用人工智能驱动的网络安全分析揭露网络犯罪
Amir Djenna1, Ezedin Barka2, Achouak Benchikh1
1College of New Technologies of Information and Communication, University of Constantine 2, Constantine 25000, Algeria.
Sensors (Basel, Switzerland)
|July 29, 2023
概括
这项研究引入了一种新的深度学习网络安全方法,以早期检测尸网络攻击. 该方法结合了无监督长期短期记忆 (LSTM) 和监督卷积神经网络 (CNN) 模型,取得了超过98.7%的成功率.
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:
- 全球网络犯罪的增加,加快了COVID-19大流行,对国家GDP构成重大威胁.
- 网络犯罪包括黑客攻击,网络鱼,欺诈,恶意软件和尸网络攻击等多种犯罪行为.
- 现有的网络安全措施需要加强,以实际打击日益复杂的网络威胁.
研究的目的:
- 开发和评估一种新的协作深度学习方法,用于早期识别和检测尸网络攻击.
- 提高对网络威胁情报和新兴尸网络攻击载体的理解.
- 为了加强网络安全法医调查程序.
主要方法:
- 提出了一个混合深度学习模型,将无监督的长期短期记忆 (LSTM) 和监督的卷积神经网络 (CNN) 集成在一起.
- 该模型使用已建立的数据集进行了训练和验证:CTU-13和IoT-23.
- 绩效是根据检测率和错误阳性率来评估的.
主要成果:
- 提出的深度学习方法在检测尸网络攻击方面表现出了卓越的性能.
- 取得了高的成功率超过98.7%.
- 保持了0.04%的低虚假阳性率.
结论:
- 协作深度学习模型有效地提高了对尸网络攻击的早期检测.
- 该研究有助于推进网络威胁情报和法医能力.
- 这种方法为改善针对不断变化的威胁的网络安全提供了强有力的解决方案.
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