Threat problem has become more comp

由于<br>需要保护大量设备免受攻击，同时保持系统可靠性和对威胁的实时响应，因此威胁问题在工业环境中变得更加复杂。在这种情况下，工业物联网（IIoT）设备中的威胁检测成为避免恶意IIoT设备注入的重要因素。基于隐马尔可夫模型<br>（HMM）的技术可能是检测检测威胁中最流行的技术。但是，<br>HMM需要对模型和计算资源进行大量培训。同样，<br>HMM的缺点是在使用Baum –<br>用于参数估计的Welch算法。为了优化HMM参数，可以使用全局搜索技术。这项工作提出了<br>用于优化HMM参数的遗传算法（GA）。威胁检测的另一个难点是<br>攻击的动态性质。几种新的威胁正在出现，其变种<br>来自现有的攻击，使威胁建模成为一项艰巨的任务。<br>结果，良好的功能对于对交通模型进行建模至关重要，并提供了一种有效的方法<br>来检测已知和可能未知的攻击。为了<br>从网络流量中更好地提取特征，我们提出了动态滑动窗口W，<br>其宽度为w。所提出的多重HMM在检测威胁方面表现良好。这<br>将仿真结果与基于Baum-Welch算法的方法所获得的结果进行比较，结果显示出更高的准确性和收敛性。

由于<br>需要在保持系统可靠性和对威胁的实时响应的同时，确保大量设备免受攻击。在这种情况下，工业物联网 （IIoT） 设备中对威胁的检测成为避免恶意 IIoT 设备注入的重要因素。基于隐藏马尔科夫模型的技术<br>（HMM） 可能是检测检测威胁最流行的。然而<br>HMM 需要对模型和计算资源进行广泛的培训。也<br>HMM在使用鲍姆时，有融合到本地最佳的缺点：<br>参数估计的韦尔奇算法。为了优化 HMM 参数，可以使用全球搜索技术。这项工作提出了遗传算法<br>（GA）用于优化HM参数。威胁检测的另一个与众不同之处是<br>攻击的动态性质。几个新的威胁正在出现，许多变种<br>这些攻击是由现有攻击创建的，使威胁建模成为一项艰巨的任务。<br>因此，良好的功能对于模拟 trafc 并提供高效的方式至关重要<br>检测已知和可能未知的攻击以检测。实现更好的功能<br>从网络trafc提取，我们提出了一个动态滑动窗口W其中<br>宽度为W。提议的多重 HMM 在检测威胁方面表现良好。模拟结果与基于 Baum-Welch 算法的方法获得的结果进行比较，该方法显示更高的精度和收敛性。

Threat problem has become more complex in the industrial environment due to the need to secure a large number of devices from attack while maintaining system reliability and real-time response to threats. In such scenario detection of threat in Industrial Internet of things (IIoT) devices becomes an important factor to avoid injection by malicious IIoT devices. The techniques based on the Hidden Markov Models (HMM) are probably the most popular in detecting threat of detection. However, HMM requires extensive training of the models and computational resources. Also, HMM has the drawback of convergence to a local optimum while using Baum–Welch algorithm for parameter estimation. In order to optimize the HMM parameters, global search techniques can be used. This work proposes Genetic algorithms (GA) for optimizing HMM parameters. The other difculty in threat detection is the dynamic nature of the attack. Several new threats are emerging with many variants which are created from existing attacks, making threat modeling an arduous task. As a result, good features are critical to model trafc and provide an efcient way to detect known and possibly unknown attacks to detect. To achieve a better feature extraction from the network trafc, we propose a dynamic sliding window W which has a width of w. The proposed multiple-HMM performs well to detect threats. The simulation results are compared to the results obtained by the Baum–Welch algorithm based approach showing higher accuracy and convergences.<br>