Network Forensics and Incident Response Tool with AI-Assisted Threat Analysis

Abstract

This research aims at the creation of an enhanced Network Forensics and Incident Response Tool, making use of AI-based threat analysis to enhance network security. The tool is engineered to offer real-time monitoring, detection, and response features that allow for the identification of prospective security incidents. Through the inspection of network traffic via packet capture (PCAP) files, AI algorithms learn to identify anomalies and suspicious activity patterns that reflect cyber threats. The incorporation of AI dramatically enhances the accuracy of threat-detection, minimizes false positives, and maximizes operational efficiency. Moreover, the software includes automated email notifications to instantly alert security teams of identified incidents, facilitating quick response and mitigation. The study emphasizes the imperative of merging machine learning with network forensics to develop a holistic security incident management approach. The tool\'s flexibility ensures that it is well-suited to various network infrastructures, providing an anticipatory solution for protection against constantly changing cyber threats. This research highlights the role of AI-based tools in contemporary cybersecurity systems, calling attention to their ability to revolutionize threat response and detection.

Introduction

In today’s digital landscape, rising cyber threats outpace traditional security measures, which are often manual, static, and ill-equipped to detect novel attacks. Network forensics and incident response are vital, but current tools rely heavily on signature-based detection and human intervention, making them slow, error-prone, and ineffective against sophisticated threats like zero-day attacks.

To address these gaps, the study proposes an AI-integrated system combining real-time packet capture, machine learning-based threat detection, and automated incident response. Using algorithms like Random Forest (RF) and Support Vector Machines (SVM) trained on datasets such as NSL-KDD and CICIDS 2017, the system detects anomalies with high accuracy and reduced false positives. It includes features like email alerts, threat severity ranking, and automatic mitigation actions.

Key Contributions:

• Identified Research Gap: Lack of real-time, intelligent, scalable solutions for network forensics and automated response.

• Proposed System: Java-based NFIR tool utilizing ML for accurate, real-time detection and automated response.

• Evaluation Metrics: High detection accuracy (up to 98.5% for DDoS), low false positives (3.4%), and fast response time (~2.5 seconds).

• Comparative Superiority: Outperforms traditional tools (e.g., Snort, Suricata) and existing AI models in precision, speed, and false alarm reduction.

System Features:

• Preprocessing: Extracts and normalizes features from PCAP files.

• ML Integration: Learns from traffic behavior to detect evolving threats.

• Automation: Initiates responses based on threat type and severity.

• Scalability: Designed for large-scale networks using future tools like Apache Kafka.

Conclusion

The Network Forensics and Incident Response Tool, based on AI-powered threat analysis and Java-developed, presents a solid solution to improving network security. Combining real-time packet capture, machine learning-powered threat detection, and automated response, the tool is highly accurate, has minimal false positives, and provides rapid response in detecting most network attacks. Yet, more refinements are required to increase attack coverage, provide better scalability, and enhance capability to adapt against new threats for it to continue being effective under dynamic, heavy-traffic settings. Deploying future innovations, including embedding deep learning models, high-throughput network optimization, and real-time adaptability, will greatly enhance the capabilities of the tool. Other features, including a Graphical User Interface and SIEM system integration, will enhance usability and compatibility with current cybersecurity infrastructures. These enhancements will make the tool a robust, scaleable, and active solution, able to address the changing challenges of contemporary network security.

References

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Copyright

Copyright © 2025 Mr. P.V.S.N. Murthy, Praneeth Kumar Neelamsetty, Pragya Gayatri Sreedharala, Madhu Babu Kondru, Likhith Kudara. 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.