Knowledge Discovery in Tweets for the Prevention of Inference Attacks

Objectives: The objective of our proposed study is to eliminate the inference attacks through knowledge discovery process without compromising the accuracy of the systems. Methods/Analysis: In this paper, we have explored an enhanced privacy adoption scheme for twitter users. A supervised learning model that prevents the inference attacks using hit analytics and metadata knowledge derivation systems. By integrating the extracted feature vectors, we have eliminated the inference attacks caused by third party applications and elegantly classified the malignant and benign URLs. Findings: The proposed model is experimented in twitter dataset. The tweet URLs are collected and segmented into different URLs and similar URLs. The performance analysis is in terms of frequency of URLs usage, User’s assurance level via hit analytics and accuracy. It is evident from the results that higher usage of twitter network is employed and as the rate of users increases, the proposed classifier detects the benign and malicious URLs at significant level of 83%. Novelty/Improvement: A recent development made in the digital era has seen a phenomenal growth of social users. The social users make use of social networks to share their opinions about the facts, events etc. Our proposed study has incorporated on twitter users where trained classifier is developed from the behavior analysis of tweets users.