Network Honeypot System for Threat Analysis

ABSTRACTModern network infrastructures are increasinglyexposed to sophisticated cyberattacks that bypasstraditional security mechanisms. Conventionalintrusion detection and prevention systems oftenprovide limited visibility into attacker behavior andemerging threat patterns. This paper presents aNetwork Honeypot System for Threat Analysis thatdeploys decoy network services to attract attackersand safely capture malicious activities in acontrolled environment. The system recordsdetailed information such as source IP addresses,attempted exploits, accessed services, andcommand-level interactions. The collected data isanalyzed using security monitoring and loganalysis tools to identify attack trends and threatintelligence. Experimental results show that theproposed honeypot system enhances situationalawareness, supports proactive defense strategies,and strengthens overall network security.Furthermore, the honeypot framework supportscontinuous monitoring and long-term datacollection, enabling researchers and administratorsto study evolving attack behaviors over time. Byisolating malicious interactions from productionsystems, the proposed approach minimizesoperational risk while providing actionableinsights. This work highlights the importance ofcyber deception techniques as a complementarylayer to conventional security mechanisms inmodern network environments.

Authors

Institutions

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-01-12
DOI
https://doi.org/10.5281/zenodo.18220861
Primary Topic
Network Security and Intrusion Detection
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Network Honeypot System for Threat Analysis

Mr. R. Kumaravel, Kapilan V, Balaje K, Ganesan G
Zenodo (CERN European Organization for Nuclear Research)
Network Security and Intrusion Detection
article

Network Honeypot System for Threat Analysis

Mr. R. Kumaravel, Kapilan V, Balaje K, Ganesan G
article en

Abstract

ABSTRACTModern network infrastructures are increasinglyexposed to sophisticated cyberattacks that bypasstraditional security mechanisms. Conventionalintrusion detection and prevention systems oftenprovide limited visibility into attacker behavior andemerging threat patterns. This paper presents aNetwork Honeypot System for Threat Analysis thatdeploys decoy network services to attract attackersand safely capture malicious activities in acontrolled environment. The system recordsdetailed information such as source IP addresses,attempted exploits, accessed services, andcommand-level interactions. The collected data isanalyzed using security monitoring and loganalysis tools to identify attack trends and threatintelligence. Experimental results show that theproposed honeypot system enhances situationalawareness, supports proactive defense strategies,and strengthens overall network security.Furthermore, the honeypot framework supportscontinuous monitoring and long-term datacollection, enabling researchers and administratorsto study evolving attack behaviors over time. Byisolating malicious interactions from productionsystems, the proposed approach minimizesoperational risk while providing actionableinsights. This work highlights the importance ofcyber deception techniques as a complementarylayer to conventional security mechanisms inmodern network environments.

Zenodo (CERN European Organization for Nuclear Research)
Kamdhenu University (IN)
Industry, innovation and infrastructure
Openalex Percentile: Top 92%
Network Security and Intrusion Detection
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.