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.

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.

To improve the safety precaution level of network system, a combined network intrusion detection method is proposed based on Supervised Locality Preserving Projections (SLPP) feature extraction and Extreme Learning Machine (ELM). In this method, the feature extraction capability of SLPP is first used to reduce the dimensionality of the original network connection and system audit data, and get a feature set, then, based on this, the advantages of ELM in pattern recognition is adopted to build a network intrusion detection model for detecting and determining intrusion behavior. Simulation results show that, under the same experiment conditions, compared with traditional neural networks and support vector machines, the proposed method has more advantages in training efficiency and generalization performance.

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.

Vehicular Ad Hoc Networks (VANETs) have a strategic goal to achieve service delivery in roads and smart cities, considering the integration and communication between vehicles, sensors and fixed road-side components (routers, gateways and services). VANETs have singular characteristics such as fast mobile nodes, self-organization, distributed network and frequently changing topology. Despite the recent evolution of VANETs, security, data integrity and users privacy information are major concerns, since attacks prevention is still open issue. One of the most dangerous attacks in VANETs is the Sybil, which forges false identities in the network to disrupt compromise the communication between the network nodes. Sybil attacks affect the service delivery related to road safety, traffic congestion, multimedia entertainment and others. Thus, VANETs claim for security mechanism to prevent Sybil attacks. Within this context, this paper proposes a mechanism, called SyDVELM, to detect Sybil attacks in VANETs based on artificial intelligence techniques. The SyDVELM mechanism uses Extreme Learning Machine (ELM) with occasional features of vehicular nodes, minimizing the identification time, maximizing the detection accuracy and improving the scalability. The results suggest that the suitability of SyDVELM mechanism to mitigate Sybil attacks and to maintain the service delivery in VANETs.

To leverage the feature extraction of DBN and the fast classification and good generalization of ELM, an improved method of DBN-ELM is proposed for intrusion detection. The improved model uses deep belief network (DBN) to train NSL-KDD dataset and feed them back to the extreme learning machine (ELM) for classification. A classifier is connected at each intermediate level of the DBN-ELM. By majority voting on the output of classifier and ELM, the final output is calculated by integration. Experiments show that the improved model increases the classification confidence and accuracy of the classifier. The model has been benchmarked on the NSL-KDD dataset, and the accuracy of the model has been improved to 97.82%, while the false alarm rate has been reduced to 1.81%. Proposed improved model has been also compared with DBN, ELM, DBN-ELM and achieves competitive accuracy.

This paper investigates the temperature demagnetization modeling method for permanent magnets (PM) in permanent magnet synchronous linear motor (PMSLM). First, the PM characteristics are presented, and finite element analysis (FEA) is conducted to show the magnetic distribution under different temperatures. Second, demagnetization degrees and remanence of the five PMs' experiment sample are actually measured in stove at temperatures varying from room temperature to 300 °C, and to obtain the real data for next-step modeling. Third, machine learning algorithm called extreme learning machine (ELM) is introduced to map the nonlinear relationships between temperature and demagnetization characteristics of PM and build the demagnetization models. Finally, comparison experiments between linear modeling method, polynomial modeling method, and ELM can certify the effectiveness and advancement of this proposed method.