A traditional crawler picks up a URL, retrieves the corresponding page and extracts various links, adding them to the queue. A deep Web crawler, after adding links to the queue, checks for forms. If forms are present, it processes them and retrieves the required information. Various techniques have been proposed for crawling deep Web information, but much remains undiscovered. In this paper, the authors analyze and compare important deep Web information crawling techniques to find their relative limitations and advantages. To minimize limitations of existing deep Web crawlers, a novel architecture is proposed based on QIIIEP specifications Sharma & Sharma, 2009. The proposed architecture is cost effective and has features of privatized search and general search for deep Web data hidden behind html forms.

A Novel Architecture for Deep Web Crawler

The clear, social, and dark web have lately been identified as rich sources of valuable cyber-security information that -given the appropriate tools and methods-may be identified, crawled and subsequently leveraged to actionable cyber-threat intelligence. In this work, we focus on the information gathering task, and present a novel crawling architecture for transparently harvesting data from security websites in the clear web, security forums in the social web, and hacker forums/marketplaces in the dark web. The proposed architecture adopts a two-phase approach to data harvesting. Initially a machine learning-based crawler is used to direct the harvesting towards websites of interest, while in the second phase state-of-the-art statistical language modelling techniques are used to represent the harvested information in a latent low-dimensional feature space and rank it based on its potential relevance to the task at hand. The proposed architecture is realised using exclusively open-source tools, and a preliminary evaluation with crowdsourced results demonstrates its effectiveness.

A Crawler Architecture for Harvesting the Clear, Social, and Dark Web for IoT-Related Cyber-Threat Intelligence

The key to Deep Web crawling is to submit promising keywords to query form and retrieve Deep Web content efficiently. To select keywords, existing methods make a decision based on keywords’ statistic information deriving from TF and DF in local acquired records, thus work well only in textual databases providing full text search interfaces, whereas not well in structured databases of multi-attribute or field-restricted search interfaces. This paper proposes a novel Deep Web crawling method. Keywords are encoded as a tuple by its linguistic, statistic and HTML features so that a harvest rate evaluation model can be learned from the issued keywords for the un-issued in future. The method breaks through the assumption of plain-text search made by existing methods. Experimental results show that the method outperforms the state of the art methods.

Learning Deep Web Crawling with Diverse Features

Big Data : Storage, Sharing, and Security

This paper proposes the concept of Minimum Executable Pattern (MEP), and then presents a MEP generation method and a MEP-based Deep Web adaptive query method. The query method extends query interface from single textbox to MEP set, and generates local-optimal query by choosing a MEP and a keyword vector of the MEP. Our method overcomes the problem of “data islands” to a certain extent which results from deficiency of current methods. The experimental results on six real-world Deep Web sites show that our method outperforms existing methods in terms of query capability and applicability.

Crawling deep web content through query forms

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Crawling deep web content through query forms