Privacy Protection System for Secure Authentication and Internal Intrusion Detection System

Abstract

The system proposes a security system, named the Internal Intrusion Detection and Protection System (IIDPS) at system call level, which creates personal profiles for users to keep track of their usage habits as the forensic features. The IIDPS uses a local computational grid to detect malicious behaviors in a real-time manner. The number of hacking and intrusion incidents is increasing alarmingly each year as new technology rolls out. The system designed Intrusion Detection System (IDS) that implements predefined algorithms for identifying the attacks over a network. Therefore, in this project, a security system, named the Internal Intrusion Detection and Protection System (IIDPS), is proposed to detect insider attacks at SC level by using data mining and forensic techniques. The system can identify a users forensic features by analyzing the corresponding SCs to enhance the accuracy of attack detection, and able to port the IIDPS to a parallel system to further shorten its detection response time.