CyberSleuth AI: Intelligent Network Forensics Analyzer

Abstract

CyberSleuth represents a cutting-edge cybersecurity initiative designed to protect Canada\'s critical infrastructure through advanced threat detection and response capabilities. This comprehensive system combines artificial intelligence, machine learning, and human expertise to provide real-time monitoring, analysis, and protection against evolving cyber threats. By leveraging AI-driven analytics for network traffic analysis, anomaly detection, and automated threat response, CyberSleuth processes vast amounts of security data to identify potential threats while minimizing false positives. The system\'s architecture integrates multiple layers of security, including predictive analytics, behavioral analysis, and automated incident response mechanisms, all while maintaining a human-in-the-loop approach for critical decision-making. Through its partnership model between the Government of Canada and critical infrastructure organizations, CyberSleuth facilitates rapid threat intelligence sharing and collaborative defense strategies. This hybrid approach of combining advanced technology with human expertise and interorganizational cooperation creates a robust framework for protecting vital infrastructure against sophisticated cyber attacks. The system\'s success in early threat detection, incident response automation, and cross-sector collaboration demonstrates its effectiveness in strengthening national cybersecurity resilience.

Introduction

Summary: AI-Driven Cyber Forensics Analyzer

In response to the growing complexity and volume of cyber threats and digital evidence, this research introduces the AI-Driven Cyber Forensics Analyzer, a cutting-edge solution that integrates artificial intelligence (AI) and machine learning (ML) into digital forensic investigations. Traditional manual methods struggle to keep up with the scale and sophistication of modern cyber attacks, which can take an average of 287 days to detect and contain (IBM, 2023).

Key Features of the Proposed System:

• Real-Time Analysis: Immediate detection and response to threats through continuous network monitoring.

• Pattern Recognition: AI models identify hidden patterns and anomalies often missed by manual reviews.

• Automated Evidence Processing: Streamlined evidence collection and analysis reduce human error and investigation time.

• Scalability: Capable of managing large-scale data across diverse platforms.

Literature Survey:

The research builds on previous work, including:

• Zhang et al. (2020) on automated forensics,

• Kumar and Singh (2021) on malware detection using deep learning,

• Chen et al. (2023) on identifying zero-day attacks with ensemble learning,

• Roberts et al. (2021) and others on automated evidence triage and memory forensics,

• Research into encrypted traffic analysis, big data handling, and real-time network behavior.

Current challenges include real-time processing, data integrity, and ethical/legal issues related to AI-driven evidence handling. Emerging trends include blockchain for evidence integrity, cloud-based analysis, IoT forensics, and potential use of quantum computing.

Methodology:

The Analyzer employs:

• Automated data collection via tools like Scapy and libpcap,

• AI-based analysis using supervised and unsupervised ML,

• Deep learning for traffic classification,

• Evidence preservation through hashing and chain of custody protocols,

• Visualization tools for interactive dashboards,

• Automated reporting and secure data handling.

System Analysis:

• Existing Systems: Suffer from manual inefficiency, fragmented tools, delayed responses, and poor visualization.

• Proposed System: Integrates scanning, monitoring, and forensic analysis into one AI-powered platform, providing real-time, adaptive, and scalable forensic capabilities with strong evidence handling.

Future Work:

Plans include:

1. Incorporating advanced AI (e.g., reinforcement learning, NLP),

2. Enhancing cloud capabilities for scalability,

3. Implementing immersive visualization (e.g., AR/VR),

4. Developing automated response mechanisms,

5. Expanding into mobile forensics.

Conclusion

In the rapidly evolving landscape of cybersecurity, the complexity and sophistication of cyber threats have grown exponentially, presenting unprecedented challenges to traditional digital forensics methodologies. This research introduces an AI-Driven Cyber Forensics Analyzer, a novel approach that leverages artificial intelligence and machine learning technologies to enhance the efficiency and accuracy of digital forensic investigations. The exponential growth of digital data, coupled with the increasing sophistication of cyber attacks, has created a significant challenge for forensic investigators. Traditional manual analysis methods are becoming increasingly inadequate in handling the volume, velocity, and variety of digital evidence. According to recent statistics, the average time to identify and contain a data breach is 287 days (IBM Security, 2023) [1], highlighting the critical need for more efficient forensic analysis tools.

References

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Copyright

Copyright © 2025 Mr. K. V Siva Prasad Reddy, B. Mohith, P. Mahesh Babu, K. Navtej. 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.