Cloud computing is a technology that enables people to access state-of-the-art resources in pay per use approach without the need for capital investment. In spite of these advantages, the organizations opting cloud technology are worried about the security of their data. Distributed Denial of Service attack is considered as one of the most aggressive attacks performed on cloud servers. The goal of this paper is to understand the multiple ways used by attackers to target the cloud service models and the modern detection mechanisms proposed by various researchers. This paper critically analyzes the latest detection approaches and throws a light on research directions in this field.

Keyword : Cloud Computing, DDoS, IaaS, PaaS, SaaS, SDN.

[1]. Kanika and N. Sidhu, "Impact of Denial of Service Attack on the Virtualization in Cloud Computing," Int. Conf. Commun. Comput. {&} Syst., pp. 56–62, 2014.
[2]. S. Khattak, N. R. Ramay, K. R. Khan, A. A. Syed, and S. A. Khayam, "A Taxonomy of botnet behavior, detection, and defense," IEEE Commun. Surv. Tutorials, vol. 16, no. 2, pp. 898–924, 2014.
[3]. I. Ristic, ModSecurity Handbook, 2nd ed. Feisty Duck, 2010.
[4]. grsk krish, "How to Secure an Apache Web Server - DZone Security," 2018. [Online]. Available: https://dzone.com/articles/howto- secure-apache-web-server. [Accessed: 10-Aug-2018].
[5]. Falko Timme, "Secure Your Apache With mod_security." [Online]. Available: ttps://www.howtoforge.com/apache_mod_security. [Accessed: 10-Aug-2018].

This paper proposed a novel framework for big data summarization,. The proposed framework works in four stages and gives a measured execution of various records outline. In this paper we proposed modified clustering algorithm and semantic approach for big summarization. The exploratory outcomes utilizing Iris dataset demonstrate that the proposed modified k-means algorithm performs superior to K-means and Kmedodis algorithm. The execution of Big Data synopsis is assessed utilizing Australian legal cases from the Federal Court of Australia (FCA) database. The experimental results demonstrate that the proposed method can summarize the big data document superior as compared with existing systems.

Keywords: Big Data, Data Summarization, MapReduce, Data Generalization, Semantic term Identification

[1]. S. Schmidt, Data is exploding: the 3 V‟s of big data. Business Computing World, 2012.
[2]. Y. Zhai, Y.-S. Ong, and IW. Tsang, The Emerging "BigDimensionality". In Proceedings of the 22nd International Conference on World Wide Web Companion, Computational Intelligence Magazine, IEEE, vol. 9, no. 3, pp. 14–26, 2014.
[3]. V. Medvedev, G. Dzemyda, O. Kurasova, and V. Marcinkeviˇcius, "Efficient data projection for visual analysis of large data sets using neural networks", Informatica, vol.22, no. 4, pp. 507–520, 2011.
[4]. G. Dzemyda, O. Kurasova, and V. Medvedev, "Dimension reduction and data visualization using neural networks", in Maglogiannis, I., Karpouzis, K., Wallace, M., Soldatos, J., eds.: Emerging Artificial Intelligence Applications in Computer Engineering. Volume 160 of Frontiers in Artificial Intelligence and Applications, IOS Press, 2007, pp. 25–49.
[5]. A. McCallum, K. Nigam,and L. Ungar, "Efficient clustering of high-dimensional data sets with application to reference matching", in Proceedings of the 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 169–178, 2000.

This paper introduces mechanisms for load balancing in a network. Load balancing is a networking method for distributing load across multiple servers. Load Balancing is usually provided by dedicated software or hardware, such as a multilayer switch or Domain Name System Server Process. There are various algorithms to perform load balancing. In this paper we will discuss how to perform load balancing using load balancers and the advantage and disadvantages of the same.

Keywords: APM, ASM, GTM, LTM, Load balancers, Nodes, VIP.

[1]. CCNA Routing and Switching Complete by Todd Lammle, ―The Official guide for CISCO Networks‖ (2014, 2nd Edition, Kindle Edition) http://www.m5zn.com/newuploads/2014/06/30/pdf/m5zn_082644c04a97cdd.pdf
[2]. Phillip Jonsson, Steven Inveson, ―F5 Networks Application Delivery Fundamentals‖, Kindle Edition, 2017. https://support.f5.com/csp/article/K29900360
[3]. ―Mastering Netscaler VPX‖, Rick Roetenberg, Marius Sandbu
[4]. Ccna Security, 1st Edition (Sybex Publications , Tim Boyles),staffweb.itsligo.ie/staff/pflynn/Telecoms%203/CCNP%202%20Secure%20WAN's/Secure%20Converged%20Networks/ CCNA%20Security.pdf

This paper studied the problem of information transmission delay in VANET, and proposed an emergency message transmission method. The method got the messages data through netfilter architecture, reassembled the warning messages with Linux virtual device, then directed the physical network card and sent it to the destination terminal. The transmission process both reduces the time delay of traditional cache and the overhead of TCP/IP peer layers. The result shows it is effectively reducing the transmission delay during the transmission process, and improving the driving safety of the vehicle.

Keywords: VANET; emergency message; Netfilter architecture; virtual device.

[1] Rene O, Carlos M, Azzedine B, et al. Reliable data dissemination protocol for VANET traffic safety applications [J]. Ad hoc Networks, 2017, 63: 30- 44.
[2] Jun Tao, Yifan Xu, Ziyi Zhang, et al. A resource allocation game with restriction mechanism in VANET cloud [J]. Concurrency& Computation Practice & Experience, 2016, 29 (14).
[3] Barakat P M, Tarek R S, Khaled S. Performance study of manet routing protocols in VANET [J]. Arabian Journal for Science& Engineering, 2016:
[4] Tariq E, Layth A K A D, Yamaan M. Review and performance comparison of VANET protocols: AODV, DSR, OLSR, DYMO, DSDV & ZRP [C]// Proc of AIC-MITCSA. 2016: 1-6.
[5] Raj K J, Jaidhar C D. Location prediction algorithm for a nonlinear vehicular movement in VANET using extended Kalman filter [J]. Wireless Networks, 2016: 1-16.

In the past few years there has been a tremendous increase in network attacks especially the Denial of Service and Distributed Denial of Service attacks. The attacker tries to send a number of packets to the victim site so as to make it unavailable to provide any service. Nowadays DoS attacks can be performed using IoT enabled devices too. Traditional network architecture are unable to handle such attacks which gave rise to Software Defined Network(SDN) as a solution.Software defined networking is a network technology that provides central control over the network and thus manages network behaviour dynamically through software via open interfaces.SDN plays a role to retain the heterogeneity in networks and objects by integrating into them a control solution that interacts with the SDN controllers............

Keywords : IoT, SDN, Open Flow, attacks, mininet.

[1]. A. García de la Villa, "Distributed Denial of Service Attacks defenses and OpenFlow: Implementing denial-of-service defense mechanisms with software defined networking", Core.ac.uk, 2017. [Online]. Available: https://core.ac.uk/display/80713323. [Accessed: 25- Oct- 2017].
[2]. Ferguson, P. Network ingress filtering: Defeating denial of service attacks which employ ip source address spoofing. Amaranth Network Inc. (2000).
[3]. Peng, T., Leckie, C., and Ramamohanarao, K. Protection from distributed denial of service attacks using history-based ip filtering. In Communications, 2003. ICC'03. IEEE International Conference on (2003), vol. 1, IEEE, pp. 482–486.
[4]. Chang, R. K. Defending against flooding-based distributed denial-of-service attacks: A tutorial. Communications Magazine, IEEE 40, 10 (2002), 42–51. Bellovin, S. M., Leech, M., and Taylor, T. Icmp traceback messages. Internet Engineering Task Force, Marina del Rey, Calif (2003).
[5]. NOX. POX Controller. http://www.noxrepo.org/pox/about-pox/. [Online; accessed 24-Oct-2017].

Under water image enhancement has been a topic of scope since last few decades. Several attempts have been made by various researchers and organizations to improve the accuracy and thereby enhance the visibility of underwater images. Normal images which are taken outside water in open environment have sufficient light to capture the scene and hence they have good visibility. However under the water light propagation is affected mainly due to absorption thereby reducing light energy and scattering effect caused by moving water which causes change in direction of light propagation. Another major factor that added noise to underwater image is turbidity of water............

Keywords: - Image fusion, underwater image, histogram equalization.

[1]. L. Torres-Mendez and G. Dudek, Color Correction of Underwater Images for Aquatic Robot Inspection, Springer, pp. 60-73, 2005.
[2]. H. Yang, P. Chen, C. Huang and Y. Zhuang, Low Complexity Underwater Image Enhancement Based on Dark Channel Prior, Innovations in Bio-inspired Computing and Applications (IBICA), Shenzhan China, 2011.
[3]. M. Hitam, W. Yussof, E. Awalludin, Z. Bachok, Mixture contrast limited adaptive histogram equalization for underwater image enhancement, International Conference on Computer Applications Technology , Sousse Tunisia, 2013.
[4]. S. Pizer, E. Amburn, J. Austin, R. Cromartie, A. Geselowitz, T. Greer, B. Romeny, J. Zimmerman and K. Zuiderveld, "Adaptive Histogram Equalization and Its Variations", Computer Vision, Graphics, and Image Processing 39, 1987, 355–368.
[5]. X. Liu, G. Zhong, C. Liu and J. Dong, Underwater image colour constancy based on DSNMF, IET Image Processing, VOL. 11(1), 2017, 38-43.

The size of patient record and other healthcare data is increasing rapidly. The data is collected using wearable devices to keep track of patient health. The storage and sharing of data is becoming complex and also data needs to be easily accessible and sharable. To overcome this problem cloud is used to store data. But the main concern is security and privacy protection of data stored in cloud. As cloud computing provides fast data storage and sharing. The privacy and security of data in cloud is maintained by using various encryption techniques. In this paper will survey different techniques to maintain privacy and security of data.

Keywords - cloud computing, data sharing, data security, wearable devices, privacy.

[1] Min Chen ,Yongfeng Qian, Jing Chen, Kai Hwang, Shiwen Mao, Long Hu, Privacy Protection and Intrusion Avoidance for Cloudlet-based Medical Data Sharing, IEEE Transactions on Cloud Computing,2016
[2] R. Lu, X. Lin, and X. Shen, Spoc: A secure and privacy-preserving opportunistic computing framework for mobile-healthcare emergency,Parallel and Distributed Systems, IEEE Transactions on, vol. 24, no. 3, pp. 614–624, 2013.
[3] L.GriffinandE.DeLeastar,Socialnetworkinghealthcare,inWearable Micro and Nano Technologies for Personalized Health (pHealth), 2009 6th International Workshop on. IEEE, 2009, pp. 75–78.
[4] N. Cao, C. Wang, M. Li, K. Ren, and W. Lou, Privacy-preserving multi-keyword ranked search over encrypted cloud data, Parallel and Distributed Systems, IEEE Transactions on, vol. 25, no. 1, pp. 222–233, 2014.
[5] Muhannad quwaider and yaser jararweh, cloudlet-based efficient data collection in wireless body area networks

In our India more than 70% people are dependent on agriculture profession. Data mining represent an important area in agriculture sector for knowledge discovery which can be helpful for the farmers in their profession. Situation of decision making for agricultural people has been changed due to more advanced techniques in information technology. Many past governments do so many efforts for improving the standard for agricultural people especially in rural areas. Government also launched one web portal like "ONE STOP SHOP FOR FARMERS". In this portal various information has been given for the agricultural people who will be helpful for their profession................

Keywords- Data mining, Information Gain, Classifier, KDD, Knowledge database

[1]. Hetal Patel, Dharmendra Patel, "A Brief survey of Data Mining Techniques Applied to Agricultural Data"International Journal of Computer Applications,Volume , 9, June 2014.
[2]. Sanket Salvi, Pramod Jain S, Sanjay H., Harshita T, M. Farhana, Naveen Jain, Suhas M,"Cloud Based Data Analysis and Monitoring of Smart Multi-level Irrigation System UsingIoT",International conference on I-SMAC (I-SMAC 2017).
[3]. M.M.Wakhare,a.G.Choure,"Design of framework for agriculture web service ", International Journal of Advances in Electronics and Computer Science, ISSN: 2393-2835 Volume-4, Issue-1, Jan.-2017
[4]. Agraj Aher, Janhavi Kasar, Palasha Ahuja , Varsha Jadhav,"Smart Agriculture using Clustering and IOT", International Research Journal of Engineering and Technology (IRJET), Volume: 05 Issue: 03 | Mar-2018 p-ISSN: 2395-007
[5]. Olakunle Elijah, Tharek Abdul RahmanIgbafe Orikumhi, Chee Yen Leow ,"An Overview of Internet of Things (IoT) and Data Analytics in Agriculture: Benefits and Challenges ", IEEE INTERNET OF THINGS JOURNAL, 2327-4662 (c) 2018 IEEE.

Sentiment Analysis can be termed as the process of estimating or determining opinion of an individual/group of individuals into various classes. At times it is referred as Opinion Mining by various researchers and practitioners. This paper presents a short survey of the work done in the field of sentiment analysis. It tries to focus on application of sentiment analysis in different domains, some of which are well explored and the others which are less explored like health care.

Keywords- Emotion Recognition(ER), Health Care, Opinion Mining(OM), Sentiment Analysis(SA).

[1]. R.Nithya , D. Maheshwari, Sentiment Analysis On Unstructured Review , Proc. 2014 International Conference on Intelligent Computing Applications (ICICA'14),pp.367-371.
[2]. Ganeshbhai, S.Y. and Shah B.K , Feature Based Opinion Mining: A Survey ,Proc. Advance Computing Conference (IACC),2015 IEEE International ISBN: 978-1-4799-8046-8,pp. 919 - 923.
[3]. Walaa Medhata,Ahmed Hassanb and Hoda Korashyb, Sentiment analysis algorithms and applications: A survey , Elsevier Science,Ain Shams Engineering Journal , Volume 5, Issue 4,2014,pp.1093–1113.
[4]. Kumar Ravia and Vadlamani Ravia, A survey on opinion mining and sentiment analysis: Tasks, approaches and applications , Elsevier Science,Journal Knowledge-Based Systems , Volume 89 Issue C,2015 ,pp. 14-46.
[5]. Jasmine Bhaskar, K. Sruthi, Prema Nedungadi, Hybrid Approach for Emotion Classification of Audio Conversation Based on Text and Speech Mining , Elsevier Science,Procedia Computer Science, Volume 46, 2015, Pages 635-643

As time progressed, the use of cloud to store data or perform different actions on data increased. Cloud services started emerging and it became more convenient for users to use cloud for storage of their data. But threat on cloud also increased considerably. After discovery of vulnerabilities in software, attacker exploited the data in the cloud. Attacks like DDOS attacks exploited the system and resulted into unavailability of services. Problems like multistep exploitation, low frequency vulnerability scanning have increased tremendously. Nice Agent which does detection or monitors the system and checks every packet. It deeply inspects the suspicious packets and detects an alertform.BDD approach is used for packet filtering. Parallel programming is used for optimisation. To prevent such attacks honeypot is used. Honeypot is a bogus IP which consist some unwanted data.

Keywords- Attack Detection, BDD, Cloud Computing, Networking, Security, Parallel Programming

[1]. Z. Duan, P. Chen, F. Sanchez, Y. Dong, M. Stephenson, and J. Barker, "Detecting spam zombies by monitoring outgoing messages," IEEE Trans. Dependable and Secure Computing, vol. 9, no. 2, pp. 198–210, Apr. 2012.
[2]. G. Gu, P. Porras, V. Yegneswaran, M. Fong, and W. Lee, "BotH-unter: detecting malware infection through IDS-driven dialog correlation," Proc. of 16th USENIX Security Symp. (SS '07), pp. 12:1–12:16, Aug. 2007
[3]. G. Gu, J. Zhang, and W. Lee, "BotSniffer: detecting botnet com-mand and control channels in network traffic," Proc. of 15th Ann.Network and Distributed Sytem Security Symp. (NDSS '08), Feb.2008.
[4]. O. Sheyner, J. Haines, S. Jha, R. Lippmann, and J. M. Wing, "Automated generation and analysis of attack graphs," Proc. IEEESymp.on Security and Privacy, 2002, pp. 273–284.
[5]. "NuSMV: A new symbolic model checker," http://afrodite.itc.it: 1024/∼nusmv. Aug. 2012.

The paper explains Enhanced Histogram of oriented gradients, a feature extractor which extract the relevant features and gives better performance in terms of Accuracy, Computation time, error rate when applied with Neural Network for classification. It has been observed through Analysis or experimentation that it gives better performance in detection of EUS(Epizootic ulcerative syndrome) disease.

Keywords - EUS (Epizootic ulcerative syndrome), EHOG(Enhanced Histogram of oriented Gradients),Neural Network(NN), PCA(Principle Component Analysis)

[1]. Tacon, A. G., Metian, M. ,"Fish matters: Importance of aquatic foods in human nutrition and global food supply", Reviews in Fisheries Science,vol. 21,issue 1,pp22-38, 2013.
[2]. Lafferty, K. D., Harvell, C. D., Conrad, J. M., Friedman, C. S., Kent, M. L., Kuris, A. M., Saksida, S. M.,"Infectious diseases affect marine fisheries and aquaculture economics", Annual review of marine science, vol7, pp- 471-496, 2015.
[3]. Jayme G. A. BarbedoBrazilian ,"Computer-Aided Disease Diagnosis in Aquaculture: Current state and Perspectives for the future" ,Revista Innover, volume 1, Number 1,pp 19-32,2014
[4]. Bagum N, Monir MS, Khan MH (2013) "Present status of fish diseases and economic losses due to incidence of disease in rural freshwater aquaculture of Bangladesh",Journal of Innovation & Development Strategy (JIDS) , Vol 7,issue 3,pp-48-53, 2013
[5]. E. J. Peeler and N. G. Taylor, "The application of epidemiology in aquatic animal health -opportunities and challenges," Veterinary Research, vol. 42, no. 1, 2011.