Online retail platforms encounter numerous challenges, such as cyber-attacks, data breaches, device failures, and operational disruptions. These challenges have intensified in recent years, underscoring the importance of prioritizing resilience for businesses. Unfortunately, conventional cybersecurity methods have proven insufficient in thwarting sophisticated cybercrime tactics. This paper proposes a novel resilience strategy that leverages Explainable Deep Learning technologies and a Blockchain-based consensus protocol strategy. By combining these two approaches, our strategy enables rapid incident detection, explains the features and related vulnerabilities that are used, and enhances decision-making during cyber incidents. To validate the efficacy of our approach, we conducted experiments using NAB datasets, preprocessed and trained the data, and performed an experimental study on real online retail architectures. Our results demonstrate the effectiveness of the proposed framework in supporting business and operation continuity and creating more efficient cyber resilience strategies that will enhance decision-making capabilities.

中文翻译：

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使用可解释的深度学习方法和基于区块链的共识协议的在线零售网络弹性框架


在线零售平台面临众多挑战，例如网络攻击、数据泄露、设备故障和运营中断。近年来，这些挑战愈演愈烈，凸显了优先考虑企业复原力的重要性。不幸的是，传统的网络安全方法已被证明不足以阻止复杂的网络犯罪策略。本文提出了一种新颖的弹性策略，利用可解释的深度学习技术和基于区块链的共识协议策略。通过结合这两种方法，我们的策略可以实现快速事件检测，解释所使用的功能和相关漏洞，并增强网络事件期间的决策。为了验证我们方法的有效性，我们使用 NAB 数据集进行了实验，对数据进行了预处理和训练，并对真实的在线零售架构进行了实验研究。我们的结果证明了所提出的框架在支持业务和运营连续性以及创建更有效的网络弹性策略以增强决策能力方面的有效性。