使用XAI转移学习以获得强大的恶意软件和物联网网络安全
Ahmad Almadhor1, Shtwai Alsubai2, Natalia Kryvinska3
1Department of Computer Engineering and Networks, College of Computer and Information Sciences, Jouf University, Sakaka, 72388, Saudi Arabia.
Scientific reports
|July 24, 2025
概括
本研究介绍了一种深度学习模型,用于检测利用隐私的恶意软件,利用转移学习来提高通过内存分析和网络入侵检测来提高网络安全威胁的准确性.
科学领域:
- 网络安全 网络安全
- 机器学习 机器学习
- 数字法医学数字法医学
背景情况:
- 越来越多的利用隐私的恶意软件需要先进的检测方法.
- 模糊技术使得实时恶意软件检测具有挑战性.
- 传统的法医分析需要复杂的模式识别.
研究的目的:
- 开发一个深度学习模型来分类隐藏的恶意软件.
- 通过转移学习提高物联网和网络流量的入侵检测.
- 提高恶意软件检测模型的透明度和互操作性.
主要方法:
- 开发了一个在MalwareMemoryDump数据集上训练的深度学习模型.
- 实施转移学习以适应NF-TON-IoT和UNSW-NB15数据集的模型.
- 综合可解释的人工智能 (XAI) 为模型透明度.
主要成果:
- 在MalwareMemoryDump上达到99.9%的准确性,在NF-TON-IoT和UNSW-NB15数据集上达到96%的准确性.
- 在跨域检测中证明了提高准确性和效率.
- 通过转移学习来减少培训时间和计算成本.
结论:
- 拟议的深度学习模型有效地处理各种网络安全威胁.
- 转移学习显著提高了跨不同领域的恶意软件检测.
- 该模型提供了一种高效和可通用的方法,其性能优于现有的安全技术.
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