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Today new technologies are entering into the world that is providing many informational resources. Technologies are needed in fast moving of information. Maximum security is created, but some lacks in security. It could be identified by cyber criminals and they are moving faster in global era and moreover they could be entering into human privacy. Information is wealth that could bring more facilities to business, entertainment, education and mobility. In India many cyber criminals are rapidly growing and breaking the security to earn money. This could be cultivated day to day by police, but many lacking in software’s and investigation. Every day cyber criminals are born and it could be endless one. This paper deals with many security depends on analyzing the various types are crime and deals with methodology like classification techniques to prevent the information from cyber-attacks.

Cybercrime statistics, Methods of prevention, Hacking, Unauthorized access and Classification

[1]. S. Shriram, RESEARCH ARTICLE CYBER SECURITY AND RELATED CRIMES IN INDIAN SCENARIO International Journal of Current ResearchVol. 6, Issue, 03, pp.5403-5412, March, 2014
[2]. Shubham Kumar, Guide Faculty - Dr.SantanuKoley, Associate Professor, *Uday Kumar * 2017 “Present scenario of cybercrime in INDIA and its preventions”International Journal of Scientific & Engineering Research, Volume 6, Issue 4, April-2015 1971 ISSN 2229-5518
[3]. AnimeshSarmah, RoshmiSarmah , AmlanJyotiBaruah, “A brief study on Cyber Crime and Cyber Law’s of India” International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395 -0056 Volume: 04 Issue: 06 | June -2017
[4]. Subhash Desai “Study of Online Cyber Crimes in India”American Journal of Computer Science and Engineering SurveyISSN 2349 – 7238
[5]. P. K. Paul* & P. S. Aithal** “cybercrime: challenges, issues, recommendation and suggestion in indian context“
International Journal of Advanced Trends in Engineering and Technology (IJATET), ISSN (Online): 2456 - 4664 (www.dvpublication.com) Volume 3, Issue 1, 2018
[6]. 1Alpna, 2Dr. SonaMalhotra “Cyber Crime-Its Types, Analysis and Prevention Techniques” Volume 6, Issue 5, May 2016 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering
[7]. SoumyaSatishRevankar “cybercrime and cyber security “ International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 04 Issue: 11 | Nov -2017
[8]. Jitender Kumar, “Cyber Crime in India: An Overview”. Imperial Journal of Interdisciplinary Research (IJIR)Vol-3, Issue-4, 2017ISSN: 2454-1362.
[9]. M. Elavarasi1* and N. M. Elango22017 “Analysis of Cybercrime Investigation Mechanism in India” Indian Journal of Science and Technology, Vol 10(40), DOI: 10.17485/ijst/2017/v10i40/119416, October 2017
[10]. PoojaAggarwal, PiyushArora, Neha, Poonam “Review on cyber crime and security” , 2014International Journal of Research in Engineering and Applied Sciences (IJREAS)

M. Suriakala, P. Narayanasamy, "TECHNOLOGICAL CYBERCRIME IN INDIA AND ITS HINDRANCE," International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.789-791, 2018.

The social network growth is increased and the interest of people in analyzing reviews and opinions for products before buy them. In this regarding research communities, academia, public and service industries are working rigorously on sentiment analysis, also known as, opinion mining, to extract and analyze public mood and views. Analyzing the sentiments in massive user-generated online data, such as product reviews and micro blogs, has become a hot research topic. Sentiment analysis is widely known as a domain dependent problem. In this paper presents a rigorous survey on cross domain sentiment analysis, challenges for social media, then identified problems in different domains usually have different sentiment expressions and a general sentiment classifier is not suitable for all domains. The main problem is the selection of sentiment from huge volume of opinionated data for different kinds of event which is available in the social networks, but there exist a huge difficulty in predicting the accurate outcome of the event at cross domain. A natural solution to this problem is to train a domain-specific sentiment classifier for each target domain. However, the labeled data in target domain is usually insufficient, and it is costly and time-consuming to annotate enough samples.

Data Mining, Opinion Mining, Machine Learning, Cross - Domain Sentiment Analysis, SentiWordNet

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V. Manimekalai, S. Gomathi @ Rohini, "A Survey on Cross - Domain Opinion Mining," International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.792-796, 2018.

DarkNet is the portion of Internet that is intentionally kept hidden and is only accessible by special soft wares and non-standard communication protocols and ports. Accessing these portion is not illegal at all times, but these software make it possible to keep the user anonymous and preserve data privacy. Anonymous communication has gained popularity and is of much interest. Anonymity leads to compromising nonrepudiation and security goals. Apart from providing freedom of speech to user, anonymity also provides conducive environment to illegal activities and different kinds of cyber-attacks. Network surveillance and forensic investigation is required to reconstruct or collect evidence but becomes a challenge due to anonymity, encryption and newer ways of cyber-attack. Innovative methods and techniques are required for overcoming these challenges of DarkNet. Sniffing the network for information, traffic analysis, anomaly and intrusion detection are few techniques to find evidences. With a plethora of tools and techniques available for collecting, identifying, processing and analyzing data on the networks, we try to explore few tools for forensic investigation in the DarkNet.

Darknet, Freenet, I2P, Tor, whonix

[1] Dr. Digvijaysinh Rathod,” Darknet forensic”, International Journal of Emerging Trends & Technology in Computer Science (IJETTCS), Volume 6, Issue 4, July- August 2017
[2] Rhyme Upadhyaya, Aruna Jain, “Cyber Ethics and Cyber Crime: a deep dwelved study into legality, ransomware, underground web and bitcoin wallet ”, Published in International Conference on Computing, Communication and Automation (ICCCA), pp.143-148 ,2016
[3] Afzaal Ali, Maria Khan, Muhammad Saddique , Umar Pirzada, Muhammad Zohaib, Imran Ahmad, Narayan Debnath “ TOR vs I2P: A Comparative Study”, Published in: 2016 IEEE International Conference on Industrial Technology (ICIT), pp.1748-1751, 2016
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Proceedings of the 6th International Conference on Network and System Security, pp.135-148, 2012.
[8] Whonix, www.whonix.org/wiki, 25-Aug- 2018
[9] Packet Sniffer-Fiddler, https://www.telerik.com/fiddle, 25-Aug- 2018
[10] Netminer Sniffer and Network Visualization tool, www.netminer.com, 25-Aug- 2018
[11] Wireshark and ethereal network protocol analyser toolkit,1st edition, elsevier,ISBN: 9781597490733
[12] Capsa Real time portable network anlalyser, Users Guide, 2018
[13] Netsnigg-ng as Network analyser, www.netsniff-ng.org, 20-Sep 2018
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[15] Suricata IDS, www.suricata-ids.org, 25-Aug- 2018
[16] Bro IDS, www.bro.org/sphinx/intro, 25-Aug - 2018
[17] Security Onion IDS, www.securityonion.net, 25-Aug - 2018
[18] Openwips IPS, www.openwips-ng.org, 25-Aug - 2018
[19] Kismet IDS, www.kismetwireless.net/ 25-Aug - 2018
[20] NetDetecor IDS, www.niksun.com, 25-Aug - 2018
[21] Seong Soo Kim and A. L. Narasimha Reddy, “NetViewer: A Network Traffic Visualization and Analysis Tool” Texas A&M University, 19th Large Installation System Administration Conference (LISA ’05), 2005
[22] Network monitoring tool, www.manageengine.com/products/netflow, 25-Aug - 2018
[23] Elasticsearch, www.elastic.co/products/kibana 25-Aug - 2018

Preeti S. Joshi, Dinesha H.A., "Study Report of existing forensic tools and technologies to identify Darknet," International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.797-800, 2018.

Many factors affecting the success of data mining techniques, the pureness of data are one of the factors. The inclusion of irrelevant and noisy data in the pattern analyzing phase, can results poor predicting performance. To discover information from the endometrial carcinoma data set, the pre-processing technique such as cleaning, transforming and modelling are applied. Diverse kinds of pre-processing techniques were functions in the data set in order to work with the full pledged data set. Methodology Used: The data mining tool WEKA is used for feature selection. Using various classifiers, evaluators and search methods six features are selected out of eighteen, attributes. The performance evaluation was done using RStudio. The accuracy of the classifiers model Random Forest and Naïve Bayes are checked for the minimized and full data sets. The hybrid model was formed by combing both the models to improve the performance of the classifier model. Findings: The Hybrid model was adopted for the performance evaluation by combining naïve Bayes and random forest classifier and the accuracy of the new model is 93.55%.

WEKA; Endometrial; R; carcinoma; classifiers; Naïve Bayes; Random Forest

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A. Hency Juliet, R. Padmajavalli, "Performance Evaluation Using Classifier Algorithm On Endometrial Cancer Data," International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.801-807, 2018.

In this research paper lupus disease is suspected using Artificial Neural Networks (ANN). Lupus is a chronic disease which is not curable. But it can be controlled by early diagnosis. By analyzing various symptoms it is very difficult to diagnose lupus manually. This paper is an approach to diagnose lupus in an efficient way with the help of ANN. An ANN has been designed here to suspect lupus. The ANN consists of many neurons associated with weights. Each neuron represents each symptom. Here patients are classified into two categories: infected and non-infected. Classification is an important tool in medical diagnosis. The data was collected from North Bengal Medical College for training the net.

Disease Suspection, Artificial Neural Networks (ANN), SLR

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Payel Saha, Rakesh Kumar Mandal, "Suspecting Lupus by Analyzing Rashes using Artificial Neural Networks (ANN)," International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.808-812, 2018.

Cloud computing is a model for enabling ubiquitous convenient, where the resources of a data center are shared using virtualization technology. Cloud computing is one of the dynamic provisioning technology. Cloud computing technology can be implemented in a wide variety of architectures under different services and deployment models. Privacy to the data stored in cloud server is a major challenging operation in cloud computing. The objective of this paper is to explore different methods for efficiently growing latest digital techniques. The main aim of this paper is to design and propose strong security system to the data stored in the cloud system. This paper presents a literature review we studied the background work of carried-out by the various approaches about the uses of cloud technology. We have proposed framework and different methods which provides, identification of high protection. The literature review presents previous studies related to the objectives of the present study. Further various features that make the proposed framework more suitable for analysis and evaluation of cloud computing. The privacy cloud algorithms are categorized into subgroups according to the high level security concerns in the cloud computing.

Cloud Computing, Privacy and Protection Technique, Digital Authentication for Data Security, Algorithms for Privacy

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Rakesh Prasad Sarang , "Cloud Computing Technologies for Privacy Data and Digital Security Authentication: A Literature Survey," International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.813-826, 2018.

The purpose of this paper is to compare the forecasting performances of daily generation of a solar plant by utilizing the autoregressive time series models. As the demand for energy is increasing frequently all over the world, the proper integration of solar energy and its accurate predictions become necessary for our society for better planning and distribution of energy. In this study, we compare our solar energy time series data with Autoregressive Integrated Moving Average with Explanatory Variable (ARIMAX) and Vector Autoregressive (VAR) time series models for analyzing our solar plant data separately and at last conclusion is made on the better performance of these two methods. Moreover for VAR model effects of various variables are tested for maximum production of solar power. For evaluating the accuracy performance of our forecasted data, we use Mean Absolute Error (MAE), Mean Absolute Scaled Error (MASE) and Root Mean Square Error (RMSE) measurements.

ARIMAX, MAE, MASE, RMSE, Solar Plant Generation, VAR

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Sukhpal Kaur, Madhuchanda Rakshit, "Performance Comparison of Forecasting on Solar Plant Generation Data," International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.827-834, 2018.

Automatic extraction of buildings and change detection of buildings from satellite images is an important tool for city management and planning. The discovery of changes is the process of identifying differences in the state of the objects extracted from the remote image by observing different time periods. The main objective of this paper is to extract the manmade objects (buildings) from the given input satellite images and detect the changes in the extracted building map. This work presents the Region of Interest (ROI) and extraction of the building using K-Nearest Neighbor (KNN), Support Vector Machine (SVM) and Extreme Learning Machine (ELM) techniques. Initially, the input satellite image is de-noised by using the Wavelet Shrinkage de-noising approach. Then the K-Means, Fuzzy C-Means (FCM) and Artificial Bee Colony (ABC) approaches are applied to the de-noised image to segment the vegetation and non-vegetation areas and then extract the features using Local Binary Pattern (LBP) Technique. Finally, the extracted features are given to the KNN, SVM and ELM classifier to get the building map and then the change detection process is applied. In this paper, the comparison is made on three clustering approaches and three classifier approaches to find the best approach for manmade object extraction. From the experimental result, it is shown that the ABC approach performs better than K-Means and FCM in clustering and ELM provides the best result than the KNN and SVM in classifiers.

Building Extraction, Vegetation, Non-Vegetation, Wavelet Shrinkage, FCM, K-Means, ABC, LBP, KNN, SVM, ELM

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Sure Venkata Padmavathi Devi, D. Murugan, A. Ramya, T. Ganesh Kumar, "Comparative Study on Detection and Classification Approaches on Man-Made Objects from Satellite Images," International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.835-844, 2018.

The advanced development of education needs the distance learning for improving the student knowledge based on the relational content providence. E-learning improvements are based on M-learning techniques through the knowledge learning process without providing the right content of subjectivity resource to the student be to create the problems. The M-learning process contains digital information with subjectivity reference of content based on the student interest. The content analysis techniques doesn’t create relational subjectivity interest measure on multimedia content services. To intake the challenge approach, we propose an Efficient Relational interest feature selection for improving the quality of M-distance education using content-based information similarity measure(RIF: MDEISM). This initially analyses the interest in multimedia content information to extract the relation feature on the subjectivity. Further, the extracted features are observed by relative semantic analysis using information similarity measure to get the optimized result from web learning resources. The resultant proves the higher efficient relational content analysis to improve the m-learning distance education.

knowledge learning, content mining, interest analysis, feature analysis, similarity measure.

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S. Senthil, M. Prabakaran, "Efficient Relational Interest Feature Selection for Improving the Quality of M-distance Education Using Content-Based Information Similarity Measure," International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.845-855, 2018.

Digital forensics tools are often used to calculate the hash value of the digital test unit. The MD5 and SHA hash function is used in digital forensics tools to calculate and verify that a dataset has not been altered, due to the application of multiple collection and analysis tools and pro-cedures of evidence. In addition, because of the impact on the personal life of the subject of the survey, the verification of the proper functioning of the tools and procedures is crucial. This article discusses the importance of hashing value in digital forensics for digital evidence. The search uses six different possible cases as an experiment to generate and verify the hash value of the test drive by using a forensic tool to demonstrate the importance of the hash value in digital forensics. In addition, unreliable results can be obtained due to incorrect use of the Tools application.

SHA; MD5; hash function ; digital forensic

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Arvind S. Kapse, Vilas M. Thakare, Avinash S. Kapse, "Result Analysis of Hash Value Generation Using Security Algorithm for Device Forensics," International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.856-862, 2018.