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基于观察者的自适应神经滑动模式控制模糊系统与基于逗留概率的多模式攻击
本研究介绍了多模式拒绝服务 (DoS) 和对模糊非线性系统的欺骗网络攻击的新模型. 拟议的基于观察者的滑动模式控制 (SMC) 策略确保了系统稳定性,抵御这些复杂的攻击.
科学领域:
- 控制系统工程 控制系统工程
- 网络安全 网络安全
- 非线性动力学是一种非线性动力学.
背景情况:
- 基于观察者的滑动模式控制 (SMC) 对模糊非线性系统至关重要.
- 多模式拒绝服务 (DoS) 和欺骗攻击对系统稳定性构成重大威胁.
- 现有的攻击模型往往无法捕捉复杂的网络攻击的动态和随机性质.
研究的目的:
- 开发一种具有时间变化的逗留概率的新型多模式DoS攻击模型.
- 使用无边界非线性函数和自适应神经网络 (NN) 建模欺骗攻击.
- 为遭受网络攻击的模糊非线性系统设计一个强大的基于观察者的SMC战略.
主要方法:
- 引入了一种新的多模式DoS攻击模型,结合了时间变化的逗留概率.
- 模拟欺骗攻击作为无边界的非线性函数,通过自适应的NNs近似.
- 设计了一个模糊的滑动表面和基于观察者的SMC定律,确保平均平方估计上限.
主要成果:
- 提出的DoS模型准确地代表了随机攻击行为,克服了基于马尔科夫模型的局限性.
- 适应性NN有效地减轻了欺骗攻击对系统稳定性的影响.
- 开发的基于观察者的SMC定律保证了模糊非线性系统的平均平方估计上限.
结论:
- 拟议的控制策略有效地确保了模糊非线性系统的稳定性,防止多模式DoS和欺骗攻击.
- 新型攻击建模和自适应性NN方法在网络威胁下的强有力的控制方面取得了重大进展.
- 通过道二极管电路模型的验证证实了该策略的优越性和有效性.
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