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International Journal of Creative and Open Research in Engineering and Management

A Peer-Reviewed, Open-Access International Journal Supporting Multidisciplinary Research, Digital Publishing Standards, DOI Registration, and Academic Indexing.
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ISSN: 3108-1754 (Online)
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ISO Certification: 9001:2015
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License: CC BY 4.0
Peer Review: Double Blind
Volume 02, Issue 04

Published on: April 2026

SENTINEL WALL CYBER DEFENCE SYSTEM

Akansha Rajput Rimjhim Abhishek Yadav Harshit Tiwari Aniket

Department of Computer Science and Engineering Nitra Technical Campus Raj Nagar

Ghaziabad UP India

Article Status

Plagiarism Passed Peer Reviewed Open Access

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Abstract

As the number of cyber attacks and other forms of internet-based hacking continues to grow exponentially every year, there is an increasing demand for new ways to secure networks efficiently and effectively while also being cost-effective and using as few resources as possible. Many conventional firewalls operate using pre-defined "rules," and do not provide real-time monitoring of any form so that they can quickly react to dynamic types of threats. The Sentinel Wall Cyber Defence System is a modular and light-weight firewall that has been specifically designed to monitor, analyze and control the traffic that flows in and out of a computer system/network in real-time using very little processing power/resources.

The system uses Scapy to capture and analyze packets, and iptables implements the security policies enforced at an operating system level. Sentinels will use a rule-based engine to process packets received based on pre-existing configurations defined in either JSON or YAML format to allow, drop, or log the traffic. To provide users with an easy to use interface to visually see network activity as it happens, a Graphical User Interface (GUI) has been developed that provides real-time monitoring of live network activity, as well as user activity on the network, and logs of all users that have been blocked from accessing any part of the network because they represented a threat to the network.

Results from testing indicate that the Sentinel Wall Cyber Defence System effectively filters unwanted network traffic, blocks unauthorized users from gaining access to the network, and uses very little processing power; thus providing a realistic, scalable method for improving network security, particularly in small or educational environments.

Keywords:
Cybersecurity, Personal Firewall, Network Traffic Analysis, Packet Sniffing, Rule-Based Filtering, Threat Detection, System Security, Network Monitoring

How to Cite this Paper

Rajput, A., Rimjhim, , Yadav, A., Tiwari, H. & Aniket, (2026). Sentinel Wall Cyber Defence System. International Journal of Creative and Open Research in Engineering and Management, <i>02</i>(04). https://doi.org/10.55041/ijcope.v2i4.936

Rajput, Akansha, et al.. "Sentinel Wall Cyber Defence System." International Journal of Creative and Open Research in Engineering and Management, vol. 02, no. 04, 2026, pp. . doi:https://doi.org/10.55041/ijcope.v2i4.936.

Rajput, Akansha, Rimjhim,Abhishek Yadav,Harshit Tiwari, and Aniket. "Sentinel Wall Cyber Defence System." International Journal of Creative and Open Research in Engineering and Management 02, no. 04 (2026). https://doi.org/https://doi.org/10.55041/ijcope.v2i4.936.

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References

[1] W. Stallings, Cryptography and Network Security, 7th ed., Pearson, 2017.
[2] W. Stallings, Network Security Essentials, Pearson, 2014.
[3] NIST, “Framework for Improving Critical Infrastructure Cybersecurity,” 2018.
[4] M. Roesch, “Snort: Lightweight Intrusion Detection,” USENIX, 1999.
[5] OSSEC, “Host-Based Intrusion Detection Guide,” 2020.
[6] V. Paxson, “Bro: A System for Detecting Network Intruders,” Computer Networks, 1999.
[7] S. Miettinen et al., “IoT Sentinel,” IEEE ICDCS, 2017.
[8] A. Javaid et al., “Deep Learning for Network Intrusion Detection,” IEEE, 2016.
[9] Y. Xin et al., “Machine Learning for Cybersecurity,” IEEE Access, 2018.
[10] A. Patcha and J. Park, “An Overview of Anomaly Detection,” Computer Networks, 2007.

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  • All submissions are screened under plagiarism detection.
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  • Peer Review Type: Double-Blind Peer Review
  • Published on: May 01 2026
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