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Wireless sensor networks (WSNs) enable new applications and require nonconventional paradigms for protocol design due to several constraints. Owing to the requirement for low device complexity together with low energy consumption (i.e., long network lifetime), a proper balance between communication and signal/data processing capabilities must be found. This motivates a huge effort in research activities, standardization process, and industrial investments on this field since the last decade. This survey paper aims at reporting an overview of WSNs technologies, main applications and standards, features in WSNs design, and evolutions with connections of Internet of things. The Internet of Things, or IoT, refers to the set of devices and systems that interconnect real-world sensors and actuators to the Internet. Emphasis is given to the IEEE 802.15.4 technology, which enables many applications of WSNs. Some example of performance characteristics of 802.15.4based networks are shown and discussed as a function of the size of the WSN and the data type to be exchanged among nodes.

WSN, IoT, nodes, gateway, self-organizing singlesink WSN, PPP, event detection, spatial process estimation

[1]. Akyildiz I., Su W., Sankarasubramaniam Y., Cayirci E. A survey on sensor networks. IEEE Commun. Mag. 2002;40:102–114.
[2]. Tubaishat M., Madria S. Sensor networks: an overview. IEEE Potentials. 2003;22:20–30.
[3]. Hac A. Wireless Sensor Network Designs. John Wiley & Sons Ltd; Etobicoke, Ontario, Canada: 2003.
[4]. Raghavendra C., Sivalingam K., Znati T. Wireless Sensor Networks. Springer; New York, NY, USA: 2004.
[5]. Sohrabi K., Gao J., Ailawadhi V., Pottie G. Protocols for selforganization of a wireless sensor network. IEEE Personal Commun. 2000;7:16–27.
[6]. Culler D., Estrin D., Srivastava M. Overview of sensor networks. IEEE Comput. 2004;37:41–49.
[7]. Rajaravivarma V., Yang Y., Yang T. An Overview of Wireless Sensor Network and Applications.
[8]. Proceedings of 35th Southeastern Symposium on System Theory; Morgantown, WV, USA. 2003; pp. 432–436.
[9]. Verdone R., Dardari D., Mazzini G., Conti A. Wireless Sensor and Actuator Networks. Elsevier; London, UK: 2008.
[10]. Verdone R. Wireless Sensor Networks. Proceedings of the 5th European Conference; Bologna, Italy. 2008.
[11]. Culler D., Estrin D., Srivastava M. Overview of sensor networks. IEEE Comput. Mag. 2004;37:41– 49.
[12]. Basagni S., Conti M., Giordano S., Stojmenovic I. Mobile Ad Hoc Networking. Wiley; San Francisco, CA, USA: 2004.
[13]. IEEE 802.15.4 Standard . Part 15.4: Wireless Medium Access Control (MAC) and Physical Layer (PHY) Specifications for LowRate Wireless Personal Area Networks (LRWPANs)
[14]. IEEE; Piscataway, NJ, USA: 2006.
[15]. Lin C., Tseng Y., Lai T. MessageEfficient InNetwork
[16]. Location Management in a Multisink Wireless Sensor Network. Proceedings of IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing; Taichung, Taiwan. 2006; pp. 1–8.
[17]. Ong J., You Y.Z., MillsBeale J., Tan E.L., Pereles B., Ghee K. A wireless, passive embedded sensor for realtime monitoring of water content in civil engineering materials. IEEE Sensors J. 2008;8:2053– 2058.
[18]. Lee D.S., Lee Y.D., Chung W.Y., Myllyla R. Vital sign monitoring system with life emergency event detection using wireless sensor network. Proceedings of IEEE Conference on Sensors; Daegu, Korea. 2006.

Atul Ranjan Srivastava, Vivek Kushwaha, "Compliances of Connectivity and Communication strategies of Wireless Sensor Network (WSN) over IoT," International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.569-573, 2019.

The Point-to-point protocol is a protocol designed to respond all the Internet protocol and allow IP addresses to be assigned dynamically as well as support authentication of the User. It connects two routers directly without any host or any other networking device in between These point-to-point protocol can control and manage the transfer of data, connection authentication, transmission encryption and compression.

Point-to-point protocol(PPP),Link control protocol(LCP),Network control protocol(NCP),Password authentication protocol(PAP),Challenge handshake authentication protocol(CHAP),Internetwork protocol control protocol(IPCP)

[1]. Computer Networking: principles, Protocols and Practices.
[2]. Point-to-point protocol and feature overview and configuration guide – Allied Telesis.
[3]. Networking model and packet guide to core network protocols.
[4]. Data communication and networking –Behrouz A.forouzan.
[5]. A pointed look at the point-to-point protocol IEEE Internet computing – C.Metz.
[6]. D.Perkins,”The point-to-point protocol for the transmission of multi-protocol data grams over point-to-point Links”
[7]. Stallings, William, cryptography and network Security (6th edition.). Upper Saddle River, N.J.: Prentic Hall.

J. Saranya, "A Study on point-to-point protocol in Data Communication and Networking," International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.574-576, 2019.

The older bridges and flyovers in India have privately and government owned areas. This fact cannot be neglected in today’s world when India is rising in the global competitive market where many new structures are coming up. These bridges and flyovers have known or unknown deficiencies and will not be identified unless a disaster is experienced. But by the time a disaster happens, a huge human loss also happens which is undesirable. This needs to conduct health monitoring and providing proper solution to the private owner or government to take care in the interest of the nation. Accidents are happening frequently all over the world due to lack of technology and human attentions at right time. Although these accidental events cannot be completely eliminated but some useful measures can reduce it definitely .In this context, the initial steps are required to avoid human deaths by introducing new technologies. This effort has been taken in this work by adopting real-time health monitoring of the bridge/flyover and automated gate controlling The above said system works on Raspberry Pi-3 embedded with required sensors fitted in the bridge/fly-over.

Bridges, Flyovers, Raspberry Pi-3, IOT

[1] Fang Zhang et al.,”Design and Implementation of Wireless Bridge Health System”, IET International Conference on smart and sustainable City, 2012.
[2] Ying Sun, “Research on the railroad bridge monitoring platform based on the Internet of Things”, International Journal of Control and Automation. Vol.7, No.1, pp. 40-408, 2014.
[3] Velmurugan K and Rajesh, “Advanced Railway Safety Monitoring System based on Wireless Sensor Networks”, IJCSET. Vol 6, Issue 2, pp. 89-94, 2016.
[4] Atharva Kekare et al., “Bridge Health Monitoring System”, IOSR-JECE., Volume 9, Issue 3, Ver. IV ,PP 08-14 , 2014.
[5] Sunaryo Sumitro, “Current and Future Trends in Long Span Bridge Health Monitoring System in Japan”, National Science Foundation on Health Monitoring of Long Span Bridges, University of California, Irvine Campus, 2001.
[6] Bart Peeters and Guido De Roeck, “One-year monitoring of the Z24-Bridge: environmental effects versus damage events”, Earthquake Engng Struct. Dyn, vol.30, pp.149–171, 2014.
[7] A.Emin Aktan et al., ”Development of a Model Health Monitoring Guide for Major Bridges”, Federal Highway Administration Research and Development., order no:DTFH61-01-P-00347, 2002.
[8] Tyler Harms et al., “Structural Health Monitoring of Bridges Using Wireless Sensor Networks”, IEEE Instrumentation & Measurement Magazine. Volume 13, Issue 6, 2010.

Alok Kumar Pani, Tapas Kumar Mishra, Manohar M, "An Improved Approach for Monitoring and Controlling of Flyovers and Bridges Using Internet of Things," International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.577-582, 2019.

Huge Data has turned out to be progressively famous, as it can give on-request, dependable and adaptable administrations to clients, for example, stockpiling and its preparing. The information security has turned into a noteworthy issue in the Big information. The open source HDFS programming is utilized to store tremendous measure of information with high throughput and adaptation to internal failure and Map Reduce is utilized for its calculations and handling. Be that as it may, it is a noteworthy focus in the Hadoop framework, security demonstrate was not structured and turned into the real disadvantage of Hadoop programming. As far as capacity, meta information security, touchy information and furthermore the information security will be a difficult issue in HDFS. With the significance of Hadoop in the present undertakings, there is likewise an expanding pattern in giving a high security includes in ventures. Over ongoing years, just some dimension of security in Hadoop, for example, Kerberos and Transparent Data Encryption(TDE),Encryption procedures, hash methods are appeared for Hadoop. This paper, demonstrates the endeavors that are made to exhibit Hadoop Authorization security issues utilizing Apache Sentry in HDFS.

Introduction, Security Issues in Hadoop Cluster , Hadoop Security Primer, Key Benefits of Apache Sentry , Working of Sentry

[1] Sirisha N & Kiran KVD, “Protection Of Encroachment On Bigdata Aspects”, International Journal of Mechanical Engineering and Technology (IJMET), Vol.8, No.7, (2017), pp.550–558.
[2] Park S & Lee Y, “Secure Hadoopwith Encrypted HDFS”, SpringerVerlag Berlin Heidelberg, (2013), pp.134–141.
[3] Dean J & Ghemawat S, “MapReduce: simplified data processing on large clusters”, CACM, Vol.51, No.1, (2008), pp.107-113.
[4] Park S & Lee Y, “Secure hadoop with encrypted HDFS”, International Conference on Grid and Pervasive Computing, (2013), pp.134-141.
[5] Zerfos P, Yeo H, Paulovicks BD &Sheinin V, “SDFS: Secure distributed file system for data-at-rest security for Hadoop-as-aservice”, IEEE International Conference on Big Data (Big Data), (2015), pp.1262-1271.
[6] Grover C &Aulakh MK, “Big Data Authentication and Authorization in HDP (Hadoop Distributed platform) using Kerberos and Ranger”, 2nd International Conference on Recent Innovations in Management and Engineering, (2017), pp.44-51.
[7] Cheng Z, Zhang D, Huang H & Qian Z, “Design and Implementation of Data Encryptionin Cloud based on HDFS”, International Workshop on Cloud Computing and Information Security, (2013), pp.274-277.
[8] Shehzad D, Khan Z, Dag H &Bozkus Z, “A novel hybrid encryption scheme to ensure Hadoop based cloud data security”, International Journal of Computer Science and Information Security, Vol.14,No.4,(2016).
[9] Rabin MO, “Efficient Dispersal of Information for Security, Load Balancing, and Fault Tolerance”, Journal of the Association for Computing Machinery, Vol.36, No.2, (1989), pp.335-348. [10] "Transparent Encryption in HDFS. https://hadoop.apache.org/docs/r2.7.2/hadoop-project dist/hadoophdfs/TransparentEncryption.html.
[11] Byers J, Luby M, Mitzenmacher M & Reg A e, “A Digital Foundation Approach to Reliable Distribution of Bulk Data”, Proc.ACM SIGCOMM’98, Vol.28, No.4, (1998), pp.56-67.
[12] Darade SA &Kamble K, “Network Level Security in Hadoop Using Wire Encryption”, International journal of Advanced research in science management and technology,Vol.1, No.6, (2015).
[13] Cloudera Inc., “HDFS Data At Rest Encryption”, http://www.cloudera.com/content/cloudera/en/documentation/core/latest/topic s/cdh_sg_hdfs_encryption.html#xd_583c10bfdbd326ba--5a52cca1476e7473cd--7f85, 2015.
[14] IBM BigInsights on Cloud, IBM, 2016. http://www-03.ibm.com/software/products/en/ibm-biginsights-oncloud.
[15] Vivekanand &Vidyavathi BM, “Security Challenges in Big Data: Review”, International Journal of aAdvanced Research in Computer Science, Vol.6, No.6, (2015).

Regha S., Manimekalai M., "Approval of Data in Hadoop Using Apache Sentry," International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.583-586, 2019.

In this era cloud computing is used in various fields like industry, military, college, etc. for various services and storage of huge amount of data. Data stored in this cloud can be accessed or retrieved on the users request without direct access to the server computer. But the major concern regarding storage of data online that is on the cloud is the Security. This Security concern can be solved using various ways, the most commonly used techniques are cryptography and steganography. But sometimes a single technique or algorithm alone cannot provide high-level security. So we have introduces a new security mechanism that uses a combination of multiple cryptographic algorithms of symmetric key and steganography. In this proposed system 3DES (Triple Data Encryption Standard), RC6 (Rivest Cipher 6) and AES (Advanced Encryption Standard) algorithms are used to provide security to data. All the algorithms use 128-bit keys. LSB steganography technique is used to securely store the key information. Key information will contain the information regarding the encrypted part of the file, the algorithm and the key for the algorithm. File during encryption is split into three parts. These individual parts of the file will be encrypted using different encryption algorithm simultaneously with the help of multithreading technique. The key information is inserted into an image using the LSB technique. Our methodology guarantees better security and protection of customer data by storing encrypted data on a single cloud server, using AES, 3DES and RC6 algorithm.

Cryptography, Encryption, Decryption, Cloud Security, Cloud Storage

[1] A. K. Shahade, V.S. Mahalle, “Enhancing the Data Security in Cloud by Implementing Hybrid (Rsa & Aes) Encryption Algorithm”, IEEE, INPAC, pp 146-149, Oct .2014.
[2] Palash Uddin, Abu Marjan, “Developing Efficient Solution to Information Hiding through text steganography along with cryptography”, IEEE, IFOST, pages 14-17, October 2014.
[3] R. T. Patil and P. S. Bhendwade , “Steganographic Secure Data Communication”,IEEE, International Conference on Communication and Signal Processing, pages 953-956,April 2014.
[4] Klaus Hofmann and S. Hesham, “High Throughput Architecture for the Advanced Encryption Standard Algorithm” IEEE, International Symposium on Design and Diagnostics of Electronic Circuits & Systems, pages 167- 170, April 2014.
[5] D. Nilesh, M. Nagle, “The New Cryptography Algorithm with High Throughput”, IEEE, ICCCI, pages 1-5, January 2014.
[6] LI Yongzhen, Zhou Yingbing, “The Design and Implementation of a Symmetric Encryption Algorithm Based on DES”, IEEE, ICSESS, pages 517-520, June 2014.
[7] A. Hasan, N. Sharma, “A New Method Towards Encryption Schemes (Name-Based-Encryption Algorithm)”, IEEE, International Conference.
[8] S.Rajendirakumar, Dr.A.Marimuthu, “Cryptographic Algorithms used in Cloud Computing – An Analysis and Comparison”, International Journal for Research in Applied Science & Engineering Technology, Vol 6, Iss. 1, 2018.
[9] Prerna Mahajan, Abhishek Sachdeva, "A Study of Encryption Algorithms AES, DES and RSA for Security", Global Journal of Computer Science and Technology, Network, Vol. 13, Iss. 15, 2013.

Aditya Poduval, Abhijeet Doke, Hitesh Nemade, Rohan Nikam, "Secure File Storage on Cloud using Hybrid Cryptography," International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.587-591, 2019.

Software Defined Network is based on separation of network intelligence from packet switching devices and merging network intelligence in a centralized controller. With SDN, Enterprises can manage the entire network from a single SDN controller, irrespective of different vendors of network elements [11].This controller acts as the main brain and act as a strategic control point to decide about routing using OpenFlow protocols. SDN controller is a kind of operating system for network where applications and devices can have communication only through it. In this paper, many SDN controllers are discussed.

SDN controller, performance, applications, API

[1] Ola Salman, Imad H. Elhajj, Ali Chehab SDN controllers: A comparative study https://www.researchgate.net/publication/304457462
[2] F. Alencar, M. Santos, M. Santana and S. Fernandes, `"How Software Aging affects SDN: A view on the controllers,"Global Information Infrastructure and Networking Symposium (GIIS), 2014, pp. 1-6.
[3] Feng Wang, Heyu Wang, Baohua Lei and Wenting Ma,"A Research on High-Performance SDN Controller," Cloud Computing and Big Data (CCBD), 2014 International Conference on, pp. 168-174.
[4] O.N. Fundation, `"Software-defined networking: The new norm for networks," ONF White Paper.
[5] Dimitra Sakellaropoulou, “A Qualitative Study of SDN Controllers”, M.Sc., Thesis, Athens, September, 2017
[6] https://www.opendaylight.org
[7] OSGi Core Release 5, OSGi Alliance, San Ramon, CA, USA, Mar. 2012.
[8] Saleh Asadollahi, Dr. Bhargavi Goswami, Dr. Atul M Gonsai, Software Defined Network, Controller Comparison, International Journal of Innovative Research in Computer and Communication Engineering, April 2017 Special Issue
[9] Abhishek Rastogi, Abdul Bais, “Comparative Analysis of Software Defined Networking (SDN) Controllers – In Terms of Traffic Handling Capabilities”, Multi-Topic Conference (INMIC), 2016 19th International
[10] https://wiki.onosproject.org
[11]Mandar B. Shinde, Sunil G. Tamhankar, “Review: Software Defined Networking and OpenFlow”, International Journal of Scientific Research in Network Security and Communication

C. Kalaivani, "An overview of SDN Controllers," International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.592-594, 2019.

Data mining is a promising technology which helps to analyze the data and to discover the interesting hidden patterns in large volume of data. The goal of data mining is to predict, identify, classify and optimize the use of resources to recognize complex patterns and make intelligent decisions based on data. Agriculture plays a vital role in economy and it is the backbone of our economic system. Data mining in agriculture provides many opportunities for exploring hidden patterns in these collections of data. Soil Fertility is the capability of soil to provide plants with enough nutrients and moisture to yield crop in better way. The yielding capability of a soil depends on soil fertility. It is very important to achieve and maintain an appropriate level of soil fertility for crop production. The main focus of this paper is to analyse the soil data which is collected from soil testing laboratory and identifying attributes to predict fertility from collected dataset by using different Machine Learning algorithms. This work also focuses on finding the best classification algorithm based on accuracy and performance measure using the soil dataset with different Data Mining classifiers like J48, Naïve Bayes and REPTree.

Agriculture, Classification, Data Mining, J48, Naïve Bayes, REPTree, Soil fertility

[1] Manisha Sahane, BalajiAglave, Razaullah Khan, Sanjay Sirsat, An Overview of DataMining Techniques Agricultural Soil Data Applied to Agricultural Soil Data,International Journal of Agriculture Innovations and Research, Vol.3, No.2,pp. 445 – 448, 2014.
[2] Dr.S.Hari Ganesh, Mrs. Jayasudha, an Enhanced Technique to Predict the Accuracy of Soil Fertility in Agricultural Mining, International Journal of Advanced Research in Computer and Communication Engineering, Vol. 4, Issue. 7, pp. 285-287, 2015.
[3] Hetal Patel, Dharmendra Patel, A Brief survey of Data Mining Techniques Applied to Agricultural Data, International Journal of Computer Applications (0975 – 8887) Vol. 95, No. 9, pp. 6-8, 2014.
[4] P. Jasmine Sheela, K. Sivaranjani, A Brief Survey of Classification Techniques Applied To Soil Fertility Prediction, International Conference on Engineering Trends and Science &Humanities (ICETSH-2015), Vol. 3, No. 5, pp. 80-83, 2015.
[5] VrushaliBhuyar, Comparative Analysis of Classification Techniques on Soil Data to Predict Fertility Rate for Aurangabad district, IJETTCS International Journal of Emerging Trends & Technology in Computer Science Issues, Vol. 3, Issues. 2, pp.200-203, 2014.
[6] Jay Gholap, Performance Tuning of J48 Algorithm for Prediction of Soil Fertility, Asian Journal of Computer Science and Information Technology, Vol.2, Issues 8, pp. 251-252, 2012.
[7] Jay Gholap, Anurag Ingole, JayeshGhoil, ShaileshGargade, Vahida Attar, Soil Data Analysis Using Classification Techniques and Soil Attribute Prediction,IJCSI, Vol. 9, No 3, 2012.
[8] Shivnath Ghosh, santanukoley, Machine Learning for Soil fertility and Plant Nutrients Management using Back Propagation Neural Networks, International Journal on Recent and Innovation Trends in Computing and Communication, Vol. 2, Issues 2, pp.2014.
[9] NikhitaAwasthi, Abhay Bansal, Application of Data Mining Classification Techniques on Soil Data Using R, Vol. 4, Issues 1, pp.33-37, 2017.
[10] B.V.RamaKrishna, Dr B.Satyanarayana, Agriculture Soil Test Report Data Mining for Cultivation Advisory, International Journal of Computer Application (2250-1797), Vol.6, No.2, pp.11-16, 2016.
[11] Ramya M.C, Lokesh V, Manjunath T.N, Ravindra S. Hegadi, A Predictive Model Construction for Mulberry Crop Productivity, ICACTA, Procedia Computer Science 45, pp.156-165, 2015.
[12] B. Murugesakumar, K.anandakumar, A.bharathi, a survey on soil classification methods using data mining techniques, International Journal of Current Trends in Engineering & Research (IJCTER), Vol. 2 Issue 7, pp. 43 – 47, 2016.
[13] Han J and Kamber M, “Data Mining: concepts and Techniques”, San Francisco, Morgan Kaufmann, 2001.
[14] Bhargavi, P. and Jyothi, S., Soil classification using GATREE. International journal of computer science and information Technology, Vol.2, No.5, pp.184-191, 2010.
[15] Dr. S.Hari Ganesh, Mrs. Jayasudha, Data Mining Technique to Predict the Accuracy of the Soil Fertility, International Journal of Computer Science and Mobile Computing, Vol. 4, Issue. 7, pp.330 – 333, 2015.
[16] R.S. Walse , G.D. Kurundkar , P. U. Bhalchandra, A Review: Design and Development of Novel Techniques for Clustering and Classification of Data, International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Special Issue.1, pp.19-22, 2018.
[17] V. Parashar, Use of ICT in Agriculture, International Journal of Scientific Research in Network Security and Communication,Vol-4, Issue-5, pp.8-11 ,2016.

R. Jayalakshmi, M. Savitha Devi, "Soil Fertility Prediction for Yield Productivity and Identifying the Hidden Factors through Machine Learning Algorithms," International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.596-600, 2019.

Intrusion compromises a computer by breaking its security and thereby the computer enters into an insecure state. If such an event takes place, the computer becomes vulnerable to several attacks. These attacks aim to obtain information about the target computer and the information so obtained can be used to conduct fraudulent activities. It is difficult to prevent an intrusion into the system. However, if these computer intrusions are detected in time, the administrator can be informed and necessary actions can be taken at early stages. Previous Intrusion detection system (IDS) utilized several features to detect various malicious activities. However, these IDS methods only detect specific attack. They fail when the attacks are combined. For this purpose, we propose an Intrusion Detection System in distributed environment to mitigate the individual and combination routing attacks. This paper explains the method we used to generate such a system. Our proposed system of Intrusion Detection uses feature selection techniques to determine significant features, along with the best classification method will distinguish between an attack and non-attack. We aim to increase detection accuracy and reduce false alarm rate. NSL-KDD dataset has been used to train our model. The paper also explains related work done in this field and briefly explains the network attacks and the dataset.

IDS, Intrusion Detection System, Multiple attacks, Machine Learning, Network Security

[1] M.N. Napiah, M.Y.I. Idris, R. Ramli, I. Ahmedy , “Compression header analyzer Intrusion Detection System (CHA - IDS) for 6LoWPAN communication protocol” ,IEEE Access, Vol. 6, 2018.
[2] P. Aggarwala, S.K. Sharma, “Analysis of KDD dataset attributes-class wise for intrusion detection”, Procedia Computer Science, Vol. 57, pp. 842–851, 2015.
[3] H. Chae, B. Jo, S. Choi, T. Park, “Feature selection for intrusion detection using NSL-KDD”, Recent Advances in Computer Science, pp. 184-187, 2013.
[4] S.S. Panwar, Dr. Y. P. Raiwani, “Data reduction techniques to analyze NSL-KDD dataset”, International Journal of Computer Engineering and Technology (IJCET), Vol. 5, Issue 10, pp. 21-31, October (2014).
[5] H.P.S. Sasan and M. Sharma, “Intrusion detection using feature selection and machine learning algorithm with misuse detection”, International Journal of Computer Science & Information Technology (IJCSIT), vol. 8,no.1,pp. 17-25, 2016.
[6] L.Dhanabal, Dr. S.P. Shantharajah. “A study on NSL- KDD dataset for intrusion detection system based on classification algorithms” International Journal of Advanced Research in Computer and Communication Engineering, Vol. 4, Issue 6, 2015.
[7] A. Narayan and T.J. Parvat., “An Intrusion Detection System, (IDS) with Machine Learning (ML) model combining hybrid classifiers” Journal of Multidisciplinary Engineering Science and Technology (JMEST), Vol. 2, Issue 4, April - 2015.
[8] P. Rutravigneshwaran, “A Study of Intrusion Detection System using Efficient Data Mining Techniques” International Journal of Scientific Research in Network Security and Communication, Vol. 5, Issue 6, December – 2017.
[9] M. Arora, S. Sharma, “Synthesis of Cryptography and Security Attacks” International Journal of Scientific Research in Network Security and Communication, Vol. 5, Issue 5, October – 2017.
[10] U. K. Singh, C. Joshi, S. K. Singh, “Zero day Attacks Defense Technique for Protecting System against Unknown Vulnerabilities” International Journal of Scientific Research in Computer Science and Engineering, Vol. 5, Issue 1, February – 2017.
[11] A. Ahmad , M. Asif, S. R. Ali, “Shallow Learning and Deep Learning Methods for Network security” International Journal of Scientific Research in Computer Science and Engineering, Vol. 6, Issue 5, October – 2018.

P. Patil, T. Bagwan, S. Kulkarni, C. Lobo, S.R. Khonde, "Multi-Attacks Detection in Distributed System using Machine Learning," International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.601-605, 2019.

Immediately Ontology Matching is a challenge wished in diverse packages, for example for comparison or merging functions. Many algorithms fix the matching hassle may be determined, but most of them do no longer bear in mind instances at all. Mappings are determined by means of calculating the string-similarity of labels, by way of recognizing linguistic word members of the family (synonyms, subsumptions and so on or via analyzing the content similarity. . It relies heavily on measuring the similarity between the devices of the listed times or occurrences. Since heterogeneous sources of large cases ontology develop systematically from day to day. Scalability has come out as preliminary studies on ontology problems eg matching of semantic context bases. With the expansion of semantics’ web technologies and the guide of large RDF groups and interrelated statistics and ontologies that create the cloud of linked data. It is essential to expand the tailored Instance Matching strategies that put it characterized by an unprecedented variety of resources across Which hit on matches, a high level of heterogeneity each. The schema and the example, and the rich semantics that accompany schemas defined in the sentences of expressive languages Such as OWL, RDFS.

Ontology, Instance Matching, Ontology population, Linked Data, Knowledge bases, Richness

[1] Antoine Isaac Lourens , van der Meij, Stefan Schlobach, Shenghui Wang, “An Empirical Study of Instance-Based Ontology Matching”, International Semantic Web Conference pp253-266,2007.
[2] Katrin and Tim, “Instance-Based Ontology Matching Using Different Kinds of Formalisms” , World Academy of Science, Engineering and Technology, pp163-171,2009.
[3] Rudra, Hanif and Masaki , “Resolving Scalability Issue to Ontology Instance Matching in Semantic Web”,15th International Conference on Computer and Information Technology (ICCIT)., pp 396-404,2012.
[4] S. Castano , “On the Ontology Instance Matching Problem”. ,19th International Workshop on Database and Expert Systems Applications, pp 180-185,2008.
[5] Anhoi, Pedro, Alon, “Reconciling Schemas of Disparate Data Sources: A Machine-Learning Approach.” 2001
[6] A. Ferrara, Nikolov , Noessner, “Evaluation of Instance Matching Tools”. Journal Of Web Semantics, Pages 49-60,Volume 21,2003.
[7] Katrin Zaiß and Tim Schl¨uter, “Instance-Based Ontology Matching Using Different Kinds of Formalisms”. World Academy of Science, pp 164-172, 2009.
[8] A.Ferrara, Montanelli, “Benchmarking Matching Applications on the Semantic Web. The Semanic Web: Research and Applications”, pp 108-122, 2011
[9] Aono, Seddiqui, “Ontology Instance Matching by Considering Semantic Link Cloud.” ACE`10 Proceedings of the 9th WSEAS international conference on Applications of computer engineering, pp 22-27, 2010.
[10] Katrin Simone Zaib, “Instance-Based Ontology Matching and the Evaluation of Matching Systems.” Ph.D. thesis, Heinrich Heine Universität Düsseldorf, 2010.
[11] R. Gruber, “A Translation Approach to Portable Ontology Specifications. Journal Knowledge Acquisition”, Volume 5 Issue 2, pp 199 – 220, 1993 .
[12] Sameer dass, “Comparitive Analysis of Various Type of Quality Measuring Techinque of Ontologies. Iaetsd Journal For Advanced Research In Applied Sciences “,Volume 4, Issue 6, pp 28- 34, 2017.

Sameer Dass, Suresh Kumar, "A Review on Scalability Issues Of Ontology’s Instance Matching," International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.606-609, 2019.

Among cloud computing resources like networks, servers, storage, applications and services, Storage is one of the major resource that is being used at large by the cloud users. Small to mid-sized business houses use Storage-as-a-Service to enhance business availability and reliability, managing backups and to extenuate risks involved in disaster recovery. Security, therefore, becomes the most critical aspect due to the confidential and intimate information being stored on the public cloud. In public cloud, since the services are being provided by the third party service providers, which are geographically dispersed and involve resources outside the user’s premises, the three service models (IaaS, PaaS, SaaS) face serious security threats. Public cloud can fall prey to threats like Data breaches, insufficient identity, insecure APIs, system vulnerabilities, account hijacking, malicious insiders, data loss, insufficient due diligence, DoS and abuse of cloud services. This paper presents a review on the most pressing security issues within cloud computing, the techniques that can be employed in addressing such cloud security issues and the challenges.

Cloud Computing, Security Issues, Cloud Security, Cloud Data Storage Security

[1] G. Vandana T. et al, “Improve Security of Data Access in Cloud Computing using Location”, International Journal of Computer Science and Mobile Computing, Vol.4 Issue.2, February-2015.
[2] P. Xul, et al, “SSDS-MC: Slice-based Secure Data Storage in MultiCloud Environment”, In the Proceedings of the 11th EAI International Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness (QSHINE 2015) pp. 304-309, 2015.
[3] N. Jose, C. Kanmani A, “Data Security Mode Enhancement in Cloud Environment”, Journal of Computer Engineering Vol. 10, Issue 2, 2013.
[4] V. R. Balasaraswathi, S. Manikandan, “Enhanced security for multi-cloud storage using cryptographic data splitting with dynamic approach”, In the Proceedings of the 2014 IEEE Advanced Communication, Control and Computing Technologies (ICACCCT 2014), pp. 1190-1194, 2014.
[5] S. Ajoudanian, M. R. Ahmadi, “A Novel Data Security Model for Cloud Computing”, International Journal of Engineering and Technology, Vol.4, No.3, June 2012.
[6] B. Fabian et al, “Collaborative and secure sharing of healthcare data in multi-clouds”, Information Systems, Vol. 48, pp. 132-150, 2015.
[7] D. Manivannan, R. Sujarani, “Light Weight and Secure Database Encryption using Tsfs Algorithm”, In the Proceedings of the Second International Conference on Computing Communication and Networking Technologies, Karur, pp. 1-7, 2010.
[8] Mazhar Ali, et al, “SeDaSC: Secure Data Sharing in Clouds”, Systems Journal, IEEE pp 1-10, 2014.
[9] V. Negi, M. Kalra, “Optimizing Battery Utilization and Reducing Time Consumption in Smartphones Exploiting the Power of Cloud Computing”, In the Proceedings of the Second International Conference on Soft Computing for Problem Solving (SocProS 2012), New Delhi, India, pp. 865-872, 2012.
[10] V. Negi, M. Kalra, “Framework for energy saving in the Android smartphone”, 2013 2nd International Conference on Information Management in the Knowledge Economy, India, pp. 161-165, 2013.
[11] J. Gantz, P. Miller, “The Salesforce Economy: Enabling 1.9 Million New Jobs and $389 Billion in New Revenue Over the Next Five Years”, IDC Whitepaper, IDC #US41691316, September 2016.
[12] L. Goasduff, C. Pettey, “Worldwide Public Cloud Services Market to Grow 18 Percent in 2017”, Gartner Press Releases, Stamford, February 22, 2017.
[13] J. M. Brook, et al, “The Treacherous 12 - Top Threats to Cloud Computing and Industry Insights”, Cloud Security Alliance, 2017.
[14] R. V. Dharmadhikari, et al, “Cloud Computing: Data Storage Protocols and Security Techniques”, International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.2, pp.113-118, 2018.
[15] Yogita G. Patil, Pooja S. Deshmukh, "A Review: Mobile Cloud Computing: Its Challenges and Security", International Journal of Scientific Research in Network Security and Communication, Vol.06, Issue.01, pp.11-13, 2018.

V. Negi, "Cloud Security Issues, Techniques and Concerns – An Overview," International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.610-614, 2019.