BIG DATA ANALYSIS SYSTEM CONCEPT FOR DETECTING UNKNOWN ATTACKS

Abstract

Nowadays threat of previously unknown cyber- attacks are increasing because existing security systems are not able to detect them. Previously, leaking personal information by attacking the PC or destroying the system was very common cyber attacks . But the goal of recent hacking attacks has changed from leaking information and destruction of services to attacking large-scale systems such as critical infrastructures and state agencies. In the other words, existing defence technologies to counter these attacks are based on pattern matching methods which are very limited. Because of this fact, in the event of new and previously unknown attacks, detection rate becomes very low and false negative increases. Today's attacks are prepared by advanced technologies are not detected until the damage has been occurred. Now the challenge is collecting and analyzing the Big Data fast enough to contain threats and perform last remediation. To defend against these unknown attacks, which cannot be detected with existing technology, a new model based on big data analysis techniques that can extract information from a variety of sources to detect future attacks is proposed . The expectation with this model is future Advanced Persistent Threat (APT) detection and prevention.