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.
Key-Words / Index Term
Cybercrime statistics, Methods of prevention, Hacking, Unauthorized access and Classification
. 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
. 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
. 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
. Subhash Desai “Study of Online Cyber Crimes in India”American Journal of Computer Science and Engineering SurveyISSN 2349 – 7238
. 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
. 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
. SoumyaSatishRevankar “cybercrime and cyber security “ International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 04 Issue: 11 | Nov -2017
. Jitender Kumar, “Cyber Crime in India: An Overview”. Imperial Journal of Interdisciplinary Research (IJIR)Vol-3, Issue-4, 2017ISSN: 2454-1362.
. 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
. 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.
 S. J. Pan, X. Ni, J.-T. Sun, Q. Yang, and Z. Chen, ‘‘Cross-domain sentiment classiﬁcation via spectral feature alignment,’’ in Proc. 19th Int. Conf. World.Wide Web, vol. 10.2010, p. 751.
 C. Lin, Y. Lee, C. Yu, and H. Chen, ‘‘Exploring ensemble of models in taxonomy-based cross-domain sentiment classiﬁcation,’’ in Proc. 23rd ACM Int. Conf. Conf. Inf. Knowl. Manage.-(CIKM), 2014, pp. 1279 –1288.
 J. Blitzer, R. McDonald, and F. Pereira, ‘‘Domain adaptation with structural correspondence learning,’’ in Proc. Conf. Empirical Methods Natural Lang. Process., 2006, pp. 120–128.
 J. Blitzer, M. Dredze, and F. Pereira, ‘‘Biographies, bollywood, boom-boxes and blenders: Domain adaptation for sentiment classiﬁcation,’’ in Proc. ACL, vol. 7. 2007, pp. 440–447.
 J. Yu and J. Jiang, ‘‘Learning sentence embeddings with auxiliary tasks for cross-domain sentiment classiﬁcation,’’ in Proc. Conf. Empirical Methods Natural Lang. Process., 2016, pp. 236–246.
 D. M. Blei and M. I. Jordan, ‘‘Modeling annotated data,’’ in Proc. 26th Annu. Int. ACM SIGIR Conf. Res. Develop. Inf. Retrieval, 2003, pp. 127–134.
 Pang, Bo, Lillian Lee, and ShivakumarVaithyanathan. "Thumbs up?: sentiment classification using machine learning techniques." Proceedings of the ACL-02 conference on Empirical methods in natural language processing-Volume 10.Association for Computational Linguistics, 2002.
 Turney, Peter D. "Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews." Proceedings of the 40th annual meeting on association for computational linguistics.Association for Computational Linguistics, 2002.
 Yan Dang; Yulei Zhang; Hsinchun Chen, "A Lexicon-Enhanced Method for Sentiment Classification: An Experiment on Online Product Reviews, Intelligent Systems, IEEE , vol.25, no.4, pp.46,53, July-Aug.2010 doi: 10.1109/MIS.2009.105
 Hung, Chihli, and Hao-Kai Lin. "Using objective words in SentiWordNet to improve word-of-mouth sentiment classification." IEEE Intelligent Systems 28.2 (2013): 0047-54.
 Bhaskar, J.; Sruthi, K.; Nedungadi, P., "Enhanced sentiment analysis of informal textual communication in social media by considering objective words and intensifiers," Recent Advances and Innovations in Engineering (ICRAIE), 2014 , vol., no., pp.1,6, 9-11 May 2014 doi: 10.1109/ICRAIE.2014.6909220.
 Muhammad faheem Khan, Aurangzeb and khairullah khan efficient word sense disambigutionteqnique for sentence level sentiment classification of online review‖ Sci.Int(Lahore).25(4),2013.
 Bollegala, D.; Weir, D.; Carroll, J., "Cross-Domain Sentiment Classification Using a Sentiment Sensitive Thesaurus," Knowledge and Data Engineering, IEEE Transactions on , vol.25, no.8, pp.1719,1731, Aug. 2013 doi: 10.1109/TKDE.2012.103
 A. Esuli, F. Sebastiani, SENTIWORDNET: a publicly available lexical resource for opinion mining, in: Proceedings of the 5th Conference on Language Resources and Evaluation LREC-06, Genoa, Italy, 2006, pp. 417–422.
 S. Baccianella, A. Esuli, F. Sebastiani, SENTIWORDNET 3.0: an enhanced lexical resource for sentiment analysis and opinion mining, in: Proceedings of LREC10, Malta, 2010, pp. 2200–2204.
 A. Moreo, M. Romero, J.L. Castro, J.M. Zurita, Lexicon-based commentsoriented news sentiment analyzer system, Expert Syst. Appl. 39 (2012) 9166– 9180.
 H. Cho et al., Data-driven integration of multiple sentiment dictionaries for lexicon-based sentiment classification of product reviews, Knowl.-Based Syst. 71 (2014) 61–71.
 M. Hu, B. Liu, Mining and summarizing customer reviews, in: Proceedings of Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, 2004, pp. 168–177.
 F.Å. Nielsen, A New ANEW: Evaluation of a Word List for Sentiment Analysis in Microblogs, 2011.
 M. Taboada, J. Brooke, M. Tofiloski, Lexicon-based methods for sentiment analysis, Comput.Linguist. 37 (2) (2011) 267–307.
 A. Weichselbraun, S. Gindl, A. Scharl, Extracting and grounding contextualized sentiment lexicons, IEEE Intell. Syst. 28 (2) (2013) 39–46.
 D. Bollegala, D. Weir, J. Carroll, Cross-domain sentiment classification using a sentiment sensitive thesaurus, IEEE Trans. Knowl. Data Eng. 25 (8) (2013).
 E. Riloff, J. Wiebe, Learning extraction patterns for subjective expressions, in: Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing (EMNLP 2003), 2003, pp. 105–112.
 P.J. Stone, E.B. Hunt, A computer approach to content analysis: studies using the general inquirer system, in: Proceedings of the Spring Joint Computer Conference (AFIPS 1963), 1963, pp. 241–256.
 E. Cambria, R. Speer, C. Havasi, A. Hussain, SenticNet: a publicly available semantic resource for opinion mining, in: AAAI Fall Symposium: Commonsense Knowledge, vol. 10, p. 02, 2010.
 J.C. de Albornoz, L. Plaza, P. Gervas, Sentisense: an easily scalable conceptbased affective lexicon for sentiment analysis, in: Proceedings of the 8th International Conference on Language Resources and Evaluation (LREC 2012), 2012, pp. 3562–3567.
 S. Cerini, V. Compagnoni, A. Demontis, M. Formentelli, C. Gandini, Micro- 862 WNOp: a gold standard for the evaluation of automatically compiled lexical resources for opinion mining, in: A. Sanso (Ed.), Language Resources and Linguistic Theory, Franco Angeli, 2007, pp. 200–210.
 S. Tan, X. Cheng, Y. Wang, H. Xu, Adapting naive bayes to domain adaptation for sentiment analysis, in: M. Boughanem et al. (Eds.), ECIR 2009, LNCS 5478, 2009, pp. 337–349.
 K. Bollacker, C. Evans, P. Paritosh, T. Sturge, J. Taylor, FreeBase: a collaboratively created graph database for structuring human knowledge, in: Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data, ACM, 2008, pp. 1247–1250.
 C. Bizer, J. Lehmann, G. Kobilarov, S. Auer, C. Becker, R. Cyganiak, S. Hellmann, DBpedia-A crystallization point for the web of data, Web Semant.: Sci., Serv. Agents World Wide Web 7 (3) (2009) 154–165.
 J. Blitzer, M. Dredze, F. Pereira, Biographies, bollywood, boom-boxes and blenders: domain adaptation for sentiment classification, in: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics, ACL’07, vol. 7, 2007, pp. 187–205 (13, 29).
 B. Pang, L. Lee, A sentiment education: sentiment analysis using subjectivity summarization based on minimum cuts, in: Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics, July 2004, p. 271.
 Z.Yan et al, EXPRS: an extended page rank method for product feature extraction from online consumer reviews, Inform Manage (2015), http://dx.doi.org/10.1016/j.imm.2015.02.002
 A.Hendari, Mhd. A.Tavakoli, N.Salim, Z.Hendari, Detection of Review Spam:aSurvvey, Expert Syst. Appl. 42 (7) (2015) 3634 – 3642.
 JyotiS.Deshmukh a, Amiya Kumar Tripathy, Entropy based Classifier for Cross – Domain Opinion Mining, Applied Computing and Informatics 144 (2017) 55-64.
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.
Key-Words / Index Term
Darknet, Freenet, I2P, Tor, whonix
 Dr. Digvijaysinh Rathod,” Darknet forensic”, International Journal of Emerging Trends & Technology in Computer Science (IJETTCS), Volume 6, Issue 4, July- August 2017
 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
 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
 Thorsten Ries, Andriy Panchenko, Radu State and Thomas Engel , “Comparison of Low-Latency Anonymous Communication Systems - Practical Usage and Performance”, AISC `11 Proceedings of the Ninth Australasian Information Security Conference - Volume 116, 2011
 Roger Dingledine, Nick Mathewson, Paul Syverson,” Tor: The Second-Generation Onion Router”, 13th conference on USENIX Security Symposium - Volume 13, 2004
 Clarke I., Sandberg O., Wiley B., Hong T.W. (2001) Freenet: A Distributed Anonymous Information Storage and Retrieval System. In: Federrath H. (eds) Designing Privacy Enhancing Technologies. Lecture Notes in Computer Science, vol 2009. Springer, Berlin, Heidelberg.
 Juan Pablo Timpanaro, Isabelle Chrisment, and Olivier Festor, “A Bird`s Eye View on the I2P Anonymous File-sharing Environment”,
Proceedings of the 6th International Conference on Network and System Security, pp.135-148, 2012.
 Whonix, www.whonix.org/wiki, 25-Aug- 2018
 Packet Sniffer-Fiddler, https://www.telerik.com/fiddle, 25-Aug- 2018
 Netminer Sniffer and Network Visualization tool, www.netminer.com, 25-Aug- 2018
 Wireshark and ethereal network protocol analyser toolkit,1st edition, elsevier,ISBN: 9781597490733
 Capsa Real time portable network anlalyser, Users Guide, 2018
 Netsnigg-ng as Network analyser, www.netsniff-ng.org, 20-Sep 2018
 M Roesch - Lisa , “ Snort: lightweight intrusion detection network”, Proceedings of LISA `99: 13th Systems Administration Conference, 1999
 Suricata IDS, www.suricata-ids.org, 25-Aug- 2018
 Bro IDS, www.bro.org/sphinx/intro, 25-Aug - 2018
 Security Onion IDS, www.securityonion.net, 25-Aug - 2018
 Openwips IPS, www.openwips-ng.org, 25-Aug - 2018
 Kismet IDS, www.kismetwireless.net/ 25-Aug - 2018
 NetDetecor IDS, www.niksun.com, 25-Aug - 2018
 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
 Network monitoring tool, www.manageengine.com/products/netflow, 25-Aug - 2018
 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.