Blocking Random Network Attacks and Avoiding Network Traffic in Ad Hoc Networks by Using Honeypot Network Security and Intrusion Prevention System

The widespread use of ad hoc networks requires the deployment of strong security mechanisms to counter the growing cyber-attacks. With the incorporation of an Intrusion Prevention System and Honeypot Network Security, the system proposed here can efficiently control network traffic and detect, evaluate, and neutralize random network threats. The solution increases the network's resistance to attacks by combining a real-time monitoring framework with a hybrid security mechanism. The system achieved 98.76% accuracy, much higher than that of conventional security models, as indicated by experimental results based on a publicly available dataset. This approach significantly improves the detection rate and reduces false-positives compared to existing approaches. This strategy strengthens network security and improves performance by minimizing resource utilization and illegal traffic.

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Publication Details

Published
2025-03-27
DOI
https://doi.org/10.1109/icfts62006.2025.11031667
Primary Topic
Network Security and Intrusion Detection
Type
article
Field-Weighted Citation Impact
0.00
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article

Blocking Random Network Attacks and Avoiding Network Traffic in Ad Hoc Networks by Using Honeypot Network Security and Intrusion Prevention System

Suresh Kallam, Ramachandran Vedantham
Network Security and Intrusion Detection
article

Blocking Random Network Attacks and Avoiding Network Traffic in Ad Hoc Networks by Using Honeypot Network Security and Intrusion Prevention System

Suresh Kallam, Ramachandran Vedantham
article en

Abstract

The widespread use of ad hoc networks requires the deployment of strong security mechanisms to counter the growing cyber-attacks. With the incorporation of an Intrusion Prevention System and Honeypot Network Security, the system proposed here can efficiently control network traffic and detect, evaluate, and neutralize random network threats. The solution increases the network's resistance to attacks by combining a real-time monitoring framework with a hybrid security mechanism. The system achieved 98.76% accuracy, much higher than that of conventional security models, as indicated by experimental results based on a publicly available dataset. This approach significantly improves the detection rate and reduces false-positives compared to existing approaches. This strategy strengthens network security and improves performance by minimizing resource utilization and illegal traffic.

Saveetha University (IN)
Openalex Percentile: Top 85%
Network Security and Intrusion Detection
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