Blockchain: Tool for Controlling Ransomware through Pre-Encryption and Post-Encryption Behavior

Singh, Anamika; Ali, Md. Akkas; Balamurugan, B.; Sharma, Vandana

doi:10.1109/ccict56684.2022.00107

“…The evolution from encryption-centric onslaughts to strategies that primarily engage in data theft has marked a pivotal change in the ransomware domain [15], [16], [17]. This shift has demanded a thorough reevaluation of countermeasures and analytical techniques within the cyber defense community [18], [19], [20].…”

Section: Entropy and Memory Forensics In Ransomwarementioning

confidence: 99%

Entropy and Memory Forensics in Ransomware Analysis: Utilizing LLaMA-7B for Advanced Pattern Recognition

Zhang,

Li,

Zhu

2023

Preprint

0

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This study investigated the utilization of memory forensics and the Large Language Model LLaMA-7B for the purpose of detecting and analyzing contemporary ransomware. It articulates a shift from traditional encryption-focused ransomware attacks to more sophisticated strategies, like data exfiltration, underscoring the evolving nature of these cyber threats. The methodology involves an integrated approach, combining memory forensic techniques with the advanced pattern recognition capabilities of LLaMA-7B, to identify and analyze ransomware signatures within system memory. The results demonstrate the efficacy of this combination in accurately distinguishing between ransomware and benign software, with a particular focus on identifying data exfiltration activities. Discussions revolve around the challenges of keeping pace with the evolving ransomware tactics and the ethical considerations in applying AI in cybersecurity. The study concludes by underscoring the importance of continuous innovation in cybersecurity strategies and the potential of AI integration in developing dynamic defense mechanisms against ransomware.

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“…Additionally, smart contract logic must encode robust access restrictions customized to organizational data usage and classification policies to prevent leakage of sensitive metadata [30], [35]. Regular compliance and security audits can help verify that transactions adhere to the access control rules [73], [72]. Overall, blockchain's unique privacy attributes demand a closer examination of vulnerabilities and a layered approach combining encryption, access control and auditing helps realize the technology's benefits without sacrificing privacy [36], [32].…”

Section: Data Privacy Considerationsmentioning

confidence: 99%

Decentralized Identity Verification System for Data Access to Prevent Data Exfiltration Ransomware

Takeuchi,

Kumamoto,

Yoshida

et al. 2023

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0

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The escalating sophistication of ransomware has catalyzed the transition from traditional encryption-focused attacks to more pernicious data exfiltration techniques. This research proposes a Decentralized Identity Verification System to combat the amplified threats of data theft and extortion. The system integrates blockchain technology to establish a resilient identity management framework reinforced by multi-factor authentication and smart contract-based access policies. By addressing fundamental vulnerabilities enabling unauthorized data access, the system proactively reduces attack surfaces vulnerable to ransomware infiltration. Simulated attack scenarios reflect promising security enhancement outcomes across metrics including prevention of data breaches, access control efficiency and system responsiveness to threats. While scope remains for continued innovation, this exploration highlights the synergetic potential of cryptography, access governance decentralization for reshaping data protection against contemporary cyber risks.

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“…The evolution from encryption-centric onslaughts to strategies that primarily engage in data theft has marked a pivotal change in the ransomware domain [15], [16], [17]. This shift has demanded a thorough reevaluation of countermeasures and analytical techniques within the cyber defense community [18], [19], [20].…”

Section: Entropy and Memory Forensics In Ransomwarementioning

confidence: 99%

Entropy and Memory Forensics in Ransomware Analysis: Utilizing LLaMA-7B for Advanced Pattern Recognition

Zhang,

Li,

Zhu

2023

Preprint

0

View full text Add to dashboard Cite

This study investigated the utilization of memory forensics and the Large Language Model LLaMA-7B for the purpose of detecting and analyzing contemporary ransomware. It articulates a shift from traditional encryption-focused ransomware attacks to more sophisticated strategies, like data exfiltration, underscoring the evolving nature of these cyber threats. The methodology involves an integrated approach, combining memory forensic techniques with the advanced pattern recognition capabilities of LLaMA-7B, to identify and analyze ransomware signatures within system memory. The results demonstrate the efficacy of this combination in accurately distinguishing between ransomware and benign software, with a particular focus on identifying data exfiltration activities. Discussions revolve around the challenges of keeping pace with the evolving ransomware tactics and the ethical considerations in applying AI in cybersecurity. The study concludes by underscoring the importance of continuous innovation in cybersecurity strategies and the potential of AI integration in developing dynamic defense mechanisms against ransomware.

show abstract

Blockchain: Tool for Controlling Ransomware through Pre-Encryption and Post-Encryption Behavior

Cited by 20 publications

References 14 publications

Entropy and Memory Forensics in Ransomware Analysis: Utilizing LLaMA-7B for Advanced Pattern Recognition

Entropy and Memory Forensics in Ransomware Analysis: Utilizing LLaMA-7B for Advanced Pattern Recognition

Decentralized Identity Verification System for Data Access to Prevent Data Exfiltration Ransomware

Entropy and Memory Forensics in Ransomware Analysis: Utilizing LLaMA-7B for Advanced Pattern Recognition

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