International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 02 | Feb 2025
p-ISSN: 2395-0072
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AI-Driven Cloud Forensics: A Novel Framework for Cybercrime Investigation and Digital Evidence Extraction Kali Rama Krishna Vucha1, Karthik Kamarapu2 1Independent Software Researcher, Computer Science, Acharya Nagarjuna University, Andhra Pradesh, India 2 Independent Software Researcher, Computer Science, Osmania University, Telangana, India
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Abstract - In the era of cloud computing, cybercriminal
unauthorized access, data breaches, and complex cyberattacks. Traditional forensic methodologies, designed for on-premise systems, are often ineffective in dynamic cloud environments where data is decentralized, volatile, and governed by multi-jurisdictional policies.
activities have evolved, making traditional digital forensic methodologies inadequate for modern investigative challenges. This research introduces an AI-driven cloud forensic framework designed to enhance cybercrime investigation and digital evidence extraction in cloud environments. The proposed framework leverages machine learning (ML) algorithms, deep learning models, and intelligent automation to detect, analyze, and attribute cyber threats in real time. Unlike conventional forensic approaches, which rely heavily on manual intervention, our AI-powered solution offers automated log analysis, anomaly detection, and forensic evidence correlation across multicloud architectures. By integrating natural language processing (NLP) for log analysis, predictive analytics for cyber threat anticipation, and blockchain-based forensic data integrity validation, this research ensures a secure, scalable, and legally admissible forensic process. Additionally, the study explores the challenges of data sovereignty, multi-jurisdictional compliance, and digital evidence admissibility in AI-driven forensic investigations. The proposed model is tested against real-world cloud security incidents, demonstrating its effectiveness in reducing forensic investigation time, improving accuracy in cyberattack attribution, and enhancing evidence traceability. This research aims to set a new benchmark in cloud forensics by introducing an intelligent, automated, and scalable approach to cybercrime detection. The findings provide critical insights for law enforcement agencies, cloud service providers, and cybersecurity professionals to fortify cloud security postures and streamline forensic investigations.
Artificial Intelligence (AI) has emerged as a transformative force in cybersecurity, offering automated, intelligent solutions for real-time threat detection, incident response, and forensic analysis. AI-driven cloud forensics leverages machine learning algorithms, deep learning models, and natural language processing to analyze vast datasets, identify anomalies, and reconstruct cyber incidents with enhanced accuracy. Unlike conventional forensic approaches that rely on manual intervention and static log analysis, AI-powered techniques provide adaptive and scalable solutions that improve investigation speed and accuracy. This research aims to develop a novel AI-driven forensic framework tailored for cloud environments, addressing critical issues such as digital evidence integrity, legal admissibility, and automated forensic analysis. The proposed framework integrates predictive analytics, blockchain technology for data validation, and federated learning for secure forensic model training. By bridging the gap between AI and digital forensics, this study seeks to provide a scalable, efficient, and legally compliant solution for cloud-based cybercrime investigations.
2. Literature Review The integration of Artificial Intelligence (AI) in cloud forensics has emerged as a pivotal solution to modern cybersecurity challenges. Traditional forensic methodologies, primarily developed for static and onpremise infrastructures, struggle to adapt to the dynamic, multi-tenant, and decentralized nature of cloud environments. As cyber threats become more sophisticated, conventional forensic techniques prove insufficient in handling large-scale digital investigations. AI-driven forensic frameworks offer advanced automation, real-time data analysis, and anomaly detection, thereby enhancing the accuracy and efficiency of cybercrime investigations. This section explores key developments in cloud forensics, AI-driven forensic models, and existing challenges in digital evidence extraction and cybercrime investigation.
Key Words: AI-Driven Cloud Forensics, Automated Forensic Analysis, Cybercrime Investigation, Digital Evidence Extraction, Machine Learning
1.INTRODUCTION The rapid adoption of cloud computing has significantly transformed the digital landscape, offering scalable and cost-effective solutions for data storage, computation, and business operations. However, this shift has also introduced new challenges in cybersecurity, particularly in cybercrime investigation and digital forensics. As organizations increasingly migrate sensitive data to the cloud, cybercriminals exploit vulnerabilities to conduct
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