网络哨兵:在高风险的操作环境中检测恶意软件的透明防御框架
Mainak Basak1, Myung-Mook Han1
1School of Computing, Gachon University, Seongnam-si 13120, Republic of Korea.
Sensors (Basel, Switzerland)
|June 19, 2024
概括
本研究介绍了一种改进的基于图像的恶意软件检测方法,使用一种新的双分支深度网络. 该方法通过完善功能和学习缺失信息来提高恶意软件分类性能.
科学领域:
- 计算机科学 计算机科学
- 网络安全 网络安全
- 人工智能的人工智能
背景情况:
- 恶意软件分类对于网络安全至关重要,但目前的方法面临性能限制.
- 基于图像的恶意软件检测通过将二进制文件转换为视觉数据提供了一个有希望的替代方案.
研究的目的:
- 提出一种新的双分支深度网络,用于增强基于图像的恶意软件分类.
- 通过完善特征提取和整合辅助信息来解决现有的恶意软件检测技术的局限性.
主要方法:
- 为恶意软件图像分析开发了一个双分支深度网络架构.
- 该网络结合了更快的不对称空间注意力,以提升功能.
- 集成了一个辅助功能分支,以捕获额外的恶意软件图像信息.
主要成果:
- 与最先进的深度学习方法相比,提出的方法显示出更高的性能.
- 实验结果证实了网络在各种评估指标上的有效性.
- 注意力机制和辅助分支的整合显著提高了分类准确性.
结论:
- 开发的双分支深度网络为基于图像的恶意软件分类提供了强大而有效的解决方案.
- 这种方法通过改进特征表示和学习来推进恶意软件检测领域.
- 这些发现表明,对基于深度学习的网络安全解决方案的未来研究有希望的方向.
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