使用MHSA-LSTM机器学习进行早期勒索软件检测的智能传感框架
Abdullah Alqahtani1,2, Mordecai Opoku Ohemeng3, Frederick T Sheldon3
1Department of Computer Science, Najran University, Najran 61441, Saudi Arabia.
Sensors (Basel, Switzerland)
|February 13, 2026
概括
一个新的智能传感框架 (ISF) 使用了一种新的多头自我注意长期短期记忆 (MHSA-LSTM) 模型来早期检测勒索软件. 这种先进的系统显著减少了假阳性,改善了网络安全防御.
科学领域:
- 网络安全 网络安全
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 勒索软件构成一个显著和不断发展的网络安全威胁,经常绕过传统的安全措施.
- 早期检测勒索软件对于减轻损害和防止广泛影响至关重要.
- 现有的深度学习模型在最初的攻击阶段面临数据稀缺和噪音的挑战.
研究的目的:
- 引入一种新的智能传感框架 (ISF),用于主动,早期的勒索软件检测.
- 通过使用专门的传感器模型来提高勒索软件检测的准确性并减少虚假阳性.
- 为了克服传统防御对隐蔽勒索软件攻击的局限性.
主要方法:
- 开发一个多头自我注意长期短期记忆 (MHSA-LSTM) 传感器模型.
- 基于获取信息的相关系数 (μ) 的纳入,以优先考虑歧视性行为特征.
- 通过对39378个勒索软件和9732个良性应用程序样本的动态分析进行验证.
主要成果:
- MHSA-LSTM传感器实现了98.4%的峰值精度,F1得分为0.972,以及0.089.08的低虚假阳性率 (FPR).
- 与CNN-LSTM和堆叠LSTM模型相比,ISF表现出更高的性能.
- 整合相关性系数 (μ) 导致与标准注意力模型相比,FPR减少了49%.
结论:
- 由MHSA-LSTM传感器提供动力的新型ISF,为早期勒索软件检测提供了强大的和可扩展的解决方案.
- 通过获取信息的自我注意机制有效地过噪音并解决数据稀缺问题.
- 显著减少虚假阳性使ISF成为现场网络安全环境的实用解决方案.
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