Volume-6 , Issue-11 , Nov - 2018 Go Back

Data is the driving force in this modern digital era. There are numerous platforms available to send a data from one place to another through computer networks. This survey paper classifies and describes the network classification based on topology, connectivity, and geographic area. A brief introduction on Wireless Sensor Network (WSN) and Internet-of-Things (IoT) has been detailed based on the research. These two technologies are identified as a platform for data processing through different sensors. The data sent through the network using different platform contains all kinds of sensitive information. It is the responsibility of the technology providers to build suitable security systems that assure data privacy. This research paper has focused on a detailed investigation of the Internet-of-Things (IoT) and its data security. Different techniques used for data security are listed, among them, an Encryption technique is found to be effective and well suitable for IoT. This research paper presents the survey of some encryption algorithms. Advanced Encryption Standard (AES), Data Encryption Standard (DES), and Rivest-Shamir-Aldeman (RSA) encryption techniques are briefed.

Internet-of-Things (IoT), Data security, Wireless Sensor Network (WSN), Encryption

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[3] S. Zhang and H. Zhang, "A review of wireless sensor networks and its applications", 2012 IEEE International Conference on Automation and Logistics, 2012.
[4] N. Khalil, M. Abid, D. Benhaddou and M. Gerndt, "Wireless sensors networks for the Internet of Things," 2014 IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP), 2014.
[5] D. Evans, The Internet of Things How the Next Evolution of the Internet Is Changing Everything. San Fransisco: Lopez, 2011, pp. 1-6.
[6]Paul Kirby, “Focus on FTC Staff Recommends Internet of Things Best Practices.” Tele communications Report, Vol. 81,No.3,pp-3-3.
[7] B. Al-Shargabi and O. Sabri, "Internet of Things: An exploration study of opportunities and challenges," 2017 International Conference on Engineering & MIS (ICEMIS), 2017.
[8] S. Kashyap, "10 Real World Applications of Internet of Things (IoT) - Explained in Videos", Analytics Vidhya, 2016. [Online]. Available: https://www.analyticsvidhya.com/blog/2016/08/10-youtube-videos-explaining-the-real-world-applications-of-internet-of-things-iot/. [Accessed: 19- Jun- 2018].
[9] "Connected Toys and What You Need to Know About Them", Us.norton.com. [Online]. Available: https://us.norton.com/internetsecurity-iot-connected-toys-and-what-you-need-to-know-about-them.html. [Accessed: 19- Jun- 2018].
[10] A. Roney Mathew and A.Al Hajj, "Secure Communications on IoT and Big Data," Indian Journal of Science and Technology, vol. 10, no. 11, pp. 1-6, 2017.
[11]G. Singh and Supriya, "A Study of Encryption Algorithms (RSA, DES, 3DES and AES) for Information Security", International Journal of Computer Applications, vol. 67, no. 19, pp. 33-38, 2013.
[12]B. Daddala, H. Wang and A. Javaid, "Design and implementation of a customized encryption algorithm for authentication and secure communication between devices," 2017 IEEE National Aerospace and Electronics Conference (NAECON), 2017.
[13] M. Noura, H. Noura, A. Chehab, M. Mansour and R. Couturier, "S-DES: An efficient & secure DES variant," 2018 IEEE Middle East and North Africa Communications Conference (MENACOMM), 2018.
[14] A. Mamathashree, K. Remya and B. Kumar, "Fault analysis detection in public key cryptosystems (RSA)," 2017 International Conference on Communication and Signal Processing (ICCSP), 2017.
[15] R. Piyare and S. Lee, "Towards Internet of Things (IOT): Integration of Wireless Sensor Network to Cloud Services for Data Collection and Sharing," International Journal of Computer Networks & Communications, vol. 5, no. 5, pp. 59-72, 2013.

Krishna Priya Gurumanapalli, M. Nagendra, "A Survey Paper on Internet of Things and its Data Security Issues," International Journal of Computer Sciences and Engineering, Vol.6, Issue.11, pp.818-824, 2018.

Sorting of products is a very difficult industrial process. Continuous manual sorting creates consistency issues. This paper describes a working prototype designed for automatic sorting of objects based on the color. TCS230 sensor was used to detect the color of the product and the Arduino microcontroller was used to control the overall process. The identification of the color is based on the frequency analysis of the output of TCS230 sensor. Two conveyor belts were used, each controlled by separate DC motors. The first belt is for placing the product to be analyzed by the color sensor, and the second belt is for moving the container, having separated compartments, in order to separate the products. The experimental results promise that the prototype will fulfill the needs for higher production and precise quality in the field of automation.

TCS230, Color Sensor, Color Sorting, DC motor

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[4] Shaiju Paul, Ashlin Antony & Aswathy B 2014, “Color Sorting Using Aurdino” International Journal of Computing and Technology Vol. 1, No.1.
[5] Jilani Sayyad , Mukadam Taha & Amol Sankpal 2017, “Advanced Car Security System” International Journal of Scientific Research in Network Security and Communication, Vol.5 , Issue.3 , pp.165-169, Jun-2017.
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[8] A. Omanakuttan, D. Sreedhar, A. Manoj, A. Achankunju , CM. Cherian, "GPS and GSM Based Engine Locking System Using Smart Password", International Journal of Computer Sciences and Engineering, Vol.5, Issue.4, pp.57-61, 2017.

Sanjeev Gangwar, Ashok Kumar Yadav, Santosh Kumar Yadav, "Color Sorting Based on Internet of Things," International Journal of Computer Sciences and Engineering, Vol.6, Issue.11, pp.825-827, 2018.

When arranging an excursion, clients dependably have particular inclinations with respect to their travels. Rather than confining clients to constrained inquiry alternatives, for example, areas, exercises, or eras, we consider self-assertive portrayals as catchphrases about customized necessities. This paper discusses travel recommendation techniques which help a user in finding tourist locations that he/she might like to visit a place from available user-contributed information and photos of that place available on sharing websites. This paper describes methods used to mine demographic information and provide travel recommendation to users. This paper also discusses skyline query processing.

Information mining, Social Media Mining, Opinion Mining, Recommender Systems, Skyline proccessing

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Uttra Kumar Verma, Jasmine Minj, "Applications of Social Media Mining and Skyline Processing on Travel Recommendation Systems a Survey," International Journal of Computer Sciences and Engineering, Vol.6, Issue.11, pp.828-831, 2018.

The vindictive site is a typical and genuine danger to digital security. Pernicious URLs have spontaneous substance like spam, phishing, drive-by misuses, and so on and draw clueless clients to wind up casualties of tricks like financial misfortune, burglary of private data, and malware establishment and so on which cause misfortunes of billions of dollars consistently. It is basic to recognize and follow up on such dangers in an opportune way. To improve the generality of malicious URL detectors, various kinds of techniques using both static and dynamic features have been explored with increasing attention in recent years. In this study, we center principally on examining the real methodologies for pernicious URL recognition procedures and work directed in the zone.

Static Analysis, Dynamic Analysis, Security Threats, Application-based threat, Mobile-based threat, Network threats, Web-based threat, Physical Threats, Blacklisting, Machine Learning

[1] D. Sahoo, C.Liu, and S.C.H. Hoi,” Malicious URL Detection using Machine Learning: A Survey”,arXiv , March 2017 For Journal
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[6] D.Sahoo, C.Liu, and S.C.H. Hoi,”Malicious URL Detection using Machine Learning: A Survey”,arXiv:1701.07179v2 [cs.LG], Mar 2017.
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[8] B. Eshete, A. Villafiorita, and K. Weldemariam, “Binspect: Holistic analysis and detection of malicious web pages,” in Security and Privacy in Communication Networks. Springer, 2013, pp. 149–166.C.T. Lee, A. Girgensohn, J. Zhang, “Browsers to support awareness and Social Interaction,” Computer Graphics and Applications, Journal of IEEE Access , Vol.24, Issue.10, pp.66-75, 2012. doi: 10.1109/MCG.2004.24
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[10] S. Sheng, B. Wardman, G. Warner, L. F. Cranor, J. Hong, and C. Zhang, “An empirical analysis of phishing blacklists,” in Proceedings of Sixth Conference on Email and Anti-Spam (CEAS), 2009.
[11] M. Kuyama, Y. Kakizaki, and R. Sasaki, “Method for detecting a malicious domain by using whois and dns features,” in The Third International Conference on Digital Security and Forensics (DigitalSec2016), 2016, p. 74.
[12] S. C. Hoi, J. Wang, and P. Zhao, “Libol: A library for online learning algorithms,” The Journal of Machine Learning Research, vol. 15, no. 1, pp. 495–499, 2014.
[13] J. Ma, L. K. Saul, S. Savage, and G. M. Voelker, “Beyond blacklists: learning to detect malicious web sites from suspicious urls,” in Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2009, pp. 1245–1254.
[14] “Learning to detect malicious urls,” ACM Transactions on Intelligent Systems and Technology (TIST), vol. 2, no. 3, p. 30, 2011.

Deepali Gupta, Jasmine Minj, "A Survey on Internet based Security Threats and Malicious Page Detection Techniques," International Journal of Computer Sciences and Engineering, Vol.6, Issue.11, pp.832-836, 2018.

Now a day’s each and everyone can store their data cloud because of its services and storage capacity. It is key for cloud advantage delegates to give a multi-appropriated restrain relationship to oblige their cost to cloud expert affiliations (CSPs) while giving service level objective (SLO) certification to their customers. Diverse multi-passed on restrict affiliations have been proposed or divide minimization or SLO guarantee. In existing system we simply store the data but we don’t know whether data will be secured or not that means we don’t have any guarantee on cloud providers still now only few works achieve both cost minimization and SLO guarantee. In this paper, we propose a multi-cloud Economical and SLO-ensured Storage Service (ES3), which picks information transport and asset reservation follows with fragment cost minimization and SLO ensure.ES3 joins an engineered data bit and resource reservation methodology, which assigns each data thing to a datacenter and determines the resource reservation amount on datacenters by leveraging all the pricing policies; (2) ) a genetic algorithm based data allocation adjustment method, which decrease data Get/Put rate contrast in each datacenter to enable the reservation to advantage. Our proposed system (i.e., Amazon S3, Windows Azure Storage and Google Cloud Storage) exhibit the unrivaled execution of ES3 in separate cost minimization and SLO guarantee in relationship with previous works.

Delegates,SLO guarantee, Storage Service, datacenter and cost decrease

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Sankar Lavanya, "SLO Guarantee and Cost Minimization under the Get Rate Variation in ES3," International Journal of Computer Sciences and Engineering, Vol.6, Issue.11, pp.837-839, 2018.

The rapid evolution of processing power, storage technologies and availability of high quality broadband speed and big data have enabled the realization of a new computing model called cloud computing. In cloud computing, resources such as computing power &infrastructure, application platforms, and business processes are provided through the internet as general utilities to users in an on demand fashion. Business enterprises are now increasingly seeking to reshape their business models to gain benefits from this new paradigm of resource sharing.

Computing power, Resources, Business models etc

[1] Voorsluys, William; Broberg, James; Buyya, Rajkumar (2011). “Introduction to Cloud Computing”. In R.Buyya, J. Broberg, A. Goscinski. Cloud Computing: Principles and Paradigms (pp. 1-44). New York, USA: Wiley
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[9] Rajendira S. Kumar, A. Marimuthu: Implementing hybrid cryptographic algorithm to enhance the security in cloud computing, IJCSE, ISSN 2347-2693, Vol. 6, Issue 10, OCT. 2018

Parul, "India’s New Cloud Computing Policy: Localization Of Data," International Journal of Computer Sciences and Engineering, Vol.6, Issue.11, pp.840-843, 2018.

The information era witnesses the creation of multimedia data, transfers and transactions in the order of millions. This data by virtue of their formats comes in varying sizes and differing temporal characteristics. The wealth of information carries potential both in terms of explicit content that is expressed and the implicit or hidden content. Processing the former is quite developed while the procedures and applications of working with the implicit knowledge are growing steadily. This paper aims to present a range of techniques from recent works pertaining to the processing and applicability of such data. The purpose of the survey is to bring to light the specific methods of social media data analysis in a concise and organized manner. Specifically, natural language processing, topic modelling, sentiment analysis and affective analysis have been identified as the overarching heads taken up by several recent researches. Finally, some observations pertaining to social media data analysis identified from several works are enlisted.

Data Mining, Machine Learning, Natural Language Processing, Social Media

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[3] A.Schmidt and M.Wiegand, "A Survey on Hate Speech Detection Using Natural Language Processing." Proceedings of the Fifth International Workshop on Natural Language Processing for Social Media, Valencia, Spain, pp. 1-10, 2017.
[4] N.Mamgain, E.Mehta, A.Mittal, and G.Bhatt, "Sentiment Analysis of Top Colleges in India Using Twitter Data." 2016 International Conference on Computational Techniques in Information and Communication Technologies (ICCTICT), New Delhi, pp. 525-530, 2016.
[5] C. Nanda, M. Dua, “A Survey on Sentiment Analysis”, International Journal of Scientific Research in Computer Sciences and Engineering, Vol 5, Issue 2, pp. 67-70, April, 2017.
[6] S.Poria, E.Cambria, A.Hussain, and G.B. Huang. "Towards an Intelligent Framework for Multimodal Affective Data Analysis." Neural Networks, Vol. 63, pp. 104-16, 2015.
[7] K.H.Lim, S.Karunasekera, A.Harwood, and L.Falzon. "Spatial-based Topic Modelling Using Wikidata Knowledge Base." 2017 IEEE International Conference on Big Data (Big Data),Boston, MA, USA, pp. 2009-2018, 2017.
[8] A.Sarker, R.Ginn, A.Nikfarjam, K.O.Connor, K.Smith, S.Jayaraman, T.Upadhaya, and G.Gonzalez. "Utilizing Social Media Data for Pharmacovigilance: A Review." Journal of Biomedical Informatics 54 , pp.202-12, 2015.
[9] D.T.Nguyen, K.A.A.Mannai, S.Joty, H. Sajjad, M.Imran,P.Mitra, “Robust classification of crisis-related data on social networks using convolutional neural networks”, Proceedings of the 11th International Conference on Web and Social Media, ICWSM, Montreal, Canada, pp. 632-635, 2017.
[10] A.Hausmann, T.Toivonen, R.Slotow, H.Tenkanen, A.Moilanen, V.Heikinheimo, and E.D.Minin. "Social Media Data Can Be Used to Understand Tourists’ Preferences for Nature-Based Experiences in Protected Areas." Conservation Letters Vol. 11, Issue. 1, pp.1-10, 2017.
[11] Z.A.Hamstead, D.Fisher, R.T.Ilieva, S.A.Wood, T.Mcphearson, and P.Kremer. "Geolocated Social Media as a Rapid Indicator of Park Visitation and Equitable Park Access." Computers, Environment and Urban Systems Vol. 72, pp.38-50,2018.
[12] A.S.Halibas, A.S.Shaffi, and M.A.K.V.Mohamed. "Application of Text Classification and Clustering of Twitter Data for Business Analytics." 2018 Majan International Conference (MIC), Muscat, Oman, pp. 1-7, 2018.
[13] R.S.Shirsath, V.A.Desale, A.D.Potgantwar, “Big Data Analytical Architecture for Real-Time Applications”, International Journal of Scientific Research in Network Security and Communication, Vol 5, Issue 4, August, 2017.

Aishwarya Rajamani, Alpha Vijayan, "A Survey on Realms and Applications of Social Media Data Analysis," International Journal of Computer Sciences and Engineering, Vol.6, Issue.11, pp.844-848, 2018.

Data in almost every field concerning daily needs is increasing by leaps and bounds. The problem of analysing such volume of data is enormous as tools and techniques may not be compatible of such volumes. In order to tackle the issue, data mining mechanisms are employed. Research mechanisms corresponding to big data analytics is discussed in this work. Also in case of misclassified data, it is required to tackle that data and then perform mining and classification. The mechanisms used to detect and predict anomalies along with misclassification are presented in comparative form. The objective of this work is to extract useful information regarding techniques used for big data analytics for future enhancements. Techniques used to minimise degree of misclassification in big data is analysed in comprehensive manner. These techniques extract useful patterns that could be used to observe big data in quick time.

Big data, misclassified data, data mining

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Aparpreet Singh, Sandeep Sharma, "Challenges and Analysis of Big Data: A Review," International Journal of Computer Sciences and Engineering, Vol.6, Issue.11, pp.849-858, 2018.

In Democratic countries like India, Bangladesh the voting system plays a major role during elections. Traditionally, the election commission in India uses electronic voting machines which need more manpower, time-consuming and also they are less trusted. For avoiding misconceptions during elections, there are lot of advanced techniques are being proposed using various methods. The voting system is managed in a easier way as all the users should login by Aadhar card number and password and click on his favorable candidates to cast the vote. In this paper we will review on Aadhar Based Biometric Voting Machine Survey, the research conducted by various researchers related to the discipline of biometric voting machine are taken into consideration and discussed in chronological order. With the help of this literature survey, it has tried to find out the basic of biometric voting system, improvement of various methods for biometric voting system, and classification of various newly developed methods.

Electronic Voting machine, Arduino, Finger print sensor, Matrix keyboard, LCD Display

[1] A. Das, M. P. Dutta, “VOT-EL:Three Tier Secured State-Of-The-Art EVM Design Using Pragmatic Fingerprint Detection Annexed With NFC Enabled Voter -ID Card”, International Conference on Emerging Trends in Engineering, Technology and science, 2016.
[2] R. Bhuvanapriya, S. R. banu, P. Sivapriya, V. K Kalaiselvi, “Smart Voting”, International Conference on Computing and Communication Technology, Chennai, India, 2017
[3] J. Deepika, S. Kalaiselvi, S.Mahalakshmi, S.Agnes Shifani, “Smart Electronic Voting System Based On Biometric Identification-Survey”, Third International Conference on Science Technology Engineering & Management (ICONSTEM), , Chennai, India, 2017.
[4] M. M. Karim, N. S. Khan, “Smart Electronic Voting System Based On Biometric Identification-Survey”, IEEE International Conference on Telecommunications and Photonics (ICTP) 26-28 December, 2017, Dhaka, Bangladesh, 2017
[5] V. K. Priya, V. Vimaladevi, T. D. Pandimeenal, “Arduino based Smart Electronic Voting Machine”, International Conference on Trends in Electronics and Informatics (ICEI), Tirunelveli, India, 2017
[6] R. Rezwan, Huzaifa Ahmed, M. R. N. Biplob, S. M. Shuvo, Md. A. Rahman, “Biometrically Secured Electronic Voting Machine”, IEEE Region 10 Humanitarian Technology Conference (R10-HTC), Dhaka, Bangladesh, 2017
[7] Ch.Jaya Lakshmi, S.Kalpana, “Secured and Transparent Voting System Using Biometrics”, Second International Conference on Inventive Systems and Control (ICISC 2018), Coimbatore, India, 2018

H. N. Prajapati, S. K. Kshirsagar, V. B. Patil, S. G. Vitore, "Literature Review on Aadhar Based Secure Biometric Voting Machine," International Journal of Computer Sciences and Engineering, Vol.6, Issue.11, pp.859-863, 2018.

A mobile app is a software application designed for use on small wireless devices such as smart phones and tablets rather than desktop or laptop computers. Nowadays thousands of mobile apps are published to the Google Play store or Apple Store. Even a person who has no knowledge on programming languages can create mobile apps, with the help of various mobile app development platforms. Lots of such mobile app development platforms are available today. Some of those are used for creating native apps and some for creating Hybrid apps. Drag and Drop App Builders give non-developer users a solution to build do-it-yourself applications. Such products include features exactly same as design products like visual drag-and-drop tools to make apps. This paper focused on reviewing and familiarizing some efficient drag and drop mobile app builder platforms.

Mob App, Native App, Hybrid App, Bootstrap etc

[1].MobileAppclassification,https://www.nngroup.com/articles/mobile-native-apps/ classification
[2].MobileAppclassification,https://blog.trigent.com/different-types-of-mobile-applications-native-hybrid-and-web-apps/
[3]Thunkable,.https://blog.thunkable.com/on-thunkable-anyone-can-build-their-own-mobile-apps-for-android-and-ios-f61abb17be11
[4]. https://thinkmobiles.com/blog/popular-types-of-apps/
[5]. https://buildfire.com/mobile-app-development- tools
[6]. https://themanifest.com/app-development/mobile-app-usage-statistics-2018
[7].AppyBuilder, https://appybuilder.com/
[8].MIT Appinventor2, http://ai2.appinventor.mit.edu/
[9]. Buziness app, https://www.business.com/reviews/biznessapps/
[10].Makeroid, http://teachabout-tech.blogspot.com/2018/03/ makeroid-tutorial-how-to-make-app.html
[11]. Appy Pie, https://www.appypie.com/

Johnsymol Joy, "Review On Different Types of Drag and Drop Mobile App Development Platforms," International Journal of Computer Sciences and Engineering, Vol.6, Issue.11, pp.864-866, 2018.