pfSENSE: Intrusion Detection System for Advanced Network Security

Abstract

Network defense has moved beyond simple perimeter filtering because modern attacks are faster, stealthier, and more adaptive than traditional rule sets can handle. This paper presents a layered security framework built around pfSense firewall, Snort intrusion detection, and the Isolation Forest anomaly detection algorithm. The design combines three complementary capabilities: access control at the network edge, signaturebased detection for known attacks, and machine learning-based anomaly detection for previously unseen behavior. The result is a practical architecture that can identify both documented threats and suspicious traffic patterns that do not match existing signatures. pfSense acts as the gateway and enforces segmentation between trusted and untrusted zones. Snort inspects live packets, compares them against rule sets, and generates alerts for malicious payloads, scans, and suspicious protocol behavior. Isolation Forest processes log-derived features such as packet size, source and destination addresses, connection rate, and timing patterns to detect deviations from normal activity. Because it is unsupervised, the model can be applied even when labeled attack data is limited. The full system is implemented in a virtualized environment so that it remains cost-effective, repeatable, and suitable for academic use. The proposed framework improves visibility, reduces response time, and strengthens the ability to defend against unknown threats. It also provides a basis for automated containment, where suspicious hosts can be blocked through firewall rule updates after detection and validation.

Introduction

As organizations increasingly rely on cloud services, remote access, and real-time communication, their exposure to cyberattacks grows. Attackers exploit weaknesses such as poor configurations, weak passwords, and unpatched systems, and modern techniques like encryption and polymorphic malware make detection harder. Traditional tools like firewalls and signature-based IDSs are no longer sufficient on their own.

To solve this, the proposed system integrates three technologies: pfSense for traffic filtering and segmentation, Snort for detecting known attacks using signatures, and the Isolation Forest machine learning algorithm for anomaly detection of unknown or unusual traffic patterns. Together, they form a defense-in-depth model that can detect both known and unknown threats, with potential for automated responses like blocking malicious IPs.

The existing system is limited because tools often work separately, rely heavily on known attack signatures, generate false positives, lack automation, and may depend on centralized or external systems that add delay. The proposed system improves on this by combining firewall control, signature detection, and behavioral analysis in a unified architecture.

The main objectives are to configure pfSense as a secure gateway, deploy Snort for real-time monitoring, implement Isolation Forest for anomaly detection, and evaluate system performance using simulated attacks. The approach is supported by prior research showing that hybrid systems combining rule-based and machine learning techniques provide better accuracy and adaptability against evolving cyber threats.

Conclusion

This project shows that an intelligent and practical defense architecture can be created by combining pfSense, Snort, and Isolation Forest. pfSense provides policy enforcement, Snort identifies known attacks, and Isolation Forest adds anomaly detection for unknown or unusual behavior. The resulting framework is more capable than any single component operating alone because it combines prevention, detection, and response in one workflow. The design is also academically and operationally useful. It demonstrates how open-source tools can be integrated into a functional security stack and evaluated using realistic test traffic. The virtualized deployment keeps the system accessible and makes it easier to repeat experiments or extend the model. The architecture therefore has value both as a teaching model and as a base for further research. Future work can include more advanced machine learning models, richer feature extraction, and integration with SIEM platforms. Endpoint correlation and threat intelligence enrichment can also be added so that the system can compare local events against broader reputation data. Another useful direction is automated orchestration, where the firewall, IDS, and analytics engine react together with minimal delay. With these extensions, the framework can evolve into a more complete adaptive security platform.

References

[1] M. Roesch, “Snort: Lightweight Intrusion Detection for Networks,” project documentation and system overview. [2] Open Source Community, “pfSense Documentation,” firewall configuration, routing, and segmentation reference. [3] L. Liu et al., “Isolation Forest,” an unsupervised anomaly detection method used for outlier identification. [4] Selected studies on hybrid IDS architectures, firewall segmentation, and machine learning-based security analytics. [5] AlienVault OTX and related reputation services, used for optional threat validation in security workflows. [6] S. Dai, “Network Intrusion Detection and Protection System Based on pfSense and Snort,” Network Security and Informatization, vol. 9, pp. 123–126, 2022. [7] D. Wang, J. Zhang, and J. Yu, “Research on Intelligent Firewalls for Network Security,” in Proc. 2nd Int. Conf. Robot., Intell. Control, Artif. Intell. (RICAI’20), pp. 255–258, 2020. [8] W. Stallings, Network Security Essentials: Applications and Standards, 6th ed. Pearson Education, 2018. [9] M. Roesch, “Snort - Lightweight Intrusion Detection for Networks,” in Proc. 13th USENIX Conf. Syst. Admin., pp. 229–238, 1999. [10] H. N. Huang, “Implementation and Detection of Denial of Service Attacks Against Snort,” Master’s Thesis, Jilin Univ., 2020. [11] Z. Zhang, “Design and Implementation of a Snort-Based Intrusion Detection System,” China Univ. Weights Meas., 2020. [12] T. Liu, K. M. Ting, and Z. H. Zhou, “Isolation Forest: A Fast Anomaly Detection Algorithm,” in Proc. 8th IEEE Int. Conf. Data Min. (ICDM’08), pp. 413–422, 2008. [13] M. Ahmed, A. N. Mahmood, and J. Hu, “A Survey of Network Anomaly Detection Techniques,” J. Netw. Comput. Appl., vol. 60, pp. 19–31, 2016. [14] Y. Tang and Y. Hsieh, “Using Machine Learning for Network Anomaly Detection: A Comparative Study,” IEEE Trans. Inf. Forensics Secur., vol. 13, no. 8, pp. 2003–2016, 2018. [15] P. Ye and Z. Zhang, “Anomaly Detection Method, Apparatus, and Electronic Device Based on Behavioral Whitelist,” China Patent 2018111809412, 2018. [16] V. Chandola, A. Banerjee, and V. Kumar, “Anomaly Detection: A Survey,” ACM Comput. Surv. (CSUR), vol. 41, no. 3, pp. 1–58, 2009. [17] [12] J. Li, “Optimization and Implementation of Snort Intrusion Detection Method,” Master’s Thesis, Northeast Normal Univ., 2021. [18] N. Shone, T. N. Ngoc, V. D. Phai, and Q. Shi, “A Deep Learning Approach to Network Intrusion Detection,” IEEE Trans. Emerg. Topics Comput. Intell., vol. 2, no. 1, pp. 41–50, 2018. [19] L. Wang and P. Roger, “A Security Detection Method for Internet Port Scanning Attacks,” Inf. Secur. Technol., vol. 2, pp. 44–45, 2016. [20] K. Dinakaran, D. Rajalakshmi, and P. Valarmathie, “Efficient Pattern Matching for Uncertain Time Series Data with Optimal Sampling and Dimensionality Reduction,” Microprocess. Microsyst., 2020.

Copyright

Copyright © 2026 Sujitha Jammula, Gochalam Vinod, Rishi Priya Nainamoina, Ms. K. Praveena. 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.