The increased usage of cloud services, growing number of users, changes in network infrastructure that connect devices running mobile operating systems, and constantly evolving network technology cause novel challenges for cyber security that have never been foreseen before. As a result, to counter arising threats, network security mechanisms, sensors and protection schemes have also to evolve in order to address the needs and problems of nowadays users.

Existing System

In our previous work, we have introduced an innovative evolutionary algorithm for modeling genuine SQL queries generated by web-application. In this paper we have extended our algorithm with Bayes inference in order to incorporate advantages of signature-based and anomaly-based methods. The proposed approach allows for extracting patterns (in form of a PCRE regular expression) of a genuine SQL queries that can be easily incorporated in any rule processing engine (e.g. Snort). Moreover, the results showed that combining that kind of attack detector with character distribution allows for additional effectiveness improvements.

Proposed System

The proposed approach engages a Bayesian inference theory for cyber attacks detection. For that purpose a directed acyclic network (graph) is built, which is a graphic representation of the joint probability distribution function over a set of variables. In such graph each node represents random variable while the edge indicates a dependant relationship.

SYSTEM REQUIREMENTS
SOFTWARE REQUIREMENTS:
• Programming Language : Python
• Font End Technologies : TKInter/Web(HTML,CSS,JS)
• IDE : Jupyter/Spyder/VS Code
• Operating System : Windows 08/10

HARDWARE REQUIREMENTS:

 Processor : Core I3
 RAM Capacity : 2 GB
 Hard Disk : 250 GB
 Monitor : 15″ Color
 Mouse : 2 or 3 Button Mouse
 Key Board : Windows 08/10