Volume-7 , Issue-8 , Aug - 2019 Go Back

Internet of Things (IoT) may be a system of reticular computing devices wherever all the items, as well as each entity, is connected - creating those objects intelligent, programmable and capable of interacting with humans. The user operates the good home devices in year out, have made mass operation knowledge, however these knowledges haven`t been utilized well within the past. This project focuses on the event of home automation system supported IOT that permits the user to alter all the devices and appliances of home and merge them to produce seamless management over each aspect of their home. The information is accustomed predict the user’s behavior custom with the event of a machine learning algorithmic program and the prediction results is used to boost the intelligence of a sensible home system. The designed system not solely provides the detector knowledge however additionally method it in keeping with the need, for instance switch on the sunshine once it gets dark and it permits the user to manage the social unit devices from anyplace. The cloud is employed to send the detector knowledge through Wi-Fi module and so a choice tree is enforced that decides the output of the electronic devices additionally, it`s accustomed reach the ability management and native knowledge exchanging which give the computer program, store all the data similar to the particular house, and question the operate info of a private household appliance.

IoT, Machine learning, Cloud, Google Assistant.

M. Aparna, M. Siva Naga Raju, "Wireless Home Automation System using Internet of Things," International Journal of Computer Sciences and Engineering, Vol.7, Issue.8, pp.251-253, 2019.

A milestone discovery in the subject of computer science in field of “Artificial Intelligence” to predict the future using Neural Network of Small events or limited variable events having few floating variables plays’s an essential role in our project. These variables can be economical as well as political or power shift in variable for predicting stock indices to have accurate prediction. This variable is used in hidden layer at multistage to iterate the value for best outcomes. To overcome such a huge calculation and trained our machine to operate individually to take such decision, we need to develop a neuron-like structure to look every possibility of outcome using “Neural Network”. Current demands of rocket trend in stock market for the assessment of health of country market and consumer power along with trust-building on company. In this paper we had implemented our research on TCS-SET’s (in Indian Stock Market) using Neural Network. This Research paper support Neural Network as it has fast computational advantage along with handling many variables at a time. The stock market closing is very important as it contributes to national growth, so a cat eye is needed on stock closing price. It also promotes the investor to invest or withdraw their share value from stock market before fall of its value. This unique quest of time and money in trade with computer knowledge help in forecasting of stock market along with Neural Network.

Artificial Neural Network, TCS, Stock Market

[1] S. Lee, D. Enke, and Y. Kim, “A relative value trading system based on a correlation and rough set analysis for the foreign exchange futures market,” Eng. Appl. Artif. Intell., vol. 61, no. February, pp. 47–56, 2017.
[2] S. Pyo, J. Lee, M. Cha, and H. Jang, “Predictability of machine learning techniques to forecast the trends of market index prices: Hypothesis testing for the Korean stock markets,” PLoS One, vol. 12, no. 11, pp. 1–17, 2017.
[3] F. Wang, Y. Zhang, Q. Rao, K. Li, and H. Zhang, “Exploring mutual information-based sentimental analysis with kernel-based extreme learning machine for stock prediction,” Soft Comput., vol. 21, no. 12, pp. 3193–3205, 2017.
[4] C. M. Anish and B. Majhi, “Hybrid nonlinear adaptive scheme for stock market prediction using feedback FLANN and factor analysis,” J. Korean Stat. Soc., vol. 45, no. 1, pp. 64–76, 2016.
[5] F. Ye, L. Zhang, D. Zhang, H. Fujita, and Z. Gong, “A novel forecasting method based on multi-order fuzzy time series and technical analysis,” Inf. Sci. (Ny)., vol. 367–368, pp. 41–57, 2016.
[6] C. Bousono-Calzon, J. Bustarviejo-Munoz, P. Aceituno-Aceituno, and J. J. Escudero-Garzas, “On the Economic Significance of Stock Market Prediction and the No Free Lunch Theorem,” IEEE Access, vol. 7, pp. 75177–75188, 2019.
[7] A. M. Rather, A. Agarwal, and V. N. Sastry, “Recurrent neural network and a hybrid model for prediction of stock returns,” Expert Syst. Appl., vol. 42, no. 6, pp. 3234–3241, 2015.
[8] L. Y. Wei, “A hybrid ANFIS model based on empirical mode decomposition for stock time series forecasting,” Appl. Soft Comput. J., vol. 42, pp. 368–376, 2016.
[9] K. Chourmouziadis and P. D. Chatzoglou, “An intelligent short term stock trading fuzzy system for assisting investors in portfolio management,” Expert Syst. Appl., vol. 43, pp. 298–311, 2016.
[10] J. Patel, S. Shah, P. Thakkar, and K. Kotecha, “Predicting stock and stock price index movement using Trend Deterministic Data Preparation and machine learning techniques,” Expert Syst. Appl., vol. 42, no. 1, pp. 259–268, 2015.
[11] R. Geng, I. Bose, and X. Chen, Prediction of financial distress: An empirical study of listed Chinese companies using data mining, vol. 241, no. 1. Elsevier B.V., 2015.
[12] M. Ballings, D. Van Den Poel, N. Hespeels, and R. Gryp, “Evaluating multiple classifiers for stock price direction prediction,” Expert Syst. Appl., vol. 42, no. 20, pp. 7046–7056, 2015.
[13] P. C. Chang, J. L. Wu, and J. J. Lin, “A Takagi-Sugeno fuzzy model combined with a support vector regression for stock trading forecasting,” Appl. Soft Comput. J., vol. 38, pp. 831–842, 2016.
[14] S. Deng, K. Yoshiyama, T. Mitsubuchi, and A. Sakurai, “Hybrid Method of Multiple Kernel Learning and Genetic Algorithm for Forecasting Short-Term Foreign Exchange Rates,” Comput. Econ., vol. 45, no. 1, pp. 49–89, 2013.
[15] E. Chong, C. Han, and F. C. Park, “Deep learning networks for stock market analysis and prediction: Methodology, data representations, and case studies,” Expert Syst. Appl., vol. 83, pp. 187–205, 2017.
[16] M. Thenmozhi and G. Sarath Chand, “Forecasting stock returns based on information transmission across global markets using support vector machines,” Neural Comput. Appl., vol. 27, no. 4, pp. 805–824, 2016.
[17] R. Dash and P. K. Dash, “Efficient stock price prediction using a Self Evolving Recurrent Neuro-Fuzzy Inference System optimized through a Modified technique,” Expert Syst. Appl., vol. 52, pp. 75–90, 2016.

Priyanka Garg, Sumit Sharma, "The Application of Neural Network in Stock Market (TCS)," International Journal of Computer Sciences and Engineering, Vol.7, Issue.8, pp.254-259, 2019.

Cloud computing is an emerging technology. It allows the customer to run the application by provisioning on-demand resources. There are cloud providers like AWS who allows their customer to provision resources. Customer may provide access to the multiple users to enable them to run their workloads using different services like EC2, S3, RDS, etc. With the help of different services provided by AWS, we can reserve computer power, storage, etc. Amazon Web Services (AWS) allows the customer to assign tags on their resources. The tags also known as metadata can be used to organize and manage resources in cloud computing. Tagging the resources in multiple accounts of AWS cloud computing is subject to errors and additional efforts. Our goal is to design a solution to automatically assign the tag on the resources so that we can easily organize and supervise resources in multi-account of AWS.

Cloud Computing, AWS, Resource Tagging

[1]. https://docs.aws.amazon.com/whitepapers/latest/aws-verview/what-is-cloud-computing.html
[2]. https://docs.aws.amazon.com/whitepapers/latest/aws-overview/introduction.html
[3]. https://aws.amazon.com/lambda/
[4]. https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/concepts.html
[5]. https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/Using_Tags.html
[6]. https://docs.aws.amazon.com/IAM/latest/UserGuide/id_users.html
[7]. https://aws.amazon.com/cloudtrail/
[8]. https://docs.aws.amazon.com/AmazonCloudWatch/latest/events/CWE_GettingStarted.html
[9]. https://aws.amazon.com/cloudwatch/
[10]. https://docs.aws.amazon.com/AmazonCloudWatch/latest/events/CloudWatchEvents-CrossAccountEventDelivery.html
[11]. https://aws.amazon.com/answers/account-management/aws-tagging-strategies/
[12]. https://aws.amazon.com/answers/account-management/aws-multi-account-billing-strategy/
[13]. https://aws.amazon.com/organizations/
[14]. https://d1.awsstatic.com/whitepapers/aws-tagging-best-practices.pdf
[15]. https://docs.aws.amazon.com/IAM/latest/UserGuide/tutorial_cross-account-with-roles.html
[16]. https://docs.aws.amazon.com/AmazonCloudWatch/latest/events/Create-CloudWatch-Events-CloudTrail-Rule.html
[17]. https://docs.aws.amazon.com/AmazonCloudWatch/latest/events/WhatIsCloudWatchEvents.html
[18]. https://docs.aws.amazon.com/IAM/latest/UserGuide/id_roles.html
[19]. https://docs.aws.amazon.com/AmazonCloudWatch/latest/logs/CloudWatchLogsConcepts.html

Israrul Haque, Ashif Ali, "Organize and Supervise Resources in Multi-account of AWS," International Journal of Computer Sciences and Engineering, Vol.7, Issue.8, pp.260-262, 2019.

This paper presents Data fusion and Internet of things (IoT) approach in Fire Disaster Management. Data fusion techniques in Internet of Things were used for predicting and detecting early fire outbreaks in households and industrial premises. Smoke, temperature and voltage measurement sensory data were used in the system for early fire detection. Action Research Methodology was adopted in carrying out research and UML was used as design tool. The architectural design consists of contextual information such as smoke, room temperature and electricity voltage level as an input. The system was implemented using JavaScript and PHP environment to verify the performance of the proposed system. Dynamic simulations were performed using a real time data obtained from River State Fire Service, Port Harcourt, Rivers State, Nigeria. The performance of the proposed system indicates that data fusion-based system with the use of smoke, temperature and voltage detector is able to detect fires more reliable and highly accurate from the fire detection unit than one sensory data. The results were promising indicating the real state of fire outbreak prediction

Data fusion, context awareness, Internet of Thing, multi-sensors, smart environments, disaster detection

[1]. Mitchell, H. B. “Multi-sensor data fusion: an introduction”. Springer Science & Business Media, 2007.
[2]. Din, S., Awais A., Anand P., Muhammad M. U. R., and Gwanggil J., "A cluster-based data fusion technique to analyze big data in wireless multi-sensor system." IEEE Access Issue.5 pp.5069-5083, 2017.
[3]. Ao, S. I., Mahyar A., and Burghard B. R., eds. “Intelligent Automation and Systems Engineering”. Springer Science & Business Media, Vol.103, 2011.
[4]. Myat, S. N., Hla, M. T., “Implementation of Multisensor Data Fusion Algorithm”, International Journal of Sensors and Sensor Networks. Vol.5, Issuie.4, pp.48-53, 2017.
[5]. Ojas S., Anup M., Deepika A., Sushmita S., “Internet of Things in Precision Agriculture using Wireless Sensor Networks”. International Journal of Advanced Engineering & Innovative Technology, Vol.2, Issue.3, 2015.
[6]. Perera, C., A. Zaslavsky, P. Christen, and D. Georgakopoulos. “Context awareness for internet of things”. In IEEE International Conference on Conference on Internet of Things (iThing), pp.775–782, 2012 Besanon, France, November.
[7]. Alam, F. Mehmood, R., Katib, I., Albogami, N. N. & Albeshri, A. "Data Fusion and IoT for Smart Ubiquitous Environments: A Survey," in IEEE Access, Issue.5, pp.9533-9554, 2017.
[8]. Wang, M., Perera, C., Jayaraman, P. Zhang, M., Strazdins, P., Shyamsundar, R.K. and Ranjan, R. City data fusion: Sensor data fusion in the internet of things. -5225-1832-7, 2017.
[9]. Gite, S., and Agrawal, H. "On context awareness for multisensor data fusion in IoT", Proc. 2nd International Conference of Computer Communication Technology, Vol.381, pp.85-93, 2015.
[10]. Ying-Yao Ting, Chi-Wei HSIAO, Huan-Sheng WANG, A Data Fusion-Based Fire Detection System, IEICE Transactions on Information and Systems, Vol.E101.D, Issue.4, pp.977-984. 2018.
[11]. Chang, N. B., Bai, K. X., Imen, S., Chen, C. F., and Gao, W., Multi-sensor satellite image fusion, networking, and cloud removal for all-weather environmental monitoring. IEEE Systems Journal, pp.1–17, 2016.
[12]. Davood, I., Jemal, H., Abawajy , S.G. and Tutut, H. “A Data Fusion Method in Wireless Sensor Networks” Sensors Vol.15, Issue.2:, pp.2964-2979, 2015.
[13]. King, C. R., Villeneuve, E., White, R., Sherratt, R., Holderbaum, W. and Harwin, W. Application of data fusion techniques and technologies for wearable health monitoring. Medical Engineering & Physics. Vol.42, Issue.12, pp.11, 2017.
[14]. Erdelj, I., Jemal, H., Abawajy , S. G. and Tutut, H. “A Data Fusion Method in Wireless Sensor Networks” Sensors, Vol.15, Issue.2, pp.2964-2979, 2015.
[15]. Nuka, B., Big data and fusion. International Journal of Image and Data Fusion, Vol.6, Issue.1, pp.1-2, 2012.
[16]. O’Brien D., “A survey on internet of things from industrial market perspective.” Access, Institute of Electrical and Electronics Engineers (IEEE), Issue.2, pp.1660–1679, 2011.
[17]. Elliot, C. H., Big Data Fusion in Internet of Things. Information Fusion. Vol.40, Issue.10, pp.1016. 1999.
[18]. Jacobson, B., Din, A., Ahmad, A., Paul, M. M., Ullah R., and Jeon, G., "A cluster-based data fusion technique to analyze big data in wireless multi-sensor system," in Institute of Electrical and Electronics Engineers (IEEE) Issue.5, pp.5069-5083, 2009.
[19]. Ivar, Y. A., Md Din N., Yussof, S., and Ullah Khan S., "Big data analytics for Flood Information Management in Kelantan, Malaysia," in Proc. IEEE Student Conference on Research and Development (SCOReD), pp.311-316. 2012.
[20]. Booch, G., “The Unified Modeling Language User Guide.” J. Database Manag.Issue.10, pp.51-52, 1999.

Adeosun A. Tajudeen, Nuka D. Nwiabu, "Data Fusion and Internet of Things (IoT) Approach in Fire Disaster Management," International Journal of Computer Sciences and Engineering, Vol.7, Issue.8, pp.263-268, 2019.

A firewall is a critical security appliance for the mitigation of the security attacks not only in the traditional network, but also in software-defined networking (SDN). Previous firewall applications over SDN controller are implemented with one of two firewall concepts: centralized firewall and distributed firewall. Centralized firewall method incurs controller overhead problem as the controller acts as a centralized firewall which maintains firewall rules and filters out the traffic. Distributed firewall method comes out the complicated firewall configuration, additional cost in rules maintenance in each switch, and less sensitive to the topology. This system proposes a firewall rules installation based on topology-aware selectively distributed stateful firewall with source-based DoS attack defense mechanism. The purpose of this system is to overcome not only the performance issues but also security issues. This paper finally shows that the stateful firewall application can not only track the TCP flow, but also reduce latency plus table lookup time up to 16% in long-lived flow and 50% in short-lived flow. Moreover, according to the security perspective, the accuracy for the DOS detection and mitigation of stateful firewall application is 98.93 % of SYN flooding attack and 92.09% for UDP flooding attack.

Stateless Firewall, Stateful Firewall, SDN

[1] Tran, Thuy Vinh, and Heejune Ahn. "Flowtracker: A SDN Stateful Firewall Solution with Adaptive Connection Tracking and Minimized Controller Processing." Software Networking (ICSN), 2016 International Conference on. IEEE, 2016.
[2] Tran, Thuy Vinh, and Heejune Ahn. "A network topology-aware selectively distributed firewall control in SDN." Information and Communication Technology Convergence (ICTC), 2015 International Conference on. IEEE, 2015.
[3] Pena, Justin Gregory V., and William Emmanuel Yu. "Development of a distributed firewall using software defined networking technology" Information Science and Technology (ICIST), 2014 4th IEEE International Conference on. IEEE, 2014.
[4] Arins, Andis. "Firewall as a service in SDN OpenFlow network" Information, Electronic and Electrical Engineering (AIEEE) , 2015 IEEE 3rd Workshop on Advances in. IEEE, 2015.
[5] Rao, S., and S. Rao. "Denial of service attacks and mitigation techniques: Real time implementation with detailed analysis" This paper is from the SANS Institute Reading Room site (2011).
[6] Rajkumar, M. Nene. "A Survey on Latest DoS Attacks: Classification and Defense Mechanisms" IJIRCCE 1.8 (2013).
[7] Ivan Pepelnjak, “What can openflow tables do?”, https://www.youtube.com/watch?v=7R91K0d2r2E.
[8] “Attack Detection and Defense Mechanisms" Juniper Networks, Inc.1194 North Mathilda Avenue Sunnyvale, California 94089 USA 408-7 45-2000 www.juniper.net, 2016.
[9] Morzhov, Sergey, Igor Alekseev, and Mikhail Nikitinskiy. "Firewall application for Floodlight SDN controller" Control and Communications (SIBCON), 2016 International Siberian Conference on. IEEE, 2016.
[10] Pena, Justin Gregory V., and William Emmanuel Yu. "Development of a distributed firewall using software defined networking technology" Information Science and Technology (ICIST), 2014 4th IEEE International Conference on. IEEE, 2014.
[11] Suh, Michelle, et al. "Building firewall over the software-defined network controller" Advanced Communication Technology (ICACT), 2014 16th International Conference on. IEEE, 2014.
[12] Trabelsi, Zouheir. "Teaching stateless and stateful firewall packet filtering: A hands-on approach" 16th Colloquium for Information Systems Security Education. 2012.
[13] Dillon, C., and Michael Berkelaar. “OpenFlow (D) DoS Mitigation”. Technical report (February 2014), http://www. delaat. net/rp/2013-2014/p42/report. pdf, 2014.
[14] Low, Christopher. "Icmp attacks illustrated" SANS Institute URL: http://rr. sans. org/threats/ICMP attacks. php (12/11/2001) (2001).
[15] Shieha, Alaauddin. "Application Layer Firewall Using OpenFlow" (2014).
[16] Mininet Network Emulator, http://mininet.org.
[17] Xargs command, Internet:http://man7.org/linux/man-pages/man1 /xargs.1.html.
[18] Hping3 Security Tool[online]. Available from: https://www.hping. org/hping3.html.
[19] D-ITG, Distributed Internet Traffic Generator, http://www.grid.unina.it/software/ITG/.
[20] Tcpdump[online].Availablefrom: https://www.tcpdump.org/manpages/tcpdump.1.html.
[21] Y. Perwej, “The Hadoop Security in Big Data: A Technological Viewpoint and Analysis”, International Journal of Scientific Research in Computer Science and Engineering, Vol.7, Issue.3, pp.1-14, June 2019.
[22] G. Abare, “A Proposed Model for Enhanced Security against Key Reinstallation Attack on Wireless Networks”, International Journal of Scientific Research in Network Security and Communication, Volume-7, Issue-3, ISSN: 2321-3256, Jun 2019.

Aung Htein Maw, "Effective Stateful Firewall in Software-Defined Networking," International Journal of Computer Sciences and Engineering, Vol.7, Issue.8, pp.269-274, 2019.

Business Analytics is a set of techniques and processes that can be used to analyze data to improve business performance through fact-based decision-making. Business analytical applications are designed to retrieve, analyze, transform and report data for business intelligence. These business analytics applications give the organization a complete overview of the company to provide key insights and understanding of the business. So smarter decisions may be made regarding business operations, customer conversions and more. A business analytics architecture is used to build business analytical applications for reporting and data analytics. The existing business analytics architecture is designed with Data Warehouse and the data flow among various business components is unidirectional. Today, Data Lake offers an optimal foundation for modern business analytics. In the literature survey, no business analytics architecture is available with Data Lake solutions. Hence, it is proposed to design a business analytics architecture with Data Lake to meet the needs of modern business organizations. The proposed business analytics architecture supports all standardized business analytic reports with Big Data analysis. The proposed business analytics architecture provides many advantages when it comes to scalability, speed, data quality, and flexibility.

Business analytics, business intelligence, business environment, business architecture, Data Lake

[1]. Business Analytics – Wikipedia, https://en.wikipedia.org/wiki/Business_analytics
[2]. The importance of business analytics, 2017. https://www.businessblogshub.com/2017/07/the-importance-of-business-analytics/
[3]. Business Intelligence - Architecture, Components and its Benefits.
https://www.managementstudyguide.com/business-intelligence.htm
[4]. NEUSTAR, A Case Study, 2017. https://www.hortonworks.com/
[5]. Hortonworks, Data Architecture Optimization, Data Architecture Optimization Customer Use Cases, 2016.
[6]. Edureka, Understanding Pentaho Architecture, 2018. https://www.edureka.co/
[7]. Fuse by Cardinal, https://www.cardinalhealth.com/ en/ about-us/ who-we-are/ fuse-by-cardinal-health.html
[8]. Gintarė Vizgaitytė, Rimvydas Skyrius, Business “Intelligence in the process of decision-making: Changes and Trends”, ekonomika, Vol. 91, Issue.3, 2012.
[9]. IBM Software Group, Cognos Enterprise Business Analytics, The right architecture for business intelligence. The foundation for effective enterprise BI, IBM Corporation, 2012.
[10]. Mike Barlow, Real-time big data analytics: Emerging architecture tools and technologies driving real-time big data analytics, 2015.
[11]. N Chandler, et al., Gartner`s business analytics framework, Gartner, Inc., 2011.
[12]. Nenshad Bardoliwalla, Paxata, Paxata Solution and Hortonworks Data Platform, 2016.
[13]. Nicolaus Henke., et al. The age of analytics: Competing in a Data-driven world, 2016.
[14]. OBAWA, Oracle business analytics warehouse architecture, 2012.
[15]. Paramita Ghosh, Data Strategy Needs to Include a Robust Data Architecture, 2018.
[16]. P Needleman, M K Sternitzke, Modern Business Intelligence: The path to big data analytics, Delloite, 2018.
[17]. Ulrika Jägare Unified Analytics Databricks, Special Edition, John Wiley & Sons, Inc. 2019.
[18]. Oracle, An Enterprise Architect’s Guide to Big Data Reference Architecture Overview, 2016.
[19]. ClearPeaks. What can Big Data do for BI? https://www.clearpeaks.com/what-can-big-data-do-for-bi/
[20]. Srinivasa Kalyanachakravarthy Vasagiri, Data Architecture Optimization with Hadoop, 2016.
[21]. K. Palanivel, “Modern network analytics architecture stack to enterprise networks”, Intel. journal for research in applied science & engineering technology, Volume 7, Issue IV, 2019.
[22]. Hugh J. Watson, “Business Analytics Insight: Hype or Here to Stay?”, Business Intelligence Journal, Volume 16, Issue 1, 2010.

Palanivel K, Manikandan J, "Business Analytics Architecture Stack to Modern Business Organizations," International Journal of Computer Sciences and Engineering, Vol.7, Issue.8, pp.275-287, 2019.

Cloud computing is a set of web-based resources and services. Cloud services are delivered worldwide from data centers. By providing virtual resources via the internet, cloud computing facilitates its consumers. Google apps, provided by Google and Microsoft SharePoint, are a general example of cloud services. The rapid growth in the "cloud computing" field also increases serious security concerns. Security remained a constant problem for Open Systems and the Internet. Lack of security is the only obstacle to broad cloud computing adoption. The cloud computing boom has created many security challenges for consumers and service providers. This survey aims to identify the most vulnerable security threats in cloud computing, enabling both end users and vendors to understand the key security threats associated with cloud computing. Our work will enable researchers and security professionals to know about the concerns of users and vendors and critical analysis of the various proposed security models and tools. In a cloud computing environment, all data resides over a set of networked resources, enabling access to data via virtual machines. Because these data centers may be beyond the reach and control of users in any corner of the world, there are multiple security and privacy challenges that need to be understood and addressed. There are various issues that need to be addressed in a cloud computing scenario regarding security and privacy. This survey paper aims at elaborating and analyzing the numerous unresolved issues that threaten the adoption and diffusion of cloud computing.

Cloud computing, Cloud Security , DDoS attacks, Software as a Service (SaaS), Platform as a Service (PaaS), Infrastructure as a Service (IaaS).

[1] H. Abbas, O. Maennel, S. Assar, “Security and privacy issues in cloud computing”, Institut Mines-Télécom and Springer-Verlag France 2017.
[2] M. H. Khan, “A Survey of Security Issues For Cloud Computing”, Journal of Network and Computer Applications, ELSEVIER 2016.
[3] B. Duncan, M. Whittington, “Enhancing Cloud Security and Privacy: The Power and the Weakness of the Audit Trail”, CLOUD COMPUTING 2016 : The Seventh International Conference on Cloud Computing, GRIDs, and Virtualization.
[4] D. Puthal, B. P. S. Sahoo, S. Mishra, S. Swain, “Cloud Computing Features, Issues and Challenges: A Big Picture”, 2015 International Conference on Computational Intelligence & Networks (CINE 2015)
[5] M. Ali, S U. Khan, A V. Vasilakos, “Security in cloud computing: Opportunities and challenges”, Information Sciences 305 (2015) pp 357–383 ELSEVIER.
[6] M.Al Morsy, Z.Grundy, Ingo Müller, “An Analysis of the Cloud Computing Security Problem”, 17th Asia-Pacific Software Engineering Conference (APSEC 2010) Cloud Workshop, Sydney, Australia, 30 November-03 December 2010.
[7] Rongxing et al, ―Secure Provenance: The Essential Bread and Butter of Data Forensics in Cloud Computing‖, ASIACCS‘10, Beijing, China..
[8] R. La‘Quata Sumter, ―Cloud Computing: Security Risk Classification‖, ACMSE 2010, Oxford, USA
[9]Mladen A. Vouch, ―Cloud Computing Issues, Research and Implementations‖, Journal of Computing and Information Technology - CIT 16, 2008, 4, 235–246
[10]Wenchaoet al, ―Towards a Data-centric View of Cloud Security‖, CloudDB 2010, Toronto, Canada
[11] Soren Bleikertz et al, ―Security Audits of Multi-tier Virtual Infrastructures in Public Infrastructure Clouds‖, CCSW 2010, Chicago, USA.
[12] Wayne A. Jansen, ―Cloud Hooks: Security and Privacy Issues in Cloud Computing‖, 44th Hawaii International Conference on System Sciesnces 2011.
[13] Dan Lin & Anna Squicciarini, ―Data Protection Models for Service Provisioning in the Cloud‖, SACMAT‘10, 2010, Pittsburgh, Pennsylvania, USA
[14]. Cong Wang, Qian Wang, Kui Ren, and Wenjing Lou, “Ensuring Data Storage Security in Cloud Computing,”
17th International workshop on Quality of Service, USA, pp.1-9, July 13-15, 2009, ISBN: 978-1-4244-3875-4
[15].Shantanu Pal, Sunirmal Khatua, Nabendu Chaki, Sugata Sanyal, “A New Trusted and Collaborative Agent Based Approach for Ensuring Cloud Security,” Annals of Faculty Engineering Hunedoara International Journal of Engineering (Archived copy), scheduled for publication in vol. 10, issue 1, January 2012. ISSN: 1584-2665.
[16]Josh Karlin, Stephanie Forrest, Jennifer Rexford, “Autonomous Security for Autonomous Systems,” Proc. Of Complex Computer and Communication Networks; vol. 52, issue. 15, pp. 2908- 2923, Elsevier, NY, USA, 2008.
[17] I Foster, Yong Zhao, I Raicu, and S Lu. Cloud computing and grid computing 360-degree compared. In Grid Computing Environments Workshop, 2008. GCE `08,
[18] Peter Mell and Tim Grance. The NIST definition of cloud computing. National Institute of Standards and Technology, 53(6):50, 2009. 7, 9
[19] William; Athley Ambrose. Cloud Computing : Security Risks, SLA, and Trust. 2010. With Cloud Computing becoming a popular term on the Information Technology (IT) market, security and accountability has become important issues to highlight.
[20] S. Ramgovind, Mariki M. Elo_, and E. Smith. “The management of security in cloud computing “ Information Security for South Africa (ISSA), 2010
[21]Parkhill D (1966) The challenge of the computer utility. Addison- Wesley, Reading
[22] D. Naccache et al., “Buffer Overflow Attacks,” Encyclopedia of Cryptography and Security, Boston, MA: Springer US, 2011, pp. 174–177.
[23] K.S. Kumar and N.R. Kisore, “Protection against Buffer Overflow Attacks through Runtime Memory Layout Randomization,” in Intl Conf. on Information Technology, 2014, pp. 184–189.
[24] C.P. Pfleeger and S.L. Pfleeger, "Security in computing," Prentice Hall, 2006.
[25] C. Cowan et al., “StackGuard: Automatic Adaptive Detection and Prevention of Buffer-Overflow Attacks, ” 7th USENIX Security Symp., San Antonio, Texas, Jan. 1998.
[26] S.K. Das, K. Kant, and N. Zhang, "Handbook on securing cyber-physical critical infrastructure," Morgan Kaufmann, 2012.
[27] A. Chonka, Y. Xiang, W. Zhou, and A. Bonti, “Cloud security defence to protect cloud computing against HTTP-DoS and XML-DoS attacks,” J. Netw. Comput. Appl., vol. 34 (4), July 2011, pp. 1097–1107.
[28] A. Mitrokotsa and C. Douligeris, “Detecting denial of service attacks using emergent self-organizing maps,” 5th IEEE Intl. Sym. on Signal Processing and Information Technology, 2005, pp. 375–380.
[29] P.L.S. Kumari and A. Damodaram, “An Alternative Methodology for Authentication and Confidentiality Based on Zero Knowledge Protocols Using Diffie-Hellman Key Exchange,” Intl. Conf. on Information Technology, 2014, pp. 368–373.
[30] Z. Wang, K. Sun, S. Jajodia, and J. Jing, “Proof of Isolation for Cloud Storage,” Secure Cloud Computing, Springer, New York, 2014, pp. 95–121. [31] L. Rodero-Merino, L. Vaquero, E. Caron, F. Desprez, and A. Muresan, “Building Safe PaaS Clouds: a Survey on Security in Multitenant Software Platforms,” Computers and Security, vol. 31 (1), Feb. 2012.
[32]R.L. Krutz and R.D. Vines, "Cloud security : a comprehensive guide to secure cloud computing,"Wiley, 2010.

Smita Sharma, R.P. Singh, "A Critical Survey on: Cloud Security and privacy issues and its associated solutions," International Journal of Computer Sciences and Engineering, Vol.7, Issue.8, pp.288-296, 2019.

The fields of science, engineering and technology are producing data at an exponential rate important to Exabyte(s) of data everyday life. Big data helps us to travel and re-invent many areas not limited to education, health and law. This paper surveys big data with underlining the big data analytics in healthcare analysis. Big data methodologies can be used for the healthcare data analytics which provide the better decision to accelerate the business profit and customer affection, acquire a better understanding of market behaviors and trends and to provide E-Health services using Digital imaging and communication in Medicine. This paper presents an overview of big data and Health care system benefits, Tools and challenges and review on it.

Big data, Health care analysis, Challenges, Tools

[1]. Raghupathi W: “Data Mining in Health Care”. In Healthcare Informatics: Improving Efficiency and Productivity. Edited by Kudyba S. Taylor & Francis; 2010:211–223.
[2]. Professor Alex Pentland,Dr Todd G Reid, and Dr Tracy Heibeck, “Big Data and Health Revolutionizing
Medicine and Public Health”, Report of the Big Data and Health Working Group 2013, pp:6
[3] M. K.Kakhani, S. Kakhani and S. R.Biradar, “Research issues in big data analytics”, International Journal of Application or Innovation in Engineering & Management, 2(8) (2015), pp.228-232.
[4] MH. Kuo, T. Sahama, A. W. Kushniruk, E. M. Borycki and D. K. Grunwell, “Health big data analytics: current perspectives, challenges and potential solutions”, International Journal of Big Data Intelligence, 1 (2014), pp.114-126.
[5] Jasleen Kaur Bains “Big Data Analytics in Healthcare- Its Benefits, Phases and Challenges” International Journal of Advanced Research in Computer Science and Software Engineering, Volume 6, Issue 4, April 2016.
[6]. N. Vanjulavalli, M. Saravanan. “The Impact of Big Data Applications in Health Care Industry”, International Journal of Computer Sciences and Engineering, Vol.-7, Special Issue, 4, Feb 2019, PP:325-327.
[7].P.Muthulakshmi1, S. Udhayapriya, “A Survey on Big Data Issues and Challenges”, International Journal of Computer Sciences and Engineering, Vol.-6, Issue-6, Jun 2018, pp: 1238-1244
[8]. Neha Maurya1 , Anirudh Tripathi , Pankaj Pratap Singh , Amit Kishor4, “Big Data Analysis for Predictive Healthcare Information System”, International Journal of Computer Sciences and Engineering, Vol.-7, Issue-6, June 2019, PP: 47- 51.

P. Sharmila, P. Rathiga, "Big Data in Health Care Analysis: A Survey," International Journal of Computer Sciences and Engineering, Vol.7, Issue.8, pp.297-300, 2019.

Most operating systems services and network devices, such as Firewalls, generate huge amounts of network data in the form of logs and alarms. Theses log files can be used for network supervision and debugging. One important function of log files is logging security related or debug information, for example logging error logging and unsuccessful authentication. In this study, 500,000 instances, which have been generated from Snort and TWIDS, have been examined using 6 features. The Action attribute was selected as the class attribute. The “Allow” and “Drop” parameters have been specified for Action class. The firewall logs dataset is analyzed and the features are inserted to machine learning classifiers including Naive Bayes, kNN, One R and J48 using Spark in Weka tool. In addition, we compared the classification performance of these algorithms in terms of measurement metrics including Accuracy, F-measure and ROC values.

Machine Learning Algorithms, Classification, log analysis, firewall, Spark

[1] Rizzardi, A.Security in Internet of Things: networked smart objects. (Doctoral Thesis, Università degli Studi dell`Insubria, 2016).
[2] Roesch, M. (1999, November). Snort: Lightweight intrusion detection for networks. In Lisa (Vol. 99, No. 1, pp. 229-238).
[3] F. Ertam and M. Kaya, "Classification of firewall log files with multiclass support vector machine," 2018 6th International Symposium on Digital Forensic and Security (ISDFS), Antalya, 2018, pp.1-4. doi: 10.1109/ISDFS.2018.8355382.
[4] R. Hunt, “Internet/Intranet firewall security - Policy, architecture and transaction services,” Comput. Commun., vol. 21, no. 13, pp. 1107–1123, 1998.
[5] Golnabi, K., Min, R. K., Khan, L., & Al-Shaer, E. (2006). Analysis of firewall policy rules using data mining techniques. In 10th IEEE/IFIP Network Operations and Management Symposium NOMS 2006 (Vol. 5, pp. 305–315). IEEE. doi:10.1109/NOMS.2006.1687561.
[6] Breier, J., & Branišová, J. (2017). A dynamic rule creation based anomaly detection method for identifying security breaches in log records. Wireless Personal Communications, 94(3), 497-511.
[7] Ucar, E., Ozhan, E.: The analysis of firewall policy through machine learning and data mining. Wirel. Pers. Commun. 96, 2891 (2017). https://doi.org/10.1007/s11277-017-4330-0.

[8] Al-Shaer, E. S., & Hamed, H. H. (2003, March). Firewall policy advisor for anomaly discovery and rule editing. In International Symposium on Integrated Network Management (pp. 17-30). Springer, Boston, MA.
[9] Al-Shaer, E., Hamed, H., Boutaba, R., & Hasan, M. (2005). Conflict classification and analysis of distributed firewall policies. IEEE journal on selected areas in communications, 23(10), 2069-2084.
[10] Snort. An open source network intrusion detection system. http://www.Snort.org/.
[11] Link to download TWIDS tool: http://twids.cute.edu.tw/en.
[12] As-Suhbani, H., Khamitkar, S.D. (2017): Enhancing snort IDS performance using TWIDS for collecting network logs dataset. Int. J. Res. Adv. Eng. Technol. 42–45 (2017). https://doi.org/10.22271/engineering.
[13] Link to download Weka:
http://www.cs.waikato.ac.nz/ml/weka/
[14] Z. C. Lipton, C. Elkan, and B. Naryanaswamy, “Optimal thresholding of classifiers to maximize F1 measure,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2014, vol. 8725 LNAI, no. PART 2, pp. 225–239.

Hajar Esmaeil As-Suhbani, S.D. Khamitkar, "Classification of Firewall Logs Using Supervised Machine Learning Algorithms," International Journal of Computer Sciences and Engineering, Vol.7, Issue.8, pp.301-304, 2019.

The flexible, reusable nature of FPGAs makes them a great fit for different applications, from driver development to data processing acceleration. FPGAs can be programmed for different kinds of workloads, from signal processing to deep learning and big data analytics. In this article, we focus on the use of FPGAs for Artificial Intelligence (AI) workload acceleration. To make this thing happen we have design FPGA based Flip Flop Design for AI Based Processor. Here we have designed energy efficient RS Flip Flop. In consideration of technology upgradation, we have used 5G frequency for calculating total power consumption from 1GHZ to 5 GHZ. We have used two different IO standard HSTL_I_12 and HSTL_II_18 with different voltage (0.970, 1.009, 0.986 and 0.998 Volt). During the experiment we have found by applying HSTL_I_12 we have reduced our total power consumption by 44.87% which is significant among all the analysis.

FPGA, AI, HSTL, flip flop, 28nm.

[1] A Saxena,C Patel,M.Khan “Energy Efficient ALU Design Based On Voltage Scaling” in Gyancity Journal of Electronics and Computer Science,Vol.1, No.1, pp.29-33, September 2016ISSN: 2446–2918 DOI: 10.21058/gjecs.2016.11006.
[2] Michael Gschwind. Reprogrammable hardware for educational purposes. In Proc. of the 25th ACM SIGCSE Symposium, Phoenix, AZ, March 1994. ACM.
[3] A Saxena, A Bhatt, P Gautam, P Verma, C Patel,”High Performance FIFO Design for Processor through Voltage Scaling Technique” In Indian Journal of Science and Technology Vol 9(45), DOI: 10.17485/ijst/2016/v9i45/106916,
December 2016
[4] Xu LY. Realization of UART Communication Based on FPGA. Microcomputer Information. 2007; 23(35):218–9.
[5] A Saxena, A Bhatt, P Gautam, P Verma, C Patel “Designing Power Efficient Fibonacci Generator Using Different FPGA Families ” International Journal of Engineering and Technology (IJET) DOI: 10.21817/ijet/2018/v10i2/181002065
[6] W.K. Huang and F. Lombardi, “An Approach for Testing Programmable/Configurable Field Programmable Gate Arrays,14th
[7] IEEE VLSI Test Symposium, pp. 450-455, Princeton, NJ,USA,
May
[8] 1996. A Saxena, A Bhatt, C Patel “SSTL IO Based WLAN Channel Specific Energy Efficient RAM Design for Internet of Thing” 2018 3rd International Conference On Internet of Things: Smart Innovation and Usages (IoT-SIU) DOI: 10.1109/IoT-
SIU.2018.8519899
[9] M. Renovell, J. Figueras, Y. Zorian, “Test of RAM-Based FPGA:
Methodology and Application to the Interconnect”, 15th IEEE VLSITest
[10] A Saxena, S Gaidhani, A Pant, C Patel “Capacitance Scaling Based Low Power Comparator Design on 28nm FPGA” in International Journal of Computer Trends and Technology (IJCTT) – Volume X Issue Y- Month
[11] A Saxena, S Sharma,P Agarwal, C Patel “SSTL Based Energy Efficient FIFO Designfor High Performance Processor ofPortable Devices ” in International Journal of Engineering and Technology (IJET)Vol 9 No 2
[12] A Saxena, A Bhatt, P Gautam, P Verma, C Patel,”High Performance FIFO Design for Processor through Voltage Scaling Technique” In Indian Journal of Science and Technology Vol 9(45), DOI: 10.17485/ijst/2016/v9i45/106916, December 2016.

Manisha Bharti, Deepshikha Kumari, Puneet Chandra Verma, "28nm FPGA HSTL IO Standard Green RS Flip Flop Design for AI Based Processor," International Journal of Computer Sciences and Engineering, Vol.7, Issue.8, pp.305-308, 2019.