With excessive exposure and use of internet, every system is prone to wide range of attacks. With this perspective, Network security is gathering every ones' concern. Network attackers are getting more and more innovative with designs. Exponential growth in attacker's intelligence require mitigation mechanism to be updated with same speed and level of innovation. This pans avenues for advanced technologies and methods to be employed for handling network security issues. Trending ones are artificial intelligence(AI), machine learning (ML) and deep learning(DL). AI is an umbrella covering ML and DL under it. Main features of AI making it big deal for network security are capacity to handle big data, faster detection, quick response, ability of unsupervised processing. Considering mentioned aspects, comprehensive study of AI approaches in handling network security issues is been carried out..

Keywords: network security,machine learning(ML), deep learning(DL), recommender systems, artificial intelligence(AI)

[1]. Daniel S. Berman, Anna L. Buczak *, Jeffrey S. Chavis and Cherita L. Corbett, "A Survey of Deep Learning Methods for Cyber Security",Information 2019, 10, 122; doi:10.3390/info10040122 www.mdpi.com/journal/information
[2]. https://www.analyticsvidhya.com/blog/2017/10/fundamentals-deep-learning-activation-functions-when-to-use-them/
[3]. Giovanni Apruzzese et al., "On the Effectiveness of Machine and Deep Learning for Cyber Security", 2018 10th International Conference on Cyber Conflict CyCon X: Maximising Effects T. Minárik, R. Jakschis, L. Lindström (Eds.) 2018 © NATO CCD COE Publications, Tallinn
[4]. https://machinelearningmastery.com/what-is-deep-learning/

Cloud document storage is becoming very popular, cost efficient and secure nowadays. Though cloud is very cost-efficient storage, the documents are accessible to service providers. Many literatures are available for document security in cloud. In most of the literatures, document encryption technique is described using different algorithms. Encrypting documents before storage is a good idea, but if encrypted documents as well as the secrete keys both are maintained on cloud server only, there is a possibility of leakage as the cloud service provider is an honest but curious entity. So, it can be a big risk if we maintain documents as well as their keys on cloud. On the other hand, if we apply unbreakable encryption on documents before storing on cloud then the searching over documents will be critical. Therefore, in this paper we focus on cloud document security as well as searching over cloud documents using AES algorithm, Caesar algorithm and blockchain technology.

Keywords: Blockchain, Cloud Computing, Advanced Encryption Algorithm, Ceasar Algorithm, public key,transaction management

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[5]. S. Kamara, C. Papamanthou, and T. Roeder, "Dynamic searchable symmetric encryption," in Proc. of ACM CCS, 2012, pp. 965–976.

mining high utility pattern is an important research area in data mining. Several business applications have been found to benefit from the discovery of high utility itemsets and association rules from transaction databases. A comprehensive survey and study of various methods in existence for high utility itemset mining, association rule mining with utility considerations have been presented in this paper. In these, the various mining techniques are used such as Incremental Mining of High Utility Patterns, High Average-Utility Patterns with Multiple Minimum Average-Utility Thresholds, Using Bio-Inspired Algorithms, Algorithm for Incremental and Interactive High Utility Itemset Mining, using Temporal-Based Fuzzy Utility Mining. However there are some issues that need to resolve these are discussed in this paper. Various algorithms are studied for the comparative analysis of high utility pattern mining algorithms and new method is proposed.

Keywords: High utility pattern mining, frequent itemset mining, fuzzy utility mining, utility threshold.

[1]. JUNQIANG LIU, (Member, IEEE), XINYI JU, XINGXING ZHANG, BENJAMIN C. M. FUNG, (Senior Member, IEEE), XIANGCAI YANG, AND CHANGHONG YU, "Incremental Mining of High Utility Patterns in One Phase by Absence and Legacy-Based Pruning", IEEE,
Vol.:7, 74168-74180, June 2019
[2]. JERRY CHUN-WEI LIN, (Member, IEEE), SHIFENG REN, AND PHILIPPE FOURNIERVIGER, "MEMU: More Efficient Algorithm to Mine High Average-Utility Patterns with Multiple Minimum Average-Utility Thresholds", IEEE, Vol.:6, 7593-7609,March, 2018
[3]. WEI SONG AND CHAOMIN HUANG, "Mining High Utility Itemsets Using Bio-Inspired Algorithms: A Diverse Optimal Value Framework", IEEE, Vol.:6, 19568-19582,April 2018
[4]. Shiming Guo, Hong Gao, "An Efficient Algorithm for Incremental and Interactive High Utility Itemset Mining", 2017 2nd International Conference on Image, Vision and Computing,996- 1001,2017

Genetic Algorithms (GAs) are a type of optimization algorithms which combine survival of the fittest and a simplified version of Genetic Process. Machine Learning provides ability to automatically learn and improve from experience without being explicitly programmed. It uses the data set to train the machine. Genetic Algorithms are used in various fields where Machine Learning algorithm also used. This study shows some examples where this both Genetic Algorithm and Machine learning Algorithms are used to get the best accuracy for model. Literature survey briefly provides the applications of Genetic Algorithm and Machine learning Algorithms in various fields. This study is fruitful for new researchers to get the basic background about the Genetic Algorithm and Machine learning with its recent application that are currently used in various fields like robotics, wireless network, agriculture, medical, banking, gaming, finding shortest path on map, environment resources etc..

Keywords: Genetic Algorithm, Machine learning, Support Vector Machine, Extreme Learning Machine

[1]. Andysah Putera, Utama Siahaan and Robbi Rahim, "Dynamic Key Matrix of Hill Cipher Using Genetic Algorithm", International Journal of Security and Its Applications Vol. 10, No. 8, pp.173-180, http://dx.doi.org/10.14257/ijsia.2016.10.8.15, 2016.
[2]. Abdulhamit Subasi, Jasmin Kevric & M. Abdullah Canbaz, "Epileptic seizure detection using hybrid machine learning methods", Neural Comput & Applic, DOI 10.1007/s00521-017-3003-y, 2017.
[3]. Seyed Amir Naghibi, Kourosh Ahmadi & Alireza Daneshi, "Application of Support Vector Machine, Random Forest, and Genetic Algorithm Optimized Random Forest Models in Groundwater Potential Mapping", Water Resour Manage 31:2761–2775, DOI 10.1007/s11269-017-1660-3, 2017.
[4]. Nongnuch Artrith, Alexander Urban, and Gerbrand Ceder, "Constructing first-principles phase diagrams of amorphous LixSi using machinelearning-assisted sampling with an evolutionary algorithm", The Journal of Chemical Physics 148, 241711; doi: 10.1063/1.5017661, 2018.
[5]. Derya Avci and Akif Dogantekin, "An Expert Diagnosis System for Parkinson Disease Based on Genetic Algorithm-Wavelet Kernel-Extreme Learning Machine", Hindawi Publishing Corporation, Parkinson's Disease, Volume 2016, Article ID 5264743, 9 pages, doi: 10.1155/2016/5264743, 2016.

One of the most dynamic and exciting developments in information and communication technology is the advent of the Internet of Things (IoT). Automation can make work more smart and easy. This paper is focused on five different techniques such as home automation system using software defined networking (SDN) based firewall, by using Near Field Communication (NFC), client – server based home automation system, home automation using Zigbee wireless sensor communication technique and home automation technique with real time E-metering using E-controller etc. Even though, some issues arising in every method which are critically analyzed in this paper to improve these issues. The paper has proposed a new home automation technique with the use of IOT, so as to reduce the existing problems and make home automation more smart and secure.

Keywords: Internet of Things (IOT), Home automation system

[1] Sajad Shirali-Shahreza, Member, IEEE, and Yashar Ganjali, Senior Member, IEEE,―Protecting Home User Deviceswith an SDN-Based Firewall‖,VOL. 64, NO. 1, February 2018
[2] Vagdevi P., Divya Nagaraj, Golla Vara Prasad, ―Home: IOT Based Home Automation Using NFC‖ February 2017
[3] Brundha S.M.,Lakshmi P. and Santhanalakshmi S., ―Home Automation in Client-Server Approach withUser Notification along with Efficient Security Alerting system‖ August 2017.

"IoT" is currently the most popular aspect of the research field. They consist of an extremely large amount of data to be researched. The data it holds is secured with privacy protocols. "The Vehicle Monitoring System" for vehicles helps in maintaining vehicle and also protect from accidents due to engine overheating as well as tire bursting. It uses wireless communication mode for communicating with the cell phone of the device user. This paper presents the review of the tire pressure monitoring, engine overheat monitoring, accident detection system and CO2 exhaust detection system. This system is helpful for maintaining vehicles and to protect vehicles from accidents and to detect the accident if it happens. The communication of the system with the authorized cell phone will be done with the help of the GSM module which is present inside the device.

Keywords: monitoring, microcontroller, detection, bursting.

[1]. Y. Shiming, T. Jianbin, Hong Q., C. Chengrong, "Wireless Communication Based Tire Pressure Monitoring System," 1-4244-1312-5/07.
[2]. I. H. Ho, J. M. Chung, H. C. Chen, H. W. Chiu, "A Battery-less Tire Pressure Monitoring System," 978-1-4244-2517-4/09.
[3]. H. Wang, S. Yu , Y. Haung, Y. Pu, "Wireless Communication of Tire Pressure Intelligent Monitoring System Based on SensorNetworks Technology,", International Conference on Intelligent Control and Automation 2006.
[4]. Conference on Intelligent System Design and Engineering Application, 2010.
[5]. F. Leens, "An introduction to I2C and SPI Protocol," IEEE Instrumentation and Measurement Magazine, 2009.

In the cloud computing, security mechanisms are not mature enough to protect the data stored in the cloud. Hence, it is necessary to propose efficient methods for providing security to the data stored in the cloud. An intrusion detection system is dynamic software for cloud networks and databases that provides security for different network infrastructures and it can be extended easily. It is used to spot the mean users who violate their system privileges and become intruders. An intrusion detection system developed for securing the computer networks is monitored by the network traffic. In this paper, an Intrusion Detection System which is capable of identifying various kinds of attacks on cloud databases in public cloud is planned and executed. The intrusion detection is performed by proposing a new algorithm which has been designed and implemented by integrating Naïve Bayes classifier with Support vector Machines (SVM) using intelligent agents. The proposed system includes a new prevention module where prevention of intruders and malicious users are carried out using an intelligent decision manager that performs inference by sacking some rules.

Keywords: Security mechanism, Intrusion Detection System, cloud database, Support vector Machines.

[1]. Yasir Mehmood, Muhammad Awais Shibli, Umme Habiba, Rahat Masood, "Intrusion Detection System in Cloud Computing: Challenges and opportunities", in 2nd National Conference on Information Assurance (NCIA), December 2013.
[2]. Uttam Kumar and Bhavesh N. Gohil, "A Survey on Intrusion Detection Systems for Cloud Computing Environment", in International Journal of Computer Applications, vol. 109 – No. 1, pp. 6-15, January 2015.
[3]. D. P. Kale and V. M. Thakare, "Different Security Features and Solutions In the Cloud Data Services", ICICV2020, unpublished.
[4]. A. Patel, M. Taghavi, K. Bakhtiyari, J. C. Junior, "An Intrusion Detection and Prevention System in Cloud Computing: A Systematic Overview", Journal of Network and Computer Applications, pp. 25–41, 2013.
[5]. Sindhu, SSS, Geetha, S & Kannan, "Decision tree based light weight intrusion detection using a wrapper approach", Expert Systems with Applications, vol. 39, no.1, pp. 129–141, 2012.

AI is a branch of Computer Science concerned with the study and creation of computer systems.AI is a study of how to make computers do things which at a moment, people do better. Today businesses using modern technologies like cloud, big data, mobile, and social media. Although these technologies unlock a whole new set of capabilities and rewards for businesses, they also expose them to to hitherto unknown risks. when hackers would deploy adware, malware, Trojan viruses, phishing attacks or standard keyloggers on private systems for small gains, the focus of hackers and cybercriminals has shifted from individual users to big businesses and corporations since they make for more lucrative targets. But financial rewards are not the only motive behind cyber-attacks. Gaining access to sensitive data and using it for illegal purposes, cause enterprises far more damage, not only in terms of financial losses but also hurting the reputation they...........

Keywords: Artificial Intelligence, Cybercrime, Cyber-attacks, Cyber Security, Artificial Neural Networks and Machine Learning Algorithms.

[1]. AI and security -https://www.youtube.com/watch?v=ojTz7bOjT6o&t=87s
[2]. How ML and AI will increase cyber security (a webinar)-https://www.youtube.com/watch?v=lO2Cf5BmjvI
[3]. Harini M Rajan, Dharani S ―Artificial Intelligence in Cyber Security-An Investigation‖ Int. Res. J. Comput. Sci. Issue, vol. 09, no. 4, pp. 28–30, 2017.
[4]. Swapnil Ramesh Kumbar ―An Overview on Use of Artificial Intelligence Techniques in Effective Security Management,‖ Int. J. Innov. Res. Comput. Commun. Eng. (An ISO, vol. 2, no.9, pp. 5893–5898, 2014.
[5]. Mohana,K.V.K. Venugopal, Sathwik H.N. ―Data Security using Genetic Algorithm and Artificial Neural Network,‖ Int. J. Sci. Eng. Res., vol. 5, no. 2, pp. 543–548, 2014.

Horticulture assumes a basic job in the worldwide economy. Weight on the agrarian framework will increment with the proceeding with the extension of the human populace. Agri-innovation and accuracy cultivating, presently additionally named advanced horticulture, have emerged as new logical fields that utilization information extraordinary ways to deal with drive agrarian profitability while limiting its ecological effect. The information produced in present-day horticultural tasks is given by a wide range of sensors that empower a superior comprehension of the operational condition (an association of dynamic harvest, soil, and climate conditions) and the activity itself (apparatus information), prompting increasingly precise and quicker basic leadership. This paper comprises about the use of Machine Learning (ML) and Internet of Things (IoT) for the enhancement in agriculture industry.

Keywords: Internet of Things, Machine Learning, Artificial Intelligence

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Urbanization creates a lot of social problems. One of these problems inherent in all cities of the world is crime. Police databases accumulate a large amount of data that could be analyzed in order to reduce crime rates. The analysis of criminal activity and prediction of number of crimes remains one of the most interesting problem for researchers.Crime is one of the most dominant andalarming aspects in the society and its prevention is an importanttask. Crime analysis is a systematic way of detecting andinvestigatingthe patterns and trends that happen in acrime.This paper is focused on analysis of five papers. In this paper, we have studied different crimeprediction techniques and presented the techniques thoroughly.

Keywords: Crime Prediction, Linear Regression, Clustering, Machine Learning, etc.

[1]. VarvaraIngilevich,SergeyIvanov,"Crime rate prediction in the urban environment using social factors",Elsevier, 2018.
[2]. Hitesh Kumar Reddy ToppiReddy, BhavnaSaini,GinikaMahajan,"Crime Prediction and Monitoring Framework based on spatial analysis",Elsevier ,2018.
[3]. Masila Abdul Jalil,FatihahMohd, Noor MaizuraMohamad Noor,"A Comparative Study to evaluate Filtering Methods for Crime Data Feature Selection",Elsevier ,2017.
[4]. S.Sivaranjani, Dr. S. Sivakumari, Aasha M., "Crime Detection and Forecasting in Tamil Nadu using Clustering Approaches ",IEEE, 2016.
[5]. Chung-Hsien Yu, Max W. Ward, Melissa Morabito, and Wei Ding, "Crime Forecasting using Data Mining Techniques",IEEE,2011.