5 tháng trước
With the continuous evolution of the Windows operating system, malware-especially those based on Portable Executable (PE) files-has become increasingly sophisticated. Recent studies have widely adopted artificial intelligence (AI), particularly deep learning (DL) models, for malware detection. Among these, approaches focusing on API function analysis have shown their potential, but often…
Đọc tiếp >>>7 tháng trước
To keep pace with the rapid advancements in both the quality and complexity of malware, recent research has extensively employed machine learning (ML) and deep learning (DL) models to detect malicious software, particularly in the widely used Windows system. Despite demonstrating promising accuracy in identifying malware, these models remain vulnerable…
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Malware continues to evolve, exposing weaknesses in conventional detectors and motivating realistic adversarial evaluations. Prior RL-based evasion methods often rely on partial model access or feature-level perturbations, limiting realism under strict black-box constraints. We propose xPriMES, a dual-environment reinforcement learning framework that generates functionality-preserving binary mutations for malware evasion in…
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The Android platform is the dominant mobile operating system, making it a prime target for malware attacks. The increasing complexity of Android malware necessitates advanced detection methods that integrate modern machine learning techniques with security analysis. This study aims to enhance Android malware detection and classification by leveraging pre-trained language…
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Honeypot allocation has emerged as a pivotal strategy in cyber deception. However, existing approaches often face scalability issues, limited coordination, and inadequate consideration of intrusion stages, which constrain their effectiveness in complex attack environments. To address these challenges, this study introduces Hawkeyes, a hierarchical multi-agent reinforcement learning (HMARL) framework for…
Đọc tiếp >>>8 tháng trước
The growing fragmentation of the blockchain ecosystem has intensified the demand for secure and scalable interoperability protocols. Existing cross-chain solutions face an ‘interoperability trilemma’, struggling to simultaneously achieve decentralization, security, and scalability amidst a fragmented blockchain ecosystem. This paper introduces Acheron, a market-based multi-relay architecture designed to navigate this trilemma…
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Smart contracts secure over $287 billion in total value locked, yet vulnerabilities caused $3.8 billion in losses during 2023. Traditional detection approaches require complete code disclosure, raising intellectual property concerns for enterprises. We present FedVuln, a privacy-preserving federated graph learning framework enabling collaborative vulnerability detection across mutually distrustful organizations without…
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