NETWORK SECURITY CENTRAL MONITORING SYSTEM

Abstract

Advanced technology makes computers an essential part of humans and business day-to- day activities. Increased information over the past decade led to the creation of big data. Each day, people interact with companies and among themselves through several avenues that include internet clicks, mobile phone transactions, user-generated content, and social media, among others. Increased volume, velocity, and variability of big data presents major threats to information storage, hence the need to enhance network security. As organizations utilize different methods to access and manage big data, malicious parties improve their capabilities to benefit from systems with weaker security measures. These unauthorized parties attack network systems using viruses, malware, and advanced attacks, among others. Organizations recognized the need for secure and safe network systems that prevent unauthorized access to information. This project proposes a method that uses ELK STACK open-source platform to improve network security system. Notably, the proposed approach is integrated with other tools to enhance the security of information contained within the network system. The method uses Suricata, an Intrusion Detection System (IDS) and Zeek IDS to conduct user behavior analysis. Besides, the ELK STACK is integrated and uses a Pfsense firewall and other networking services, such as HTTP, FTP, and SSH, to enhance security, whereas an X-Pack machine learning tool is used to extracts data. This project chooses the ELK STACK platform as it aims to demonstrate effectiveness of an integrated network security central monitoring system in enhancing data security. The project demonstrates the efficiency of an integrated centralized security method over individual usage of different security components.

Date of Award	2020
Original language	American English
Awarding Institution	HBKU College of Science and Engineering