An enhanced system to identify mischievous social malwares on Facebook applications

Abstract

Hackers try to induce malicious content on Facebook. The user is unknown about the characteristic of malicious apps which differ expressively from friendly apps with respect to numerous features. Weka tool is a collection of machine learning algorithms for data mining tasks which are used to detect and classify the malicious app on Facebook. App-nets are large groups of securely connected applications which are improved using the widely used algorithms. K-means clustering is one such algorithm which is implemented in Weka tool. Security is a major issue for retrieving the clustered data. There is a need to reduce the risk of hackers on Facebook Application. To develop secured business applications Frappe, is used. Frappe is a web information gathering framework to observe the posting behaviour of Facebook app users. Using Frappe for detecting malicious apps and Weka tool for efficient classification, an efficient framework is developed in the proposed system to identify the mischievous Facebook applications.