Implementation of Intrusion Detection and Prevention System Based on Software Approachs

O.J. Adaramola¹ , G.O. Oamen²

¹ Dept. of Computer Engineering, School of Engineering, Federal polytechnic Ilaro, Ogun State, Nigeria .
² Dept. of Computer Engineering, School of Engineering, Federal polytechnic Ilaro, Ogun State, Nigeria.

Section:Research Paper, Product Type: Journal
Vol.10 , Issue.5 , pp.1-9, Dec-2022

Online published on Dec 31, 2022

Copyright © O.J. Adaramola, G.O. Oamen . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

View this paper at   Google Scholar | DPI Digital Library

XML View     PDF Download

How to Cite this Paper

241 Views    255 Downloads    146 Downloads

Abstract :
The detection of security vulnerabilities is ever more difficult as cyber-attacks get more complicated. The faith in security services like data confidentiality and integrity is eroded by a failure to prevent security breaches. The literature has suggested a number of intrusion detection techniques to counter computer security risks. This effort aims to create an intrusion detection and prevention system, or "IDPS,� An integrated system that maximizes each factor`s advantages while reducing its disadvantages is the proposed solution for intrusion prevention. By demonstrating how attackers can elude detection. The outcome of this research is a security system that can recognize attack attempts, block the IP address of the attacker, and carry out network forensic investigations. According to the findings of our study, Snort the IPS mode in PfSense, can identify assaults aimed at your system, and PfSense, having visualization capability, immediately implements preventive actions by blocking the attacker`s IP address. Network forensics can use this method to conduct an investigation into an attack and determine whether the attack is having a negative impact based on the alarms produced by the snort. It also sheds light on potential future research challenges to stop these attacks and strengthen computer systems` security.

Key-Words / Index Term :
Intrusion Detection, Intrusion Prevention, PfSense, Security, Snort.

References :
[1] N.Gavrilovi?1, V. ?iri?1, N.Lozo1, “Snort IDS System
Visualization Interface for Alert Analysis�, Serbian Journal of Electrical Engineering, Vol. 19, No. 1, pp. 67-78, 2022.
[2] A. Tasneem, A. Kumar, S. Sharma, “Intrusion Detection
Prevention System using SNORT�, International Journal of Computer Applications, Vol.181, No. 32, pp. 0975 – 8887, 2018.
[3] K. Langsari, “Intrusion Detection System (IDS)�, Institut
Teknologi, Sepuluh Nopember (ITS), indonessia, pp.1-14 2015.
[4] A.M. Resmi, Dr. R M. chezian, “Intrusion Detection System
Techniques and Tools: A Survey�, Scholars Journal of Engineering and Technology, Vol.5, No. 3, pp. 122-130, 2017.
[5] J. Surana, J. Sharma, I. Saraf, N. Puri,B. Navin, “A Survey On Intrusion Detection System�, International Journal of Engineering Development and Research, Vol. 5, Issue 2, pp. 960-965, 2017.
[6] A. Kukruza, M. Tayyem, “Snort and beyond�, Research Papet, TCD 577, pp. 1-3, 2018.
[7] H. Hilal, A. Ghafri, Z. Z. Abidin, K. Kurbonov, R. Yusof, “Implementation of Intrusion Detection System using Snort�, Journal of Advanced computing Technology and Application, Vol. 1, No. 1, pp. 1-7, 2019.
[8] F. Erlacher, F. Dressler, “On High-Speed Flow-based Intrusion Detection using Snort-compatible Signatures�, IEEE Transactions on Dependable and Secure Computing, VOL. XX, NO. YY, pp. 1-6, 2019.
[9] H.P. Rao, M.R. Anirudh, U.S. Diksha, S. D. Bhuvana,� Portable Intrusion Detection System�, International Journal of Engineering and Advanced Technology (IJEAT), Vol. 9 Issue. 5, pp. 1258- 1260, 2020
[10] J. Verma, A Bhandari and G Singh,� Review of Existing Data Sets for Network Intrusion Detection System�, Advances in Mathematics; Scientific Journal, vol.9, No. 6, pp. 3849-3854, 2020
[11] S. Sasikumar, K.S. Abhay, V. (C.N.S) kumar,� Network Intrusion Detection and Deduce System�, Turkish Journal of Computer and Mathematics Education, Vol.12, No.9, pp. 404-410, 2021.
[12] J. Ebrima, X. Wang,�A novel hybrid-based approach of snort automatic rule generator and security event correlation (SARG-SEC)�, PeerJ Computer Science, vol. 10, No. 7717, pp. 4-31, 2022
[13] R D. Mounika, R. Naresh, “The concept of Privacy and Standardization of Microservice Architectures in cloud
computing�, European Journal of Molecular & Clinical Medicine, Vol. 7, No 2, pp. 5349-5370, 2020.
[14] F. A. Saputra, Saputra, M. Saputra, J. A. N. Hasim, I. U. Nadhori, & K. Ramli, “The Next?Generation NIDS Platform: Cloud?Based Snort NIDS Using Containers and Big Data�, Big Data and Cognitive Computing, Vol. 6, Issues 1, pp.1-19, 2022.