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The heterogeneous digital arena emerged as the open depiction for malicious activities, and cyber criminals and terrorists are targeting the cyber depiction for controlling its operation. In the dark web (DW), diverse illegal hacking communities are using the sensing-chip webnet to transfer their bots for tracking the user activity so that criminal activities could be accomplished like money laundering, pornography, child trafficking, drug trafficking, arms and ammunition trafficking, where professionals could also be hired and contracted for generating flood infringement and ransomware infringement.

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One of the most critical activities of revealing terrorism-related information is classifying online documents.The internet provides consumers with a variety of useful knowledge, and the volume of web material is increasingly growing. This makes finding potentially hazardous records incredibly difficult. To define the contents, merely extracting keywords from records is inadequate. Many methods have been studied so far to develop automatic document classification systems, they are mainly computational and knowledge-based approaches. due to the complexities of natural languages, these approaches do not provide sufficient results. To fix this shortcoming, we given approach of structure dependent on the WordNet hierarchy and the frequency of n-gram data that employs word similarity. Using four different queries terms from four different regions, this approach was checked for the NY Times articles that were sampled. Our suggested approach successfully removes background words and phrases from the document recognizes connected to terrorism texts, according to experimental findings.

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