Best Project Center System Overview

Best Project Center System Overview Objective  To categories the Spam and Non-spam tweets.  To work on a performance evaluation such as Precision, Recall, F-measure.  To categorize the tag based tweets and link based tweets.

Contributions Spam is plaguing Twitter now. In addition, it entices much more victims than email spam. Spam not only interferes user experience, but also causes damage to users, such as malware downloading, phishing, worm propagation, etc.

Understanding and detecting Twitter spam is of great urgency and importance.

In order to achieve best project center in nagercoil, this thesis firstly provide a through data analysis of spam on Twitter. We demonstrate that various deceptive content of spam performs differently in luring victims to malicious sites and the regional response rate to various Twitter spam outbreaks varies greatly. In addition, spammers are becoming “smarter" by employing more complex spamming strategies to avoid being detected. We then carry out a performance evaluation of streaming spam detection frameworks, which was from three different aspects of data, feature and model. From that, we therefore identified an unseen issue in Twitter spam detection, i.e. “Spam Drift". To address this problem, we propose an Lfun scheme, which can learn from unlabeled tweets. Experiments on real-world datasets show that our scheme can greatly improve the detection accuracy. Our contributions of this thesis is summarised as:

Best project center in Tirunelveli have collected and labelled a large Twitter data set of around 600 million tweets. After analysing it, we find various deceptive information of spam performs differently in luring victims to malicious sites. In addition, the regional distribution of victims varies due to deferent types of deceptive information. This suggests the spam detection system could leverage the deceptive information contained in spam tweets to improve detection efficiency.

We have also identified three new spamming strategies applied by spammers, which indicates that spammers are also fighting back to avoid being detected.

Researchers and industry should pay more attention to spammers' behaviour, and propose advanced detection systems.

Research community lacks of a performance evaluation of existing machine learning based streaming spam detection methods. We, thus, evaluate the impact of different factors to performance the streaming spam detection, which include spam to non-spam ratio, feature discretization, training data size, data sampling, timerelated data, and machine learning algorithms. The importance of this work is to show that the streaming spam tweet detection is still a big challenge and a robust detection technique should take into account the three aspects of data, feature and model.

We firstly identify the “Spam" issue in detecting Twitter spam. We then propose a Bayesian scheme which can discover “changed" spam tweets from unlabeled tweets and incorporate them into classifier's training process. Our Bayesian scheme can effectively detect spam tweets even when they are drifting.

Current Twitter spam detection systems can take advantage of our work to further improve their accuracy.

Contact: AB Technologies Chettikulam, Nagercoil – 629002 9840511458 abtech@abtechnologies.in https://abtechnologies.in/