基于深度学习的入侵检测与对手
1National Institute of Standards and Technology, Gaithersburg, MD 20899, USA.
IEEE access : practical innovations, open solutions
|June 17, 2024
概括
深度神经网络在入侵检测系统 (IDS) 中容易受到敌对攻击. 本研究检查了使用NSL-KDD数据集对深度学习IDS进行最先进的攻击,以了解漏洞.
科学领域:
- 计算机科学 计算机科学
- 网络安全 网络安全
- 机器学习 机器学习
背景情况:
- 深度神经网络 (DNN) 在机器学习方面表现出色,包括入侵检测系统 (IDS).
- 最近的研究表明,DNN容易发生对抗性例子,其中微小的像素变化会导致错误分类.
- 这一漏洞引起了对安全关键应用程序 (如IDS) 的担忧.
研究的目的:
- 调查针对基于深度学习的IDS的先进攻击算法的性能.
- 在遭受攻击时检查IDS中使用的神经网络的漏洞.
- 探索个体特征在为IDS创建对抗性示例时的影响.
主要方法:
- 使用TensorFlow实现了深度神经网络.
- 评估了最先进的对抗性攻击算法.
- 用NSL-KDD数据集进行实验.
主要成果:
- 证明了对抗攻击对深度学习IDS的有效性.
- 在受到攻击的神经网络模型中确定了特定的漏洞.
- 获得了对对抗性示例生成的特征重要性的见解.
结论:
- 基于深度学习的IDS容易受到敌对攻击.
- 了解特征角色对于开发强大的防御至关重要.
- 需要进一步的研究来提高IDS中的DNN的安全性.
相关概念视频
01:18Steps in Outbreak Investigation
122
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
122
01:05Difference from Background: Limit of Detection
6.3K
The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
The LOD indicates the presence or absence...
6.3K