The Computational System To Classify Cyber Crime Offenses With Twitter Dataset Using Effdt Classification
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Abstract
The Rapid growth of the Internet in the current decade enables the users to access the internet for day-to-day activities. People access the internet for many purposes: entertainment, Transactions, educational purposes and business. On the other hand cyber-crime has increased equally in terms of handling the massive data in the cloud using the access failures. Cyber-crimes are eventually increasing and reducing cyber-attacks for the data stored in the cloud. Existing framework and approaches fail to control the cybercrime attacks and thus many officers are increased because of the predictive control failure. The present study is focused on developing an effective computational method using a machine learning algorithm to analyze the cybercrime rate and to classify the cybercrimes. The system utilized Natural Language Processing (NLP) is used to process the text data. The particle Swarm Optimization algorithm is used to extract the features from the text stop. The main process involved here is the end sampled feed forward decision tree algorithm used to classify the text where any cyber assault are injected into the text. The main operation is to remove the read and features in the text and classifies the existing test text data Using SVM classifier and K nearest neighbor classifier in order to obtain the efficient classifier.
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