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Analysis of web content and sentiment nowadays are hot topics among researchers. The dark web is that integral part of WWW, which provides freedom of content hosting. The dark web is majorly used to do illegal activities; although accessing the dark web is legal in most countries, its usage can arouse suspicion with the law. The dark web's hosted websites provide various services in multiple Categories like Adult, Counterfeits, illegal markets, Drugs, weapons are prevalent. Sentiment's analysis attempts to identify emotions and opinions based on the transactions and text communications. Pattern Detection is a process of recognizing patterns in a dataset using a machine-learning algorithm. This study provides inputs to a framework for automating Dark Web Scraping and analysis of its hosted content. During the analysis, we also find some exciting patterns in hosted content of the dark web.

Storage networking technology has enjoyed strong growth in recent years, but security concerns and threats facing networked data have grown equally fast. Today, there are many potential threats that are targeted at storage networks, including data modification, destruction and theft, DoS attacks, malware, hardware theft and unauthorized access, among others. In order for a Storage Area Network (SAN) to be secure, each of these threats must be individually addressed. In this paper, we present a comparative study by implementing different security methods in IP Storage network.

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