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Leaky Scanning02:28

Leaky Scanning

During most eukaryotic translation processes, the small 40S ribosome subunit scans an mRNA from its 5' end until it encounters the first start AUG codon. The large 60S ribosomal subunit then joins the smaller one to initiate protein synthesis. The location of the translation initiation is largely determined by the nucleotides near the start codon as there may be multiple translation initiation sites present on the mRNA.  Marilyn Kozak discovered that the sequence RCCAUGG (where R stands for...
Line Protection with Impedance Relays01:27

Line Protection with Impedance Relays

Coordinating time-delay overcurrent relays in complex radial systems and directional overcurrent relays in multi-source transmission loops can be challenging. Impedance relays address these issues by responding to the voltage-to-current ratio, specifically measuring the apparent impedance of a line. These relays become more sensitive during faults as current increases and voltage decreases, thereby reducing the apparent impedance.
Under normal conditions, low load currents keep the measured...

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Updated: Jul 19, 2026

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
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使用深度学习来保护CAN总线,以检测车辆中的入侵.

Ritu Rai1, Jyoti Grover1, Prinkle Sharma2

  • 1Department of Computer Science and Engineering, Malaviya National Institute of Technology, Jaipur, 302017, India.

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概括

深度学习有效地检测到车辆通信网络中的网络威胁. LSTM和VGG-16模型在识别对控制器区域网络 (CAN) 总线的攻击方面表现出高准确性,提高了汽车安全性.

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科学领域:

  • 网络安全 网络安全
  • 汽车工程 汽车工程
  • 机器学习 机器学习

背景情况:

  • 控制器区域网络 (CAN) 总线对于车辆通信至关重要,但缺乏安全性,使智能运输系统 (ITS) 容易受到网络攻击.
  • 拒绝服务 (DoS),模糊,假冒和伪造等攻击对车辆安全和数据完整性构成重大风险.

研究的目的:

  • 评估深度学习方法在CAN总线网络内检测入侵的有效性.
  • 评估各种反复神经网络 (RNN) 架构在识别汽车网络威胁方面的性能.

主要方法:

  • 使用了三个数据集:汽车黑客,生存分析和OTIDS用于训练和测试深度学习模型.
  • 探索重复神经网络 (RNN) 变体,包括长期短期记忆 (LSTM),门式重复单元 (GRU) 和VGG-16,以分析CAN消息的时间和空间特征.
  • 实现了Bi-LSTM,通过向前和向后处理数据来增强序列分析.

主要成果:

  • 在对入侵检测的二进制分类任务中,LSTM实现了99.89%的准确性.
  • VGG-16在多类分类场景中显示出100%的准确性.
  • 这些模型有效地识别了CAN总线数据中的异常和网络威胁.

结论:

  • 深度学习技术,特别是LSTM和VGG-16,显示出增强智能运输系统 (ITS) 安全性的巨大潜力.
  • 这些方法为检测和减轻CAN总线网络上的网络攻击提供了强大的解决方案,提高了车辆的整体弹性.