , pp.30-33, Aug-2018

  CrossRef-DOI:   https://doi.org/10.26438/ijsrcse/v6i4.3033

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  Abstract
  Cloud computing present a new thinking to apply security since basic administrations are regularly outsourced to the cloud vendor. The current encryption techniques are regularly exposed to threats. Therefore it is important to study the features of the existing algorithm. This paper studies AES 128 and AES 256 based on various parameters and compare their performance that may suggest in development of better encryption technique.

  Key-Words / Index Term
  Cryptography, Cloud Computing, AES

  References
  [1]. Samiul Islam “Comparative Analysis of AES Algorithms and Implementation of AES in Arduino” 20.12.2015
  [2]. V. R. Joan Daemen. AES Proposal: Rijndael, version 2, AES submission. 1999.
  [3]. Joan Daemen, Steve Borg and Vincent Rijmen.”The Design of Rijndael: AES The Advanced Encryption Standard”, Springer, 2002.
  [4]. Mansoor Ebrahim, Shujaat Khan, Umer Bin Khalid, “Symmetric Algorithm Survey: A Comparative Analysis” May 2, 2014
  [6] cs.utsa.edu/~wagner/laws/AESintro.html
  [5]. csrc.nist.gov/publications/fips/fips197/fips 197.pdf. NIST_SP-500-291_Version-2_2013_June18_FINAL
  [7]. Chenhui Jin, Ting Cu “Classification of SPN Structures From the Viewpoint of Structural Cryptanalysis” IEEE, December 27, 2017
  [8] Umer Farooq, M. Faisal Aslam “Comparative analysis of different AES implementation techniques for efficient resource usage and better performance of an FPGA”, Journal of King Saud University – Computer and Information Sciences, 10 January 2016, sciencedirect
  [9]. Arash Karimi, Hadi Shahriar Shahhoseini “Cryptanalysis of a Substitution-Permutation Network Using Gene Assembly in Ciliates” Int`l J. of Communications, Network and System Sciences Vol. 5 No. 3 ,2012.
  

  Citation
  Toa Bi Irie Guy-Cedric , Suchithra. R., "A Comparative Study on AES 128 BIT AND AES 256 BIT," International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.4, pp.30-33, 2018

• Open Access   Article

  An approach to analyze suicidal tendency in blogs and tweets using Sentiment Analysis

  Shrija Madhu

  Research Paper | Isroset-Journal (IJSRCSE)

  Vol.6 , Issue.4 , pp.34-36, Aug-2018

  CrossRef-DOI:   https://doi.org/10.26438/ijsrcse/v6i4.3436

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  Abstract
  Sentiment Analysis(SA) or Opinion Mining is done to find the opinion of the users and customers to review and analyse their opinions on various products and services. It is one of the major tasks in business these days for knowing demands of customers. In this paper a sentiment analysis method for analyzing the suicidal tendencies in blogs and tweets is proposed. The proposed method uses concepts like Bag of words ,Part of Speech and Natural Language Processing for analzing the text.In life sometimes such a situation arises where the person finds himself trapped and suicide only seems to be the ultimate respite from all problems. Such a person may not share his mental condition verbally with anyone but may share it through tweets and messages. This paper is an approach to reach out a helping hand to such people by analyzing such tendencies in their messages. The proposed method uses Python Language module Textblob for performing different analysis task on the text.

  Key-Words / Index Term
  Sentiment Analysis; Suicide; Textblob; blogs;tweets

  References
  [1]. Collobert, R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu , K., & Kuksa,P.“Natural language processing (almost) from scratch”. The Journal of Machine Learning Research, 12, 2493–2537, 2011.
  [2]. Gimpel, K., Schneider, N., O’Connor, B., Das, D., Mills, D., Eisenstein, J., etal. “Part-of-speech tagging for twitter: Annotation, features, and experiments”. In Proceedings of the 49th annual meeting of the association for computational linguistics: Human language technologies: Short papers - volume 2 HLT ’11,pp. 42–47,2011
  [3]. Pak, A., & Paroubek, P. “Twitter as a corpus for sentiment analysis and opinion mining”. In LREc: vol. 10,pp. 1320–1326,2010
  [4]. Pang, B., Lee, L., & Vaithyanathan, S. “Thumbs up?: sentiment classification using machine learning techniques”. In Proceedings of the ACL-02 conference on
  empirical methods in natural language processing-Volume 10,pp. 79–86,2002
  [5]. Taboada, M., Brooke, J., Tofiloski, M., Voll, K., & Stede, M. “Lexicon-based methods for sentiment analysis”. Computational linguistics, 37, 267–307,2011.
  [6]. Xia, R., & Zong, C. “Exploring the use of word relation features for sentiment classification”. In Proceedings of the 23rd international conference on computational linguistics: Posters COLING ’10,pp. 1336–1344,2010
  [7]. Kharde,A.V., & Sonawane,S.S. “Sentiment Analysis of Twitter Data: A Survey of Techniques”. International Journal of Computer Applications, Volume 139 , No.11, 2016
  

  Citation
  Shrija Madhu, "An approach to analyze suicidal tendency in blogs and tweets using Sentiment Analysis," International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.4, pp.34-36, 2018

• Open Access   Article

  A Study on Energy Efficient VM Allocation in Green Cloud Computing

  Chingrace Guite, Kamaljeet Kaur Mangat

  Review Paper | Isroset-Journal (IJSRCSE)

  Vol.6 , Issue.4 , pp.37-40, Aug-2018

  CrossRef-DOI:   https://doi.org/10.26438/ijsrcse/v6i4.3740

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  Abstract
  Cloud computing plays a significant role since its evolution. With its ubiquitous nature, sharing of resources and management of services has never been convenient than ever before. Due to its ability to provide scalability and elasticity infrastructure, many organization utilizes the services, where the workload is shifted in cloud data centers. This data center consumes more power and there is the release of unwanted carbon footprint in the environment. Therefore here lies the need to improve the use of energy and at the same time minimizing power consumption. In this paper, we present a survey on VM placement and migration to achieve energy efficiency in cloud data centers.

  Key-Words / Index Term
  Power Consumption, Virtual Machine Allocation, Virtual Machine Migration, Green Computing

  References
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  Citation
  Chingrace Guite, Kamaljeet Kaur Mangat, "A Study on Energy Efficient VM Allocation in Green Cloud Computing," International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.4, pp.37-40, 2018

• Open Access   Article

  A Novel Approach to Minimize DFA State Machines Using Linked List

  Yogesh Pant

  Research Paper | Isroset-Journal (IJSRCSE)

  Vol.6 , Issue.4 , pp.41-45, Aug-2018

  CrossRef-DOI:   https://doi.org/10.26438/ijsrcse/v6i4.4145

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  Abstract
  DFA minimization is a significant problem in algorithm design. Minimization of DFA works on the concept of DFA equivalence: Two DFA’s are equivalent if and only if both accept the identical strings of same set. Plethora of computational problems can be solved simply by using the encoding method. Combinatorial objects which are to be used as strings over a finite alphabet can be encoded. Standard algorithms from computational linear algebra are subjected to be used efficiently to solve the computational problems if a DFA recognize the set of encoded strings. This proposed method provides better results whereas other methods may face serious problem of time and space complexities.

  Key-Words / Index Term
  DFA; NFA; Regular expressions; Compiler Design; Linked List

  References
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  [7] Aakanksha Pandey, Dr. Nilay Khare and Akhtar Rasool“Efficient Design and Implementation of DFA Based Pattern Matching on Hardware”, IJCSI March 2012.
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  Citation
  Yogesh Pant, "A Novel Approach to Minimize DFA State Machines Using Linked List," International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.4, pp.41-45, 2018

• Open Access   Article

  Detection of Node Capture Attack in Wireless Sensor Networks

  K. Ravikumar, V. Manikandan

  Research Paper | Isroset-Journal (IJSRCSE)

  Vol.6 , Issue.4 , pp.56-61, Aug-2018

  CrossRef-DOI:   https://doi.org/10.26438/ijsrcse/v6i4.5661

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  Abstract
  A comprehensive analysis on connectivity and resilience of secure sensor networks under the widely studied q-composite key pre-distribution scheme. For network connectivity, which ensures that any two sensors can find a path in between for secure communication, we derive the conditions to guarantee connectivity in consideration of: 1) node-capture attacks, where the adversary may capture a set of sensors and compromise keys in their memory; 2) sensor mobility, meaning that sensors can move around so that the network topology may change over time; 3) physical transmission constraints, under which two sensors have to be within each other`s transmission range for communication; 4) the boundary effect of network fields; and 5) link unreliability, meaning that links are allowed to be unreliable. In contrast, many prior connectivity analyses of secure sensor networks often ignore the above issues. For resilience, although limited studies have presented formal analysis, it is often assumed that the adversary captures a random set of sensors, whereas their paper allows the adversary to capture an arbitrary set of sensors. A present conditions to ensure unassailability and unsplittability in secure sensor networks under the q-composite scheme. Unassailability ensures that an adversary capturing any set consisting of a negligible fraction of sensors can compromise only a negligible fraction of communication links although the adversary may compromise communications between non-captured nodes, which happen to use keys that are shared by captured nodes. Unsplittability means that when a negligible fraction of sensors are captured, almost all of the remaining nodes are still securely connected. Based on the results of connectivity, unassailability, and unsplittability , to provide useful guidelines for the design of secure sensor networks.

  Key-Words / Index Term
  Sequential Analysis, Replica Detection, Wireless Sensor Network, Node capture attack, Event-Based Attack Decomposition

  References
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  Citation
  K. Ravikumar, V. Manikandan, "Detection of Node Capture Attack in Wireless Sensor Networks," International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.4, pp.56-61, 2018