Volume-7 , Issue-2 , Feb - 2019 Go Back

Immediately IoT or Internet of things is a technology that deals with bringing control of physical devices over the Internet. Here I propose an efficient industry automation system that allows the user to efficiently fetch data from physical machines over the Internet. My system will analyze that particular data and determine the exact loss or profit of the company. For a demonstration of this system, I`m using 2 loads (temperature and count) as industrial appliances like temperature and counter. My main system takes the information and passes it to the Internet for further processing. The sensors and a microcontroller will be used for sending data from machine to system. Also, it displays the system state on an LCD display. Thus, we can automate the entire industry using online GUI for easy industry automation.

IOT, Temperature Sensor, IR Sensor, LCD Display

[1] Li Da Zu “Internet of Things in Industries: A Survey” IEEE Transactions on Industrial Informatics, Vol. 10, no. 4, November 2014.
[2] Sadeque Reza Khan Professor Dr. M. S. Bhat “GUI Based Industrial Monitoring and Control System” IEEE paper, 2014
[3] Ayman Sleman and Reinhard Moeller “Integration of Wireless Sensor Network Services into other Home and Industrial networks” IEEE paper
[4] Nilamadhab Mishra, “Internet of Everything Advancement Study in Data Science and Knowledge Analytic Streams”, International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.1, pp.30-36, Feb(2018)
[5] Rajeev Piyare and Seong Ro Lee “Smart Home-Control and Monitoring System Using Smart Phone” ICCA 2013, ASTL Vol. 24, pp. 83 - 86, 2013 © SEnXRSC 2013
[6] Jinsoo Han, Chang-Sic Choi, Wan-Ki Park, Ilwoo Lee “Green home energy management system through comparison of energy usage between the same kinds of home appliances” 2011 IEEE 15th International Symposium on Consumer Electronics
[7] S.d.t. Kelly, N.K. Suryadevara and S.C. Mukhopadhyay Towards “The Implementation of IoT for Environmental Condition Monitoring in Homes”, IEEE Paper 2013.

Mayuri Sewatkar, Ajitkumar Khachane, "Analysis of Report Generation of Daily Production in Manufacturing Industry using IoT," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.308-312, 2019.

Offline Handwritten Character Recognition is the trending application of computer vision in machine learning. Though a large amount of work has already been done in Handwritten Gurmukhi Character recognition, but still in a belief to get better accuracy with state of the art algorithm like deep convolution neural networks. Any character recognition process consists of five stages i.e. digitization, pre-processing, segmentation, feature extraction and Classifier. Feature Extraction is one of the significant stage in the process because extracted features of one character differentiate it from another character. In this paper, various techniques have been summarized which are used to extract the feature of digitized character image and various classifiers used mainly in character recognition.

Handwritten Gurmukhi Character Recognition, Feature Extraction, SIFT, Classification Methods, ConvNet

[1] Neeraj Kumar and Sheifali Gupta, “Offline Handwritten Gurmukhi character Recognition: A Review” International Journal of Software Engineering and its Applications, Vol. 10 No. 5, pp: 77-86, 2016
[2] Gita Sinha, Rajneesh Rani , Renu Dhir “Handwritten Gurmukhi Numeral Recognition using Zone based hybrid Feature Extraction Techniques” International Journal of Computer Applications Volume 47 - Number 21, 2012
[3] M. K. Mahto, K. Bhatia and R. K. Sharma,"Combined horizontal and vertical projection feature extraction technique for Gurmukhi handwritten character recognition," International Conference on Advances in Computer Engineering and Applications, Ghaziabad, 2015, pp. 59-65, 2015.
[4] Siddharth, Kartar & Jangid, Mahesh & Dhir, Renu & Rani, Rajneesh. “Handwritten Gurmukhi Character Recognition Using Statistical and Background Directional Distribution Features”, Internat. J. Computer Sci. Eng. (IJCSE). 3. 2332-2345, 2011.
[5] Ms. Seema A. Dongare , Prof. Dhananjay B. Kshirsagar , Ms.Snehal V . Waghchaure “Handwritten Devanagari Character Recognition using Neural Network” IOSR Journal of Computer Engineering (IOSR-JCE) Volume 16, Issue 2, PP 74-79, 2014
[6] Panyam Narahari Sastry, T.R.Vijaya Lakshmi, N. V. Koteswara Rao and Krishnan Rama Krishnan,” A 3D Approach for Palm Leaf Character Recognition Using Histogram Computation and Distance Profile Features”, Proceedings of the 5th International Conference on Frontiers in Intelligent Computing : Theory and Applications, Advances in Intelligent Systems and Computing 516, DOI 10.1007/978-981-10-3156-4_40, 2017.
[7] S. Arora, D. Bhattacharjee, M. Nasipuri, D. K. Basu, and M. Kundu, “Recognition of non-compound handwritten Devanagari characters using a combination of MLP and minimum edit distance,” International Journal of Computer Science and Security, vol. 4, no. 1, pp. 1–14, 2010.
[8] Salahat, E., Qasaimeh, M. “Recent advances in features extraction and description algorithms: a comprehensive survey” , IEEE International Conference on Industrial Technology (ICIT), pp. 1059 –1063 ,2017.
[9] M Shamim, Mohammad Badrul Alam Miah, Angona Sarker, Masud Rana, Abdullah Al Jobair S., “Handwritten Digit Recognition Using Machine Learning Algorithms” Global Journal of Computer Science and Technology, [S.l.], apr. 2018. ISSN 0975-4172.
[10] Alex KrizhevskyIlya SutskeverGeoffrey E. Hinton "ImageNet Classification with Deep Convolutional Neural Networks", NIPS 2012
[11] Singh, Pritpal & Budhiraja, Sumit.” Handwritten Gurmukhi Character Recognition Using Wavelet Transforms”,International Journal of Electronics, Communication & Instrumentation Engineering Research and Development (IJECIERD). 2. 27-37,2012.
[12] Kumar, M., Jindal, M.K. & Sharma, R.K. ,” A Novel Hierachical Technique for Offline Handwritten Gurmukhi Character Recognition” Natl. Acad. Sci. Lett. 37: 567. https://doi.org/10.1007/s40009-014-0280-1,2014
[13] Verma, K. & Sharma, R.K. Sādhanā.,” Recognition of Online handwritten Gurmukhi characters based on zone and stroke identification” 42: 701. https://doi.org/10.1007/s12046-017-0632-x , 2017
[14] G. Singh and M. Sachan, "Multi-layer perceptron (MLP) neural network technique for offline handwritten Gurmukhi character recognition", IEEE International Conference on Computational Intelligence and Computing Research, Coimbatore, pp. 1-5,2014.
[15] Pankaj Kale, Arti V. Bang, Devashree Joshi, “Recognition Of Handwritten Devanagari Characters Using Machine Learning Approach " , International Journal of Industrial Electronics and Electrical Engineering , Volume-3, Issue-9 ,Sep, 2015.
[16] A. Gaur and S. Yadav, "Handwritten Hindi character recognition using k-means clustering and SVM “,4th International Symposium on Emerging Trends and Technologies in Libraries and Information Services, Noida, 2015, pp. 65-70,2015
[17] Ashutosh Aggarwal , Karamjeet Singh, “Use of Gradient Technique for extracting features from Handwritten Gurmukhi Characters and Numerals”,Procedia Computer Science, Volume 46, PP 1716-1723, 2015
[18] Sukhpreet Singh and Renu Dhir. Article: “Recognition of Handwritten Gurmukhi Numeral using Gabor Filters”, International Journal of Computer Applications 47(1):7-11, June 2012
[19] Akm Ashiquzzaman and Abdul Kawsar Tushar, “Handwritten Arabic Numeral Recognition using Deep Learning Neural Network”,IEEE International Conference on Imaging, Vision & Pattern Recognition, At University of Dhaka, Dhaka, Bangladesh, 2017
[20] S. Acharya, A. K. Pant and P. K. Gyawali, "Deep learning based large scale handwritten Devanagari character recognition," 9th International Conference on Software, Knowledge, Information Management and Applications (SKIMA), Kathmandu, pp. 1-6,2015.
[21] Harpreet Kaur, Simpel Rani “Handwritten Gurumukhi Character Recognition Using Convolution Neural Network”, International Journal of Computational Intelligence Research Volume 13, Number 5 pp. 933-943, 2017.
[22] Zhang, D., & Lu, G.“Review of shape representation and description techniques”, 37, 1–19, 2004. doi:10.1016/j.patcog.2003.07.008
[23] Mamta Maloo, K.V.Kale, “Support Vector Machine Based Gujarati Numeral Recognition”, International Journal on Computer Science and Engineering (IJCSE) Vol. 3 No. 7 pp: 2595-2600,2011.
[24] Aditi Goel, Saurabh Kr. Srivastava. “Role of Kernel Parameters in Performance Evaluation of SVM", 2016 Second International Conference on Computational Intelligence & Communication Technology (CICT), 2016
[25] Lowe, D.G.”Distinctive Image Features from Scale-Invariant Keypoints” , International Journal of Computer Vision 60: 91, 2004 . https://doi.org/10.1023/B:VISI.0000029664.99615.94
[26] Saha Sourajit, Nisha Saha, "A Lightning fast approach to classify Bangla Handwritten Characters and Numerals using newly structured Deep Neural Network", Procedia Computer Science, vol. 132, pp. 1760-1770, 2018.
[27] Lele, N.S., “Image Classification Using Convolutional Neural Network”, International Journal of Scientific Research in Computer Science and Engineering. 6. 22-26. 10.26438/ijsrcse/v6i3.2226,2018.
[28] Gangania, Priti & Mishra, Sowmya & Garg, Shreshtha & Agarwal, Sonam.,” Handwriting Recognition System Using Optical Character Recognition”, International Journal of Scientific Research in Computer Science and Engineering. 6. 18-21. 10.26438/ijsrcse/v6i3.1821,2018.

Sonia Flora, Parth Goel, Anju Kakkad, "A Survey on Feature Extraction Methods & Classifiers for Handwritten Gurmukhi Character Recognition," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.313-320, 2019.

Encryption becomes a solution and different encryption techniques which roles a significant part of data security on cloud. Encryption algorithms is to ensure the security of data in cloud computing. Because of a few limitations of pre-existing algorithms, it requires for implementing more efficient techniques for public key cryptosystems. ECC (Elliptic Curve Cryptography) depends upon elliptic curves defined over a finite field. ECC has several features which distinguish it from other cryptosystems, one of that it is relatively generated a new cryptosystem. Several developments in performance have been found out during the last few years for Galois Field operations both in Normal Basis and in Polynomial Basis. On the other hand, there is still some confusion to the relative performance of these new algorithms and very little examples of practical implementations of these new algorithms. Efficient implementations of the basic arithmetic operations in finite fields GF(2m) are need for the applications of coding theory and cryptography. The elements in GF(2m) know how to be characterized in a choice of bases. A variety of basis used to represent field elements has a major impact on the performance of the field arithmetic. The multiplication techniques that make use of polynomial basis representations are very efficient in comparison to the best techniques for multiplication using the other basis representations. In this paper, focuses on user confidentiality and security protection in cloud, and that uses enhanced ECC technique over Galois Field GF(2m). The strong point of the proposed ECPC (Elliptical Curve and Polynomial Cryptography) algorithm is based on the complexity of computing discrete logarithm in a large prime modulus, and the Galois Field allows mathematical operations to mix up data effectively and easily. This technique is mainly used to encrypting and decrypting data to ensure security protection and user confidentiality in the cloud computing. Results show that the performance of ECPC over Galois Field, in two region of evaluation, it has better than the other technique that is used for comparison purpose.

Cloud Computing, Security, Cryptography, ECC, RSA, ECDH, ECDSA, Elliptical Curve and Polynomial Cryptography (ECPC)

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[2]. Wolf Halton, Opensource and security on “Security Issues and Solutions in Cloud Computing”, July 25, 2010, wolf in cloud computing, Tech Security.
[3]. L. Arockiam, S. Monikandan « Data Security and Privacy in Cloud Storage using Hybrid Symmetric Encryption Algorithm » International Journal of Advanced Research in Computer and Communication Engineering Vol. 2, Issue 8, August 2013
[4]. Ponemon Institute and CA “Security of cloud computing Users: A study of Practitioners in the US & Europe”. May 12, 2010.
[5]. Ryan K L Ko, Peter Jagadpramana, Miranda Mowbray, Siani Pearson, Markus Kirchberg , Qianhui Liang , Bu Sung Lee, “TrustCloud: A Framework for Accountability and Trust in Cloud Computing” 2011 IEEE World Congress on Services.
[6]. Muhammad Rizwan Asghar, Mihaela Ion, Bruno Crispo, “ESPOON Enforcing Encrypted Security Policies in Outsourced Environment”, 2011 Sixth International Conference on Availability, Reliability and Security.
[7]. Xu Huang, Pritam Gajkumar Shah and Dharmendra Sharma, “Multi-Agent System Protecting from Attacking with Elliptic Curve Cryptography,” the 2nd International Symposium on Intelligent Decision Technologies, Baltimore, USA, 28-30 July 2010.
[8]. Xu Huang, Pritam Shah, and Dharmendra Sharma, “Minimizing hamming weight based on 1‟s complement of binary numbers over GF (2m),” IEEE 12th International Conference on Advanced Communication Technology, Phoenix Park, Korea Feb 7-10, 2010. ISBN 978-89-5519-146-2, pp.1226-1230.
[9]. Xu Huang, Pritam Shah, and Dharmendra Sharma, “Fast Algorithm in ECC for Wireless Sensor Network,” The International MultiConference of Engineers and Computer Scientists 2010, Hong Kong, 17-19 March 2010. Proceeding 818-822.
[10]. Pritam Gajkumar Shah, Xu Huang, Dharmendra Sharma, “Analytical study of implementation issues of elliptical curve cryptography for wireless sensor networks,” The 3rd International Workshop on RFID & WSN and its Industrial Applications, in conjunction with IEEE AINA 2010, April 20-23, 2010, Perth, Australia.
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[15]. Setiadi, I., Kistijantoro, A.I. and Miyaji, A. (2015) Elliptic Curve Cryptography: Algorithms and Implementation Analysis over Coordinate Systems. 2015 2nd International Conference on Advanced Informatics: Concepts , Theory and Applications, Chonburi, 19-22 August 2015, 1-6. https://doi.org/10.1109/icaicta.2015.7335349
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[19]. Setiadi, I., Kistijantoro, A.I. and Miyaji, A. (2015) Elliptic Curve Cryptography: Algorithms and Implementation Analysis over Coordinate Systems. 2015 2nd International Conference on Advanced Informatics: Concepts , Theory and Applications, Chonburi, 19-22 August 2015, 1-6. https://doi.org/10.1109/icaicta.2015.7335349

D.Pharkkavi, D. Maruthanayagam, "Time Complexity Analysis of Cloud Data Security: Elliptical Curve and Polynomial Cryptography," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.321-331, 2019.

Cognitive Radio is a technology that overcomes the problem of spectrum shortage by embedding the wireless devices with an intelligent agent to make the opportunistic use of available white spaces in the radio environment. However due to ubiquitous nature of cognitive radio networks, it is sensitive to a number of security threats which disturbs the overall performance of cognitive radio networks. The main goal of this paper is to thwart one of the security threats in Cognitive Radio Networks known as Primary User Emulation Attack. Primary User Emulation Attack is one of the most popular Dynamic Spectrum Access attack. In this paper we are detecting the primary user emulation attack based on TDOA values using Grey Wolf Optimizer. Simulation results show that Grey Wolf Optimizer is more accurate than using the Particle Swarm Optimization Algorithm for mitigation.

Cognitive Radio Network;Primary User Emulation Attack;Grey Wolf Optimizer;Particle Swarm Optimization;Dynamic Spectrum Access; Time Difference Of Arrival

[1] Beibei Wang and K. J. Ray Liu, “Advances in Cognitive Radio Networks: A Survey,” IEEE Journal of Selected Topics in Signal Processing, vol. 5, no. 1, February 2011.
[2] Rajesh K. Sharmaand Danda B. Rawat, “Advances on Security Threats and Countermeasuresfor Cognitive Radio Networks: A Survey” IEEE Communications Surveys & Tutorials.
[3] T. Charles Clancy, Nathan Goergen, “Security in cognitive radio networks: threats and mitigation,” 3rd International Conference on Cognitive Radio Oriented Wireless Networks and Communications, 2008. Crown Com 2008, 2008, pp 1–8 (IEEE).
[4] H. Srikanth Kamath, L. M. Schalk, “Primary User Localization Schemes in Cooperative Sensing,” International Journal of Computer Applications (0975 8887) Volume 105 - No. 13, November 2014.
[5] K.Challapali, S. Mangold, and Z. Zhong, “Spectrum agile radio:Detecting spectrum opportunities,” Proceedings of the 6th AnnualInternational Symposium on Advanced Radio Technologies, March 2004.
[6] M.P.Olivieri, G. Barnett, A.Lackpour, A. Davis, and P. Ngo, “A scalable dynamic spectrum allocation system with interference mitigation forteams of spectrally agile software defined radios,”Proceedings of theIEEE International Symposium on New Frontiers in Dynamic SpectrumAccess Networks, November 2005.
[7] D. Cabric, S. M. Mishra, and R. W. Brodersen, “Implementation issues inspectrum sensing for cognitive radios,” Proceedings of the Thirty-eightAsilomar Conference on Signals, Systems, and Computers, November2004.
[8] L.P.Goh, Z.Lei, and F Chin, “Dvb detector for cognitive radio,” Proceedings of the International Conference on Communications, page 64606465, Glasgow, Scotland, June 2007.
[9] Y.Qi, T.Peng, W.Wang, and R.Qian, “ Cyclostationarity-based spectrumsensing for wideband cognitive radio,” Proceedings of the 2009 WRIInternational Conference on Communications and Mobile Computing,page 107111, Washington, DC, USA, 2009.
[10] W.Xia, S.Wang, W.Liu, and W.Cheng, “Correlation-based spectrum sensing in cognitive radio,” Proceedings of the 2009 ACM Workshop on Cognitive Radio Networks, page 6772, New York, NY, USA, 2009.
[11] H Li and Z Han, “Dogfight in spectrum: Combating primary useremulation attacks in cognitive radio systems, part i: Known channel statistics,”IEEE Transactions on Wireless Communications, 9(11):3566–3577, 2010.
[12] Husheng Li and Zhu Han, “Dogfight in Spectrum: Combating Primary User Emulation Attacks in Cognitive Radio Systems-Part II: Unknown Channel Statistics,” IEEE Transactions on Wireless Communications, Vol. 10, No. 1, January 2011.
[13] S Anand, Z Jin, and KP Subbalakshmi, “An analytical model for primary user emulation attacks in cognitive radio networks,”Proceedingsof IEEE Symposium on New Frontiers in Dynamic Spectrum AccessNetworks, Chicago, IL, USA, October 2008.
[14] Z Jin, S Anand, and KP Subbalakshmi, “Detecting primary user emulationattacks in dynamic spectrum access networks,” Proceedings of IEEE International Conference on Communications, Dresden, Germany, June2009.
[15] Z Jin, S Anand, and KP Subbalakshmi, “Mitigating primary useremulation attacks in dynamic spectrum access networks using hypothesis testing,” ACM SIGMOBILE Mobile Computing and Communications
Review, 13(2):74–85, 2009.
[16] Z Jin, S Anand, and KP Subbalakshmi, “Performance analysis of dynamicspectrum access networks under primary user emulation attacks,” Proceedings of IEEE Global Telecommunications Conference, Miami,FL, USA, December 2010.
[17] Zituo Jin,“Primary user emulation attack in dynamic spectrum accessnetworks: threats, mitigation and impact,” Licentiate dissertation, StevensInstitute of Technology, Hoboken, NJ, May 2012.
[18] Peng Ning Yao Liu and Huaiyu Dai,“Authenticating primary users’ signals in cognitive radio networks via integrated cryptographic and wireless link signatures,”Proceedings of the IEEE Symposium on Security and Privacy, Oakland, CA, May 2010.
[19] R.Chen, J.M.Park, and J.H. Reed,“Defense against primary useremulation attacks in cognitive radio networks,”IEEE Journal on SelectedAreas in Communications Special Issue on Cognitive Radio Theory andApplications, 2008.
[20] Olga Leon, Juan Hernandez-Serrano, Miguel Soriano, “Cooperative Detection of Primary User Emulation Attacks in CRNs,” Computer Networks, Elsevier, Vol. 56, pp. 3374-3384, Sep. 2012.
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[23] Walid R. Ghanem, Mona Shokair and Moawad I. Desouky, “An improved Primary User Emulation Attack Detection in Cognitive Radio Networks Based on Firefly Optimization Algorithm,” 2016, 33rd National Radio Science Conference (NRSC 2016), Feb 22‐25, 2016, Aswan, Egypt.
[24] Seyedali Mirjalili, Seyed Mohammad Mirjalili and Andrew Lewis, “Grey Wolf Optimizer,” Advances in Engineering Software 69 (2014) 46–61, Elsevier.

Aasia Rehman, "Detection Of Primary User Emulation Attack in Cognitive Radio Networks Based On TDOA using Grey Wolf Optimizer," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.332-337, 2019.

Plant diseases takes place when an organism infects a plant and disrupts its normal growth habits. Diseases have many cause including fungi, bacteria and viruses. Fungi are identified mostly from their morphology, with importance placed on their reproductive structures. Bacteria are measured more primitive than fungi and usually have simpler life cycles. With few exceptions, bacteria are as single cells and increase in numbers by dividing into two cells during a process called binary fission. Viruses are tremendously tiny particles consisting of protein and genetic material with no related protein. The term disease is usually used only for the damage of live plants. Detection of these symptoms with visual aid is matter of time and inconsistent results. Even the experts from related areas have found this visual approach of detection to be erroneous. So by using image processing techniques and machine learning algorithms we can detect and classify diseases of plants.

agricultural science, image processing, machine learning, classification, disease detection and classification

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[2] Mokhtar, Usama, et al. "Tomato leaves diseases detection approach based on support vector machines", Computer Engineering Conference (ICENCO), 2015 11th International. IEEE, 2015,pp 978-1-5090-0275-7/15.
[3] Mondal, Dhiman, et al. "Detection and classification technique of yellow vein mosaic virus disease in okra leaf images using leaf vein extraction and Naive Bayesian classifier.", Soft Computing Techniques and Implementations (ICSCTI), 2015 International Conference on. IEEE, 2015.
[4] Ganatra, Patel, et al. “A Survey on Diseases Detection and Classification of Agriculture Products using Image Processing and Machine Learning”, International Journal of Computer Application, 2018.
[5] Mohanaiah, P. P. Sathyanarayana, and L. GuruKumar. "Image texture feature extraction using GLCM approach", International Journal of Scientific and Research Publications3.5 (2013)
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[7] Mukesh Kumar Tripathi, Dr. Dhananjay D. Maktedar, “Recent Machine Learning Based Approaches for Disease Detection and Classification of Agricultural Products”, International Conference on. IEEE, 2016.
[8] Vijay Borate, Sheetal Patange, et.al. “Plant Leaf Disease detection using Machine Learning”, IJARIIE- ISSN (O) - 2395-4396.

Shraddha Tadmare, Bodireddy Mahalakshmi, "A Survey on Plant Disease Detection and Classification Using Different Machine Learning Algorithms," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.338-341, 2019.

Cloud Technology is an emerging technology that has improved the performance of many organizations by utilizing minimum resources and maximum outcomes. Cloud provides virtualized services, applications and can store a large amount of data from various locations. As the cloud environment is accessed through Internet, it cannot be trusted blindly. Thus security is considered as major barrier for users to adopt Cloud, where threats are considered as the major reason for the degradation of the quality of services. For effective use of cloud services, individual focus on the cloud threats is required and an approach is needed from the end user side to gain knowledge about various threats pertaining inside a cloud infrastructure. In the cloud deployment process, various network protocols are used to establish the connectivity between the infrastructure, services and clients. As a result, the server-end needs to be enough strong to provide security to network transmission. However, still the invader secretly accesses the transaction and modifies the communication between two parties. This invader gives birth to most common and critical Man-in-the-Middle (MiTM) attack. The aim of the paper is to re-examine ‘Man-in-the-Middle’ attack and its root causes. The focus is to present a broad indication on ‘Man-in-the-Middle’ attack, rising as an imperative security concern in cloud computing. The research study aims to review the previous literature and to emphasis on conclusive findings for future research in the related domain based on the published work and industry/organization reports.

Cloud Computing, Cloud Security, Cloud Threats, Man-in-the-Middle Attack, MiTM

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Vaishali Singh, Kavita Bhatia, S. K. Pandey, "Revisiting Cloud Security Threats: Man-in-the-Middle Attack," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.342-348, 2019.

Today’s world needs most of the things would be automated, the machine would be able to do itself everything by its own, from initiation of job to till the decision-making capabilities. Similarly the Security of the data needs the automated, self-decision and self-protective proactive mechanisms. Since, the existing tools and mechanisms are semi-automated still it needs the human intervention to update and configure the security layers and features. This paper gives the summarized view of the existing work carried out so far in the area of data security – data leakage detection and prevention.

Data, Detection, Dynamic, Leakage, Prevention, Protection, Sensitive, Self-protective, Self-configurable

[1] Carlie Carlett, et. Al, “A Scientific Research and Development Approach to Cyber Security”, report submitted to Department of Energy, Dec 2008.
[2] Eric Yuan, et. Al, “A Systematic Survey of Self-Protecting Software Systems”, ACM, 1539-9087/2010/03-ART39.
[3] Neeraj Kumar, et. Al, “Detection of Data Leakage in Cloud Computing Environment”, Sixth International Conference on Computational Intelligence and Communication Networks, 978-1-4799-6929-6/2014,IEEE
[4] Marco Pistoia Omer Tripp, et. Al.,” Labyrinth: Visually Configurable Data-leakage Detection in Mobile Applications”, 16th IEEE International Conference on Mobile Data Management, 2015.
[5] Sherry Zhu, An Efficient Data Leakage Prevention Framework for Semiconductor Industry, Proceedings of the 2016 IEEE IEEM, 978-1-5090-3665-3/16/$31.00 ©2016 IEEE
[6] Becker et. Al., Real-Time Data Leakage Prevention and Reporting, 21st September 2017, US Patent publication No. US 2017/0270310 A1
[7] Min-JiSeo, et. Al., An advanced data leakage detection system analyzing relations between data leak activity, International journal of Applied Engineering Research ISSN 0973-4562 Volume 12, No.21,2017, pp 11546-11554
[8] Neha Ramteke, et. Al., Review paper on prevention of data leaks using 3DES Encryption, International journal of innovations & advancement in computer science, IJIACS, ISSN 2347-8616, Vol 6, Issue 11, Nov 2017.
[9] YavuzCanbay, A Turkish language based data leakage prevention system, 2017 5th International Symposium on Digital Forensic and Security (ISDFS), 978-1-5090-5835-8/17/$31.00 ©2017 IEEE
[10] Sarker T. Ahmed Rumee, et. Al., “MirrorDroid :A Framework to Detect Sensitive Information Leakage in Android by Duplicate Program Execution”, 978-1-5090-4780-2/17, IEEE, 2017
[11] Michael D Nelson, Data Leak Protection, 10 May 2018, US Patent Publication, us 2018/0131674 A1
[12] Shivakumara T, et. Al, “To incorporate value-added security features into current data for outsourcing cloud data applications through Secured Access Control and Assured Deletion”, International Journal of Emerging Trends & Technology in Computer Science (IJETTCS), Volume 7, Issue 3, May-June 2018 , ISSN 2278-6856

Shivakumara T, Rajshekhar M Patil, Muneshwara M S, "Review Paper on Dynamic Mechanisms of Data Leakage Detection and Prevention," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.349-358, 2019.

The utilization of cloud condition is developing step by step. The private ventures are utilizing cloud for their everyday need of assets since cloud give on interest and pay per use administrations. The business which is of low spending plan and not be ready to setup wide foundation for late innovations, Distributed computing is favouring for them. As the need increments, overseeing load at cloud is the greatest test that the cloud supplier has. Conveying meet load in various hub which might be topographically at various area is serious issue. Different load adjusting calculations are there for even dissemination of load. Again stack adjusting will enhance the parameters like cost, reaction time, through put and so forth. Too Load adjusting is a major perspective as far as power use what`s more, asset use.

Cloud Computing, Round Robin, Throttled, K-mean, Firefly, Distributed

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Alok Kumar, Shivani Rohilla, Manish Bhardwaj, "Analysis of Cloud Computing Load Balancing Algorithms," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.359-362, 2019.

Hypothetical Data security is the most troublesome issue on the planet and the diverse security risks in the computerized security must be avoided and to give more prominent protection to the customers additionally, to engage high dependability and openness of the data. For the equivalent the assurance of the proper data encryption estimation depends consistently on its key length, data size and its execution criteria. In this paper, we separated the distinctive data encryption figuring for instance, DES, AES, blowfish, MD5 estimations on the reason of the diverse parameters and made a comparison of these counts for secure trade of EHR.

EHR, Performance Analysis, Algorithm

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[2]. Pradeep Deshmukh “Design of cloud security in the EHR for Indian healthcare services Journal of King Saud University Computer and Information Sciences” Production and hosting by Elsevier B.V. This is an open access article 2017.
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[11]. Niraj Bachhav, Swapnil Biradar, Mayank Mishra, Priti Purbey, Monali P. Deshmukh, “A Construction for Secret Sharing Scheme with General Access Structure”, International Journal of Innovative Research in Science, Engineering and Technology’, Vol. 5, Issue 5, May 2016
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[13]. Pundkar, S. N. and Shekokar, N, “Cloud computing security in multi-clouds using Shamir’s secret sharing scheme”, International Conference on Electrical, Electronics, and Optimization Techniques’(ICEEOT), pp. 392–395, 2016
[14]. Hossain, M. A., Hossain, M. B., Uddin, M. S., and Imtiaz, S. M, “Performance Analysis of Different Cryptography Algorithms”, International Journal of Advanced Research, Computer Science and Software Engineering, 2016.

Shraddha M. Dudhani, Santosh S. Lomte, "Performance Analysis of Data Encryption Algorithms for Secure EHR Transmission," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.363-366, 2019.

Big data and IoT is two different challenging terms in IT industry. Currently, IoT is an emerging technology and is being used various application for development and research. Big Data Stream Computing (BDSC) is an emerging feature for dealing a real-time data streams and providing faster decisions. BDSC is being used in much of the real time IOT applications. The main objective of the work is to review and measure the performance analysis of real-time IoT application data processing using BDSC platform.

IOT, Big Data, BDSC

[1] SWDS Li, J., Bao, Z. and Z. Li, “Modeling Demand Response Capability by Internet Data Centers Processing Batch Computing Jobs”, IEEE Trans. on Smart Grid, Vol. 6, No. 2, pp. 737–747, 2015.
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[3] Liu, X., N. Iftikhar and X. Xie, “Survey of Real-Time Processing Systems for Big Data”, 18th Int. Database Engineering and Applications Symposium, New York, pp. 356–361, USA, 2014
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[6] Singh, K. and R. Kaur, “Hadoop: Addressing Challenges of Big Data”, 2014 IEEE Int. Advance Computing Conf., Navi Mumbai, pp. 686-689, India, 2014.
[7] Sinnen, O., A. Tok and M. Khan, “Contention-aware Scheduling with Task Duplication”, J. Parall. Distr. Comput., Vol. 71, No. 1, pp. 77–86, 2011.
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[11] 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, Issue.3, pp.81-85, 2018
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[13] Shilpa Manjit Kaur, “BIG Data and Methodology- A review” ,International Journal of Advanced Research in Computer Science and Software Engineering, Volume 3, Issue 10, October 2013.

Ajay Kumar Bharti, Rashmi Negi, Deepak Kumar Verma, "A Review on Performance Analysis and Improvement of Internet of Things Application," International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.367-371, 2019.