基于多重二进制观测信息熵的线性系统识别导向最优篡改攻击策略与实施

Algorithms Pub Date : 2024-06-03 DOI:10.3390/a17060239
Zhongwei Bai, Peng Yu, Yan Liu, Jin Guo
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引用次数: 0

摘要

随着计算机技术、通信技术和控制技术的飞速发展,网络物理系统(CPS)得到了广泛的应用和发展。然而,CPS 中存在大量的信息交互,导致网络传输的数据量不断增加。数据通信一旦受到网络攻击,将严重影响系统的安全性和稳定性。本文针对具有多个二进制观测值的线性系统中存在的数据篡改攻击,在防御方估计算法未知的情况下,从攻击方的角度出发,构建了基于信息熵的优化指标,并对问题进行了建模。针对有能量约束的多参数优化问题,本文利用粒子群优化(PSO)获得最优数据篡改攻击解集,并给出了未知参数情况下的未知参数估计方法。为了实现在线执行的实时改进,设计了 BP 神经网络。最后,通过数值模拟验证了结论的正确性。这意味着攻击方可以在不知道防御方判别算法的情况下,根据信息熵构建有效的度量。此外,基于 PSO 和 BP 的最优攻击策略实施也是有效的。
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Linear System Identification-Oriented Optimal Tampering Attack Strategy and Implementation Based on Information Entropy with Multiple Binary Observations
With the rapid development of computer technology, communication technology, and control technology, cyber-physical systems (CPSs) have been widely used and developed. However, there are massive information interactions in CPSs, which lead to an increase in the amount of data transmitted over the network. The data communication, once attacked by the network, will seriously affect the security and stability of the system. In this paper, for the data tampering attack existing in the linear system with multiple binary observations, in the case where the estimation algorithm of the defender is unknown, the optimization index is constructed based on information entropy from the attacker’s point of view, and the problem is modeled. For the problem of the multi-parameter optimization with energy constraints, this paper uses particle swarm optimization (PSO) to obtain the optimal data tampering attack solution set, and gives the estimation method of unknown parameters in the case of unknown parameters. To implement the real-time improvement of online implementation, the BP neural network is designed. Finally, the validity of the conclusions is verified through numerical simulation. This means that the attacker can construct effective metrics based on information entropy without the knowledge of the defense’s discrimination algorithm. In addition, the optimal attack strategy implementation based on PSO and BP is also effective.
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