The Predicting and Prevention of Malware from Cyber Hacking Breaches in Online Social Network

Abstract

Abstract: Analyzing cyber incident information units is an essential approach for deepening our information of the evolution of the risk situation. This is a notably new studies topic, and plenty of research continue to be to be done. In this paper, we record a statistical evaluation of a breach incident information set similar to 12 years (2005–2017) of cyber hacking sports that encompass malware attacks. We display that, in evaluation to the findings suggested withinside the literature, each hacking breach incident inter-arrival instances and breach sizes need to be modeled through stochastic processes, instead of through distributions due to the fact they show off autocorrelations. Then, we recommend specific stochastic method fashions to, respectively, match the inter-arrival instances and the breach sizes. In this paper we be aware that, through reading their actions, we are able to classify malware right into a small quantity of Behavioral classes, every of which plays a restrained set of misbehaviors that signify them. These misbehaviors may be described through tracking capabilities belonging to exclusive platforms. In this paper we gift a singular host-primarily based totally malware detection machine in OSN which concurrently analyzes and correlates capabilities at 4 levels: kernel, application, person and package, to come across and prevent malicious behaviors. It has been designed to do not forget the ones behaviors traits of virtually each actual malware which may be observed withinside the wild. This prototype detects and efficaciously blocks greater than 96% of malicious apps, which come from 3 massive datasets with approximately 2,800 apps, through exploiting the cooperation of parallel classifiers and a behavioral signature-primarily based totally detector. Keywords: Cyber security, Malware, Emerging technology trends, Emerging cyber threats, Cyber attacks and countermeasures