Discuss about the Information Security of Deep Web .
The deep web or the hidden web is the specific parts of World Wide Web or WWW, where all the contents are not indexed by the standard of web search engines for any particular reason (Zhao et al., 2016, p. 2). There are various interesting pages of indexes, internal networks, databases or ideas on the deep web. These pages could either be nefarious or innocent for the management of deep web. The opposite of the deep web is known as the surface web that could be accessed by everyone with the help of the Internet connection. The contents of this deep web is completely hidden in the forms of HTTP and thus is involving the various utilizations like web mail, services, online banking and many more, which is required by any user to be paid off and is protected and secured by any typical pay wall (Sharma & Sharma, 2017, p.2). This pay wall could be various online magazines, newspapers and videos on demand. All the contents of this deep web could be easily located as well as accessed by the direct URL or the IP address and thus would need various security accesses and passwords from the previous website page.
The following report outlines a brief discussion on the concept of the technology of deep web and how this deep web is being utilized by all users. This report will be providing the background of deep web with various security challenges or issues that are faced by the users of deep web. Moreover, the potential solutions will also be provided here.
The deep web is the part of the Internet that is not being indexed by any of the search engines. It is the most diversified world that could be nefarious in nature. The term deep web was at first coined by the popular computer scientist, Michael K. Bergman (Huang e al., 2013, p.1). He said that this part of the Internet is nothing but a simple search indexing term. The conflation of the two terms of deep web as well as the dark web first came into account in the year of 2009. The terminology of the deep web search engine was eventually discussed with all types of illegal and unethical actions that were taking place on the specific Freenet darknet (Thamviset & Wongthanavasu, 2014, p. 1109). After the year of 2009, the utilization within the media reporting of Silk Road, several people and outlets of media are utilizing this deep web with the dark net or dark web. There is a distinct difference between the deep web and dark web. Deep web is the typical type of search engine that helps to search anything online; however, dark web is the exact opposite of deep web. It is the nefarious activities of deep web, thus overshadowing the simple nature of deep web (Oita, Amarilli & Senellart, 2017, p. 1). Deep web is the most significant and important reference to any particular site that is not accessible with the help of any traditional search engine, the dark web, on the other hand, is the smaller part of the deep web, which is intentionally kept hidden and is also inaccessible by the standard methods or browsers. The part of the web, which is being indexed by the standard search engines, is called the surface web. During the year of 2001, the deep web was larger than the surface web in terms of the orders of magnitude (Mallede, Marir & Vassilev, 2013, p. 32). It is nearly impossible in measuring as well as putting estimates on the deep web size as most of the information is either hidden or is locked within the databases. The information or the sites added within the deep web are making the growth of it extremely high and thus could not be quantified.
Although, deep web is extremely important as well as beneficial for all the users, there are some of the most significant security challenges posed by it (Furche et al., 2013, p.24). These security challenges make the entire concept of deep web often threatening for the society and the users. The most significant and important security challenges of the deep web are as follows:
The above mentioned security issues are extremely dangerous for the society and to all those people, who are using this technology. However, there are some of the potential solutions to these problems. They are as follows:
Conclusions and Future Trends
Therefore, from the above discussion, it can be concluded that deep web or invisible web is one of the largest parts of the connection of Internet, which is solely inaccessible to the conventional search engines. The contents of the deep web majorly includes various chat messages, emails, private contents over the social media, electronic records of health, electronic bank statements and many others. The contents of deep web also include which are accessed on the Interne; however, are not indexed or crawled by the search engines such as Yahoo, Bing, Google or DuckDuckGo. The most significant reason for this not indexing the contents of deep web varies eventually. The content could either be proprietary, where the content is accessed through the VPN or virtual private network. When the content of the deep web is absolutely commercial in nature, they reside within the member wall and could only be accessed by the clients, who have made the payment. This content could even be PII or personal identifiable information, and then this is to be protected with the help of compliance regulations. The components of the deep web often lack the permanent URL or uniform resource location for becoming the part of the technology. The above report has provided the detailed description about the part of WWW, i.e. deep web by providing relevant details. Moreover, the several security and privacy issues that are common in deep web are also given here. Solutions are also provided in the report for each and every problem identified and the final part of the report has discussed about the future trends of this deep web.
The future of the Deep Web is quite advanced and increased with popularity. There are five predictions that the Deep Web will be more advanced in the near future. These predictions of future trends of Deep Web are given below:
These above mentioned five predictions about the future of deep web will be bringing out the future trends of this Deep Web effective immediately.
References
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Furche, T., Gottlob, G., Grasso, G., Guo, X., Orsi, G., & Schallhart, C. (2013). The ontological key: automatically understanding and integrating forms to access the deep Web. The VLDB Journal, 22(5), 615-640.
Huang, P. S., He, X., Gao, J., Deng, L., Acero, A., & Heck, L. (2013, October). Learning deep structured semantic models for web search using clickthrough data. In Proceedings of the 22nd ACM international conference on Conference on information & knowledge management (pp. 2333-2338). ACM.
Khelghati, M., Hiemstra, D., & Van Keulen, M. (2013, May). Deep web entity monitoring. In Proceedings of the 22Nd International Conference on World Wide Web (pp. 377-382). ACM.
Khurana, K., & Chandak, M. B. (2016). Survey of Techniques for Deep Web Source Selection and Surfacing the Hidden Web Content. INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS, 7(5), 409-418.
Mallede, W. Y., Marir, F., & Vassilev, V. T. (2013). Algorithms for mapping RDB schema to RDF for facilitating access to deep web. In Proceedings of the First International Conference on Building and Exploring Web Based Environments (pp. 32-41).
Noor, U., Daud, A., & Manzoor, A. (2013, September). Latent dirichlet allocation based semantic clustering of heterogeneous deep web sources. In Intelligent Networking and Collaborative Systems (INCoS), 2013 5th International Conference on (pp. 132-138). IEEE.
Oita, M., Amarilli, A., & Senellart, P. (2017). Cross-fertilizing deep Web analysis and ontology enrichment.
Sharma, D. K., & Sharma, A. K. (2017). Deep Web Information retrieval Process. The Dark Web: Breakthroughs in Research and Practice: Breakthroughs in Research and Practice, 114.
Thamviset, W., & Wongthanavasu, S. (2014). Information extraction for deep web using repetitive subject pattern. World Wide Web, 17(5), 1109-1139.
Wu, W., & Zhong, T. (2013, May). Searching the deep web using proactive phrase queries. In Proceedings of the 22nd International Conference on World Wide Web (pp. 137-138). ACM.
Zhao, F., Zhou, J., Nie, C., Huang, H., & Jin, H. (2016). SmartCrawler: a two-stage crawler for efficiently harvesting deep-web interfaces. IEEE transactions on services computing, 9(4), 608-620.
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