Volume-07 , Special Issue-14 , May - 2019 Go Back

The lack of adopting new technology in agriculture directly impact on countries GDP, to enhance agro products need to adopt IOT based techniques to increase crop survey, crops status, managing water resources etc. Internet of Things has more potential and transfers the way we do traditional agriculture into smart agriculture. The increasing global population will cross 12 billion by 2015, so, in order to feed food to this huge population the agriculture need to be automating using smart system such as IoT based systems and components. The demand of food has to meet many challenges in future such as change in climate, weather conditions, environment changes impact result into intensive farming practices, smart agriculture farming through IoT based system helps to farmers to reduce human manpower resources and generated very less agriculture waste. And enhance the crops productivity. IoT Based smart farming is a hi-tech system of growing crops using tools and techniques and automated systems. It uses ICT base hardware tools, applications and advance techniques in agriculture. To incorporate IoT based farming need to use of sensors, automated vehicle, automated hardware systems, control units etc. are key components of this system.

IoT,ICT,IT,GDP,Automated Hardware

[1]Carlos Driemeier, Liu Yi Ling, Angélica O. Pontes, Guilherme M. Sanches, Henrique C. J. Franco “Data Analysis Workflow for Experiments in Sugarcane Precision Agriculture” in 2014 IEEE 10th International Conference on eScience
[2] LiangYu, Luo Yongjun “A Research and Practice for Sugarcane Area`s Farm Management Information Service Platform” in 2010 International Conference on Computer Application and System Modeling (ICCASM 2010)
[3] Moises Alencastre Miranda, Joseph R. Davidson, Richard M. Johnson “Robotics for Sugarcane Cultivation: Analysis of Billet Quality using Computer Vision” in 3828 IEEE ROBOTICS AND AUTOMATION LETTERS, VOL. 3, NO. 4, OCTOBER 2018
[4] P.Vijaya Bhaskar Redd A.Rama Mohan Reddy “Content based image indexing and retrieval using directional local extrema and magnitude patterns” In AEU-International Journal of Electronics and Communications 68 (7), 637-643
[5] Seokhoon Jeong , Yong Min Lee and Sangjoon Lee “ Development of an automatic sorting system for fresh ginsengs by image processing techniques” Jeong et al. Hum. Cent. Comput. Inf. Sci. (2017) 7:41
[6] Yoon-Ki Kim, Yongsung Kim and Chang-Sung Jeong “RIDE: real-time massive image processing platform on distributed environment” Kim et al. EURASIP Journal on Image and Video Processing (2018) 2018:39
[7]Xavier P.Burgos-Artizzu AngelaRibeiro AlbertoTellaeche GonzaloPajarescCesarFernández-Quintanilla “Analysis of natural images processing for the extraction of agricultural elements” Image and Vision Computing Journal Volume 28, Issue 1, January 2010, Pages 138-149
[8] Liang Yu and Luo Yongjun “ A research and Practice for sugarcane Area’s farm management Information service platform”: 978-1-4244-7237 @2010 IEEE , international conference ICCASM 2010

Anilkumar Hulsure, P.V. Bhaskar Reddy, "Research and Survey Practice for sugarcane farming using Internet of Things (IOT)", International Journal of Computer Sciences and Engineering, Vol.07, Issue.14, pp.432-435, 2019.

Utilization of credit cards encourages individuals to buy products online via the Internet. Individuals tend to do much of purchasing online or offline by utilizing the credit card facility provided by the bankers to their customers. Credit cards have turned out to be the most prominent facility available to the people around the globe to encourage paperless trades at an enormous speed. Whenever any such trade happens in exchanges or net marketing by using a paperless framework, it is subjected under high risk of fraudulent transactions due to many pitfalls in the security system of the usage of credit cards on the networks. This paper presents a brief survey of important and basic linear and non-linear machine learning algorithms that are focused to predict the fraudulent transactions by studying the patterns present in the credit card transactional datasets. The authors provide the methodology of Random Forest (RF), Support Vector machine (SVM) and Artificial Neural Network (ANN) classifiers to accurately classify whether a unseen credit card transaction is fraudulent or not.

Credit Card Fraud Detection, Random Forest, Support Vector Machine, Artificial Neural Networks

[1] Chavan, J., 2013. Internet banking-benefits and challenges in an emerging economy. International Journal of Research in Business Management, 1(1), pp.19-26.
[2] Al Hasib, A., 2009. Threats of online social networks. IJCSNS International Journal of Computer Science and Network Security, 9(11), pp.288-93.
[3] Resnick, P. and Zeckhauser, R., 2002. Trust among strangers in Internet transactions: Empirical analysis of eBay`s reputation system. In The Economics of the Internet and E-commerce (pp. 127-157). Emerald Group Publishing Limited.
[4] Franklin, J., Perrig, A., Paxson, V. and Savage, S., 2007, October. An inquiry into the nature and causes of the wealth of internet miscreants. In ACM conference on Computer and communications security (pp. 375-388).
[5] Özkan, S., Bindusara, G. and Hackney, R., 2010. Facilitating the adoption of e-payment systems: theoretical constructs and empirical analysis. Journal of enterprise information management, 23(3), pp.305-325
[6] Minelli, M., Chambers, M. and Dhiraj, A., 2012. Big data, big analytics: emerging business intelligence and analytic trends for today`s businesses. John Wiley & Sons.
[7] Davenport, T. and Harris, J., 2017. Competing on Analytics: Updated, with a New Introduction: The New Science of Winning. Harvard Business Press.
[8] Bhattacharyya, S., Jha, S., Tharakunnel, K., and Westland, J. C. (2011). Data burrowing for Mastercard deception: A close report. Decision Support Systems, 50(3), 602613. Elsevier B.V.
[9] Rahul Johari and shalini gupta" A New Framework for Credit Card Transactions including Mutual Authentication among Cardholder and Merchan 978-0-7695-4437-3/11 $26.00 © 2011 IEEE DOI 10.1109/CSNT.2011.12
[10] Mahmoud Reza Hashemi and Leila Seyedhossein" Mining Information from Credit Card Time Series for Timelier Fraud Detection" 978-1-4244-8185-9/10/$26.00 ©2010 IEEE
[11] M.Kavitha and Dr.M.Suriakala `Constant Credit Card Fraud Detection on Huge Imbalanced Data utilizing Meta-Classifiers`978-1-5386-4031-9/17/$31.00 ©2017 IEEE
[12] M.Kavitha and Dr.M.Suriakala `Constant Credit Card Fraud Detection on Huge Imbalanced Data utilizing Meta-Classifiers`978-1-5386-4031-9/17/$31.00 ©2017 IEEE
[13] Mohammed Ibrahim Alowais and lay-ki-soon` Credit Card Fraud Detection: Personalized or Aggregated Model` 978-0-7695-4727-5/12 $26.00 © 2012 IEEE DOI 10.1109/MUSIC.2012.27
[14] Chaitanya Ghorpade and ankit Mishra `Charge card Fraud Detection on the Skewed Data Using Various Classification and Ensemble Techniques` 978-1-5386-2663-4/18$31.00 c 2018 IEEE
[15] Mrs.Vimala Devi. J , Dr.Kavitha,K.S "Misrepresentation Detection in Credit Card Transactions by utilizing Classification Algorithms" 978-1-5386-3243-7/17/$31.00 ©2017 IEEE
[16] References: Shukur, H.A. and Kurnaz, S., 2019. Credit Card Fraud Detection using Machine Learning Methodology.
[17] An Evaluation of Computational Intelligence in Credit Card Fraud Detection Mohammad Sultan Mahmud Department of Computer Science and Engineering World University of Bangladesh Dhaka-1205, Bangladesh 978-1-4673-8139-0/16/$31.00 ©2016 IEEE
[18] Djeffal Abdelhamid1 , Soltani Khaoula1 , Ouassaf Atika2 Automatic Bank Fraud Detection Using Support Vector Machines Proceedings of the International conference on Computing Technology and Information Management, Dubai, UAE, 2014
[19] References: A Comparison of Machine Learning Techniques for Credit Card Fraud Detection Lusis April 20, 2017
[20] Mrs.Vimala Devi. J , Dr.Kavitha,K.S Cambridge Institute of Technology, Global Academy of Technology, Bangalore-98 Fraud Detection in Credit Card Transactions by using Classification Algorithms 978-1-5386-3243-7/17/$31.00 ©2017 IEEE
[21] International Journal of Applied Engineering Research ISSN 0973-4562 Volume 13, Number 24 (2018) pp. 16819-16824 © Research India Publications. http://www.ripublication.com 16819 Machine Learning For Credit Card Fraud Detection System Lakshmi S V S S1 Selvani Deepthi Kavila2
[22] Credit Card Fraud Detection on the Skewed Data Using Various Classification and Ensemble Techniques Ankit Mishra MANIT Bhopal, Bhopal, Madhya Pradesh , 2018 IEEE International Students’ Conference on Electrical, Electronics and Computer Science
[23] Cost Sensitive Modeling of Credit Card Fraud Using Neural Network Strategy Fahimeh Ghobadi Computer Engineering Department Islamic Azad University South Tehran Branch Tehran, Iran Ghobadi.Fahimeh@Gmail.com 978-1-5090-5820-4/16/$31.00 ©2016 IEEE
[24] Cluster Analysis and Artificial Neural Networks A Case Study in Credit Card Fraud Detection Emanuel Mineda Carneiro, Luiz Alberto Vieira Dias; Adilson Marques da Cunha 978-1-4799-8828-0/15 $31.00 © 2015 IEEE DOI 10.1109/ITNG.2015.25
[25] Analysis on Credit Card Fraud Detection Methods 1 S. Benson Edwin Raj, 2A. Annie Portia 978-1-4244-9394-4/11/$26.00 ©2011 IEEE
[26] Proceedings of the Seventh International Conference on Machine Learning and Cybernetics, Kunming, 12-15 July 2008 978-1-4244-2096-4/08/$25.00 ©2018 IEEE 3630 NEURAL DATA MINING FOR CREDIT CARD FRAUD DETECTION TAO GUO, GUI-YANG LI
[27] Improved Fraud Detection in e-Commerce Transactions Jisha Shaji PG Scholar Department of Computer Engineering St. Francis Institute of Technology, Mumbai, India 978-1-5090-4381-1/17/$31.00 © 2017 IEEE
[28] Wells, A.J., 2019. Cyber-Security Incidents and Organizational Policies in Healthcare (Doctoral dissertation, Northcentral University).

Vidyashree V, Akram Pasha, Udayarani V, Vinay Kumar M, "Machine learning Classifiers for Credit Card Fraud Detection: A Brief Survey", International Journal of Computer Sciences and Engineering, Vol.07, Issue.14, pp.436-442, 2019.

In emerging developing countries such as India, companies heavily rely on their human workforce for services. That is why employee performance management at the individual level is must and the business case for implementing a system to measure and improve employee performance should be strong. The concept of the project is: Today majority of the giant retail companies are facing a lot of issues in their current assessment planning of their employees. This wrong assessment planning leads to employees not being used to the fullest potential which causes loss to businesses and major capital loss in man hours, also this assessment planning requires a lot of manual strategies which are very costly and hence these assessment strategies then turn out to be costly, time taking, biased and working on mostly non relevant data. We used the Machine learning classification technique for the extraction of knowledge significant for predicting employee performance using a .csv file sourced from (INX Future Inc.).

Employee Performance Analysis,SVM, Machine Learning, Algorithm K-NN algorithm, random forest

[1]. Al-Radideh, Q.P., Al-Naagi, E., (2011). Machine learning used for Classification Model for Employees Performance prediction, International Journal of Advanced sciences of computer and Applications, 3(2), pp 144 – 151
[2]. Anchetra, R.A, Cabautan, R.J.M., Lorenaa, B.T.T., Rabagoo, W., (2010). Predicting faculty trainings and performance development using rule-based algorithm classification, Asean Journal of Science of Computer and Information Technology 2: 7, pp 204 – 208.
[3]. heine, C.F., Chen, L.F., (2008). Learning machine to improve employee selection and increase human input: A case study in technology industry, Expert applications on system, 34(1), pp 283–293
[4]. Delaavari, N., Phon-Amnnuaisuk S., (2009). Machine learning Application in Higher Learning Institutions, education on informatics, 7(1), pp. 32–55
[5]. Hamidah J., Abdul R.H., Zulaiha A.O., (2010). Discover Knowledge Techniques for Talent Forecasting in employee Resource Applications, World Academy of Engineering, Science and Technology.
[6]. Jantan, H., Hamdan, A. R., Othman, Z. A. (2011). applying Machine learning techniques for talent management. International Conference on Engineering of Computer and Applications IPCSIT Vol. 3.
[7]. Jantan, H., Hamdan, A. R., & Othman, Z. A. (2010). resource talent prediction in HRM using C4. 5 classification algorithm, International Journal on Science Computer and Engineering, 2(08-2011), pp 2525-2535.
[8]. Jantan, H., Hamdana, A. R., Othman, Z. A. (2008). Knowledge discovery techniques for talent forecasting in human resource application. World Academy of Science, Engineering and Technology, Penang, Malaysia, pp 803.
[9]. Jayanthi R., D.P. Goyal, S.I Ahson, (2008). Data techniques of Mining for better decisions in human resource systems, International Journal of Business Information Systems, 3(6), pp 468 – 484
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Archana Boob, Sandeep Sharma, Saurabh Singh, Rafsan Ali, "Application of Machine Learning in Employee Performance Prediction", International Journal of Computer Sciences and Engineering, Vol.07, Issue.14, pp.443-447, 2019.

This paper outlines state-of-the-art solution for landmine detection that facilitates the detection of mine or possible explosives that are hidden. The latitude and longitude positions are then sent to the controller via SMS making it easier to locate and further to diffuse it as well. The major components used are Arduino Uno Board, HC-05 Bluetooth module, GSM SIM900A and copper coil. The result shown from this state-of-the-art proposed system provides better security and safety mechanism for the soldiers of our country.

Arduino Uno, HC-05 Bluetooth, GPS, GSM, L29N Motor Driver, Metal Detector

[1] S. Sasikumar, K. Suganya, K. Santhosh Kumar, N. Karthikeyan, Prof. S. Kalpanadevi, “Multi Utility Landmine Detection Robotic Vehicle”, International Research Journal of Engineering and Technology, Vol.5, Issue.3, pp.3862-3865, 2018.
[2] Rajesh Kannan Megalingam, Vamsi Gontu, Ruthvik Chanda, Prasant Kumar Yadav, Allada Phanindra Kumar, “Landmine Detection and Reporting using Light Weight Zumo Bot”, In the Proceedings of the 2017 IEEE International Conference on Inventive Computing and Informatics (ICICI 2017), pp.618-622, 2017.
[3] V. Abilash, J. Paul Chandra Kumar, “Arduino Controlled Landmine Detection Robot”, In the Proceedings of the 2017 IEEE Third International Conference on Science Technology Engineering and Management (ICONSTEM 2017), pp.1077-1082, 2017.

Suhiena Shazleem M. Ghouse, Syed Ejaz Ahammed, Tanmay, Zaid Ghori, Kiran M., "Landmine Detection HC-05 Bluetooth controlled Robot (LDBR) using GPS and GSM Technology", International Journal of Computer Sciences and Engineering, Vol.07, Issue.14, pp.448-452, 2019.

The progress of 21stcentury can barely be anticipated without the indication of the part of social media in it. It wouldn’t be overstating to say that social media is ubiquitously present in all spheres of life, be it education, health care, business, disaster management, politics, tourism industry and of course the use of media sharing and entertainment needs no mention. In the wake of all such convenience provided by the social media, it too, does have a darker side to cast. Misuse of social media, the other side of the coin, also needs to be accounted. In the light of this and more so because of the upcoming Lok Sabha elections in India, the authors of this report feel an urge to address the current status of knowledge, the research community possess regarding the use of social media during election. The paperdiscusses the basics of Social Media Analytics i.e., from its evolution and framework to tool and techniques and also some applications in brief. Finally, several studies on social media analytics during elections have been described. It is sought to contemplate the degree to which the result of an election can be predicted, public opinions be altered or its usefulness in campaigning for an election. Apart from this, the authors also hope that this study will be helpful for other researchers to analyse the social media data and yield productive outcomes that contribute to the development of society, government and the nation.

Social Media Analytics, Political science, Elections, Social media

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P. N. Jain, N. V. Alone, "Importance of Social Media Analytics During Elections: A Review", International Journal of Computer Sciences and Engineering, Vol.07, Issue.14, pp.453-458, 2019.

Cloud computing on its own is a technology, arming many services with its resources on the internet, when we speak about big data context, as we have witnessed massive growth in the use of internet that indeed has increased the demand for greater storage capacities where now MB’s and GB’s are small talks in the fields of storage. When we talk about the cloud storage there are privacy and security concerns that we need to work upon for which as of in this paper we propose and use techniques such as pre-authentication, encryption and de- encryption policies. The pre-authentication and proxy re-encryption mechanisms combine the advantages of proxy conditional re-encryption multi-sharing mechanism It can simply be termed as privacy preserving approach to increase security of data on cloud.

Privacy, pre-authentication, big data

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[12] Lidong Zhou, Michael A. Marsh, Fred B. Schneider, and Anna Redz, Distributed blinding for ElGamal re- encryption,Cornell Computer Science Department, 2004.

Sushma A, Sonisha S, M S Soumya Sree, Pooja M.S, Deekshitha G H, "Security Enhancement Using pre-authentication and Proxy re-encryption", International Journal of Computer Sciences and Engineering, Vol.07, Issue.14, pp.459-462, 2019.

These days, numerous costs are made in the field of protection in embracing crude safety efforts to shield the edge of the city from the people who wish to enter without authorization. Most of the military associations rely on a robot inthe hazard inclined regions which is difficult to achieve with armed force personnel. The existing Army robots are highly limited in scope with the audio/video, sensors, and metal identifier. The primary goal of our work is to disguise the robot including few add on parameters like Bluetooth for ongoing information handled by the visualization and PIR sensor to follow the interlopers. In this manner the proposed framework utilizing Bluetooth reduces the chances of any careless mistakes and assures the security from the threats posed by the enemy.

Bluetooth Module, Army Robot, PIR Sensor, Wireless Camera and Colour sensor

[1] Premkumar .M “Unmanned Multi-Functional RobotUsing Zigbee Adopter Network For DefenseApplication” International Journal of Advanced Research in
Computer Engineering & Technology (IJARCET)Volume 2, Issue 1, January 2013.
[2] Akash Ravindran and AkshayPremkumar “Camoflage
Technology”International Journal of Emerging Technology in
Computer Science & Electronics (IJETCSE)
ISSN: 0976-1353 Volume 8 Issue 1 –APRIL 2014.
[3] George Bekey,” Autonomous Robots: From Biological Inspiration toImplementation and Control”, MIT Press, Cambridge, MA, 2005.Mr. M. Arun Kumar, Mrs. M. Sharmila ”Wireless Multi AxisROBOT for Multi-Purpose Operations”, Department of ECE,
SVCET & JNT University Anantapur, India.
[4] Robotic Systems Joint Project Office- Unmanned Ground SystemsRoadmap by Materiel Decision Authority (MDA):Macro USA,McClellan, CA, February2012.

Sudhindra O.S, Sughosh. G, Sai Prakash Reddy, Yash Sokalla, Bindushree D.C, "Camouflage Surveillance Robot", International Journal of Computer Sciences and Engineering, Vol.07, Issue.14, pp.463-465, 2019.

Stepping into the magical world of Virtual Reality gifts an experience unlike any other. All around us all things are bright and beautiful. By this state-of-the-art work, we aim to give our users/stakeholders/visitors the experience of visiting the REVA University campus virtually, from any place at any desired time. We would be able to achieve this by a Head Mounted Display (HMD), here we used the VR Box. Once the user wears the VR Box, on it will be mounted a display which is the key tool in transporting the user virtually to the campus. It will make you feel like you are there mentally and physically. When you turn your head, you can able to see the 360-degree world turns with you, so the illusion created by whatever world you are in is never lost. There are several types of virtual reality from fully-immersive and non-immersive to collaborative and web-based. Here, we have used the fully-immersive variation because this is the explorable and interactive 3D computer-created world that can take you around the campus. The user will be able to experience Virtual Reality tour with a full satisfaction of having visited the REVA University without having to physically walk around the entire campus.

REVA University, Virtual Reality, Unity, VR Headset

[1] R. F. Rahmat, Anthonius, M. A. Muchtar, A. Hizriadi and M. F. Syahputra, “Virtual reality interactive media for universitas sumatera utara – a campus introduction and simulation”, Journal of Physics: Conf. Series (2nd International Conference on Computing and Applied Informatics), doi: 10.1088/1742-6596/978/1/012101, 2018.
[2] https://www.youvisit.com/collegesearch/
[3] https://insights.samsung.com/2016/01/19/virtual-reality-tours-can-help-every-student-pick-the-right-college/
[4] https://concept3d.com/
[5] https://unity3d.com/unity/features/multiplatform/vr-ar

Lipika Sreedharan, M. Sneha, Lisha Kamala K, Jennifer Susa Sen, Kiran M., "REVA University Campus Tour using Virtual Reality", International Journal of Computer Sciences and Engineering, Vol.07, Issue.14, pp.466-470, 2019.

we have used Illusion PIN(IPIN) to solve the problems regarding shoulder_surfing attacks on authentication schemes.PIN -based authentication are practically used on touch screen devices. PIN works on the principle technique of hybrid images.The hybrid images are been merged to keypads. These keypads are ordered to different digits.Thus,making the user to see the device and enter the password whereas the attacker would see another password as the attacker is not as close as the user.The keypads are been shuffled to avoid further attacks,if the attacker is able to remember the position of the keypads.To increase the reliability and security of illusion pin,we worked on algorithm based on human visual perception and calculatimg the minimum distance from attacker and the user.We evaluated our calculations with 84 simulated shoulder-surfing attacks that were obtained from 21 different people.All of the 84 attacks were unsuccessful and we evaluated the minimum distance that a camera cannot capture the necessary data from use’s keypad.. According to our analysis,surveillance camera were not able to capture the PIN of a touchscreen user when Illusion PIN is used.

PIN, touch creen devices, Analysis surveillance

[1] Stajano, “The quest to replace passwords: was written by J. Bonneau, C. Herley, P. C. Van Oorschot, and F. Stajano,
[2] M. Harbach, A. De Luca, and S. Egelman, “The anatomy of smartphone unlocking,”
[3] J. Bonneau, S. Preibusch, and R. Anderson, “A birthday present every eleven wallets? the security of customer-chosen banking pins.”
[4] A. J. Aviv, K. Gibson, E. Mossop, M. Blaze, and J. M. Smith, “Smudge attacks on smartphone touch screens.”
[5] A. Oliva, A. Torralba, and P. G. Schyns, “Hybrid images,” ACM Transactions on Graphics (TOG)
[6] D. Kim, P. Dunphy, P. Briggs, J. Hook, J. W. Nicholson, J. Nicholson, and P. Olivier, “Multi-touch authentication on tabletops.”
[7] L.-W. Chan, T.-T. Hu, J.-Y. Lin, Y.-P. Hung, and J. Hsu, “On top of tabletop: A virtual touch panel display,”
[8] W. Matusik, C. Forlines, and H. Pfister, “Multiview user interfaces with an automultiscopic display,”
[9] C. Harrison and S. E. Hudson, “A new angle on cheap lcds: making positive use of optical distortion,”
[10] S. Kim, X. Cao, H. Zhang, and D. Tan, “Enabling concurrent dual views on common lcd screens,”
[11] E. Hayashi, R. Dhamija, N. Christin, and A. Perrig, “Use your illusion: secure authentication usable anywhere,”
[12] I. Jermyn, A. J. Mayer, F. Monrose, M. K. Reiter, A. D. Rubin et al., “The design and analysis of graphical passwords.”
[13] N. H. Zakaria, D. Griffiths, S. Brostoff, and J. Yan, “Shoulder surfing defence for recall-based graphical passwords,”
[14] D. S. Tan, P. Keyani, and M. Czerwinski, “Spy-resistant keyboard: Towards more secure password entry on publicly observable touch screens,”
[15] V. Roth, K. Richter, and R. Freidinger, “A pin-entry method resilient against shoulder surfing,”

Soubhagyalaxmi V Nerabenchi, Sanjana B, Shaik ajith, Siddalinga Navadagi, "Illusion Pin: Authentication Using Zero Knowledge Protocol", International Journal of Computer Sciences and Engineering, Vol.07, Issue.14, pp.471-473, 2019.

Key benefit of this paper is to provide solution to reduce the time gap between the attacker to compromise the organization and organization to detect it has been compromised. It can be done through real time monitoring the organization data activities. These activities can be from the network assets such as firewall, servers, active directory, IPS, IDS, etc. Studies show that on an average this time gap will be 4 to 6 months, by this time the attacker would have caused severe potential damage to the enterprise which might bring us huge financial loss, confidential data might be breached. To Proactively protect enterprises from such threats It is necessary to have a security operational center which helps organization in real-time monitoring and proactive analysis.

Splunk, system logs, correlation, CSV-comma separated values

[1]K.SANKARI,R.LAVANYA,S.AMALAGRACY “Real Time Monitoring System Using Splunk” IJCSMC, Vol. 4, Issue. 3, March 2015, pg.434 – 441, pp. ISSN 2320–088X
[2] KAVITA AGRAWAL1, READER HEMANT MAKWANA “Data Analysis and Reporting using
Different Log Management Tools” IJCSMC, Vol. 4, Issue. 7, July 2015, pg.224 – 229 pp. ISSN 2320–088X
[3]Harikrishnan V N, Gireesh Kumar T “Advanced Persistent Threat Analysis using
Splunk” Volume 118 No. 20 2018, 3761-3768
[4] Aron Warren “Setting up Splunk for Event Correlation in Your Home Lab” Accepted : SANS Institute Information Security Reading Room on November 19th 2013 ((GCIA) Gold Certification).
[5] Igino Corona, Giorgio Giacinto “Detection of Server-side Web Attacks” JMLR: Workshop and Conference Proceedings 11 (2010) 160–166.
[6] William Geiger “Proactively Guarding Against Unknown Web Server Attacks” Accepted: SANS Institute Information Security Reading Room on 2001.
[7] Kavita Agrawal, Hemant Makwana “A Study on Critical Capabilities for Security Information and Event Management” International Journal of Science and Research (IJSR) ISSN (Online): 2319-7064 Index Copernicus Value (2013): 6.14 | Impact Factor (2013): 4.438.
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[9] Varsha R Mouli, KP Jevitha “Web Services Attacks and Security- A Systematic Literature Review” 6th International Conference On Advances In Computing & Communications, ICACC 2016, 6-8 September 2016, Cochin, India.
[10] L.K. Joshila Grace, V.Maheswari, Dhinaharan Nagamalai “Analysis Of Web Logs And Web User In Web Mining ” International Journal of Network Security & Its Applications (IJNSA), Vol.3, No.1, January 2011.

Venu S N, Shilpa N R, Krishna Badiger, "Proactive Web Security", International Journal of Computer Sciences and Engineering, Vol.07, Issue.14, pp.474-478, 2019.