Biblio

Intrusion detection method cautions and through build recognition rate. Through determine worries forth execution support vector machine (SVM), multilayer perceptron and different procedures have endured utilized trig ongoing work. Such strategies show impediments & persist not effective considering use trig enormous informational indexes, considering example, outline & system information. Interruption recognition outline utilized trig examining colossal traffic information; consequently, a proficient grouping strategy important through beat issue. Aforementioned issue considered trig aforementioned paper. Notable AI methods, specifically, SVM, arbitrary backwoods, & extreme learning machine (ELM) persist applied. These procedures persist notable trig view epithetical their capacity trig characterization. NSL-information revelation & knowledge mining informational collection components. Outcomes demonstrate a certain ELM beats different methodologies.

Supervisory control and data acquisition (SCADA) systems are used with monitoring and control purposes for the process not to fail in industrial control systems. Today, the increase in the use of standard protocols, hardware, and software in the SCADA systems that can connect to the internet and institutional networks causes these systems to become a target for more cyber-attacks. Intrusion detection systems are used to reduce or minimize cyber-attack threats. The use of deep learning-based intrusion detection systems also increases in parallel with the increase in the amount of data in the SCADA systems. The unsupervised feature learning present in the deep learning approaches enables the learning of important features within the large datasets. The features learned in an unsupervised way by using deep learning techniques are used in order to classify the data as normal or abnormal. Architectures such as convolutional neural network (CNN), Autoencoder (AE), deep belief network (DBN), and long short-term memory network (LSTM) are used to learn the features of SCADA data. These architectures use softmax function, extreme learning machine (ELM), deep belief networks, and multilayer perceptron (MLP) in the classification process. In this study, anomaly-based intrusion detection systems consisting of convolutional neural network, autoencoder, deep belief network, long short-term memory network, or various combinations of these methods on the SCADA networks in the literature were analyzed and the positive and negative aspects of these approaches were explained through their attack detection performances.

Polymerization process is a process in the production of polyester fiber, and its reaction parameter intrinsic viscosity has an important influence on the properties of the final polyester fiber. In this paper, a feature selection extreme learning machine model based on binary encoding Atom Search Optimization algorithm is proposed and applied to the polymerization process of polyester fiber production. Firstly, the distance measure of K-NearestNeighbor algorithm, combined with binary coding, and Atom Search Optimization algorithm are used to select features of industrial data to obtain the optimal data set. According to the data set, atom search optimization algorithm is used to optimize the weight and threshold of extreme learning machine and the activation function of the improved extreme learning machine. A prediction model with root mean square error as fitness function was established and applied to polyester production process. The simulation results show that the model has good prediction accuracy, which can be used for reference in the follow-up industrial production.

The age of the wireless network already advances to the fifth generation (5G) era. With software-defined networking (SDN) and network function virtualization (NFV), various scenarios can be implemented in the 5G network. Cloud computing, for example, is one of the important application scenarios for implementing SDN/NFV solutions. The emerging container technologies, such as Docker, can provide more agile service provisioning than virtual machines can do in cloud environments. It is a trend that virtual network functions (VNFs) tend to be deployed in the form of containers. The services provided by clouds can be formed by service function chaining (SFC) consisting of containerized VNFs. Nevertheless, the challenges and limitation regarding SFCs are reported in the literature. Various network services are bound to rely heavily on these novel technologies, however, the development of related technologies often emphasizes functions and ignores security issues. One noticeable issue is the SFC integrity. In brief, SFC integrity concerns whether the paths that traffic flows really pass by and the ones of service chains that are predefined are consistent. In order to examine SFC integrity in the cloud-native environment of 5G network, we propose a framework that can be integrated with NFV management and orchestration (MANO) in this work. The core of this framework is the anomaly detection mechanism for SFC integrity. The learning algorithm of our mechanism is based on extreme learning machine (ELM). The proposed mechanism is evaluated by its performance such as the accuracy of our ELM model. This paper concludes with discussions and future research work.