Deep learning model for distributed denial of service (DDoS) detection

Distributed denial of service (DDoS) attacks is one of the serious threats in the domain of cybersecurity where it affects the availability of online services by disrupting access to its legitimate users. The consequences of such attacks could be millions of dollars in worth since all of the online services are relying on high availability. The magnitude of DDoS attacks is ever increasing as attackers are smart enough to innovate their attacking strategies to expose vulnerabilities in the intrusion detection models or mitigation mechanisms. The history of DDoS attacks reflects that network and transport layers of the OSI model were the initial target of the attackers, but the recent history from the cybersecurity domain proves that the attacking momentum has shifted toward the application layer of the OSI model which presents a high degree of difficulty distinguishing the attack and benign traffics that make the combat against application-layer DDoS attack a sophisticated task. Striding for high accuracy with high DDoS classification recall is key for any DDoS detection mechanism to keep the reliability and trustworthiness of such a system. In this paper, a deep learning approach for application-layer DDoS detection is proposed by using an autoencoder to perform the feature selection and Deep neural networks to perform the attack classification. A popular benchmark dataset CIC DoS 2017 is selected by extracting the most appealing features from the packet flows. The proposed model has achieved an accuracy of 99.83% with a detection rate of 99.84% while maintaining the false-negative rate of 0.17%, which has the heights accuracy rate among the literature reviewed so far.

Evaluation System of Smart Logistics Comprehensive Management Based on Hospital Data Fusion Technology

With the acceleration of the informatization process, but because of the late start of the informatization construction of logistics management, the current digital system construction of logistics management has not been popularized, and the intelligent logistics integrated management evaluation system is also extremely lacking. In order to solve the lack of existing intelligent logistics comprehensive management evaluation system, this paper introduces the research of intelligent logistics comprehensive management evaluation system based on hospital data fusion technology. This paper analyzes and utilizes the Kalman filter and adaptive weighted data fusion technology in data fusion technology and then analyzes the evaluation index and system design principles of the intelligent logistics comprehensive management evaluation system and then designs the application layer from the application layer. Design the application layer from the application layer. Then design the framework of the intelligent logistics comprehensive management evaluation system at the network layer and the data layer. The system is finally tested, and the test results show that the evaluation accuracy of the system reaches 80%.

Research on Sports Training Action Image Recognition Based on SDN

Current image recognition methods cannot combine the transmission of image data with the interaction of image features, so the steps of image recognition are too independent, and the traditional methods take longer time and cannot complete the image denoising. Therefore, a recognition method of sports training action image based on software defined network (SDN) architecture is proposed. The SDN architecture is used to integrate the image data transmission and interactive process and to optimize the image processing centralization. The network architecture is composed of application layer, control layer, and infrastructure layer. Based on this, the dimension of image sample set is reduced, and the edge detection operator in any direction is constructed. The image edge filter is realized by calculating the response and threshold of image edge by using lag threshold and nonmaximum suppression (NMS). The Hough transform algorithm is improved to optimize the detection range. Extracting the neighborhood feature of sports training action, the recognition of sports training action image based on SDN architecture is completed. Simulation results show that the proposed method takes less time and the image denoising effect is better. In addition, the F1 test results of the proposed method are higher than those of the literature, and the convergence is better. Therefore, the performance of the proposed method is better.

Research on MVB conformance test system

The design and implementation of the MVB conformance test system is of great significance in both professional theory and practical application. Conformance test for MVB, mainly to determine whether the MVB equipment IUT is consistent with the MVB protocol standard requirements in the TCN standard. The conformance test of MVB equipment IUT covers most of the contents of the RTP real-time protocol such as the physical layer, link layer, network layer, transport layer and application layer. This subject will analyse and study the consistency test of the MVB physical layer.

Design of Smart Grid Test Bed Using OPNET and PLC

In this design unit, a design to test the performances of varying models was developed for the simulations in the PLC-base data link layer. The design includes a smart home and a Smart Grid environment where a comparison between Zigbee and WiMax-based models can be performed. The Smart Grid Test Bed has been designed using OPNET and Power Line Communication is proposed in this book. It is being designed to allow test bed experiments in four layers among OSI 7 layers. This chapter is organized as follows: The Physical Layer and Datalink Layer for Smart Grid Test Bed in Section 1; the Transport Layer for Smart Grid Test Bed in Section 2; and finally, Application Layer for Smart Grid Test Bed in Section.

Implementation of Smart Grid Test Bed Using OPNET and PLC

In this chapter, a design that allows testing of the performances of various models was developed with OPNET for the simulations in the PLC-base data link layer. As the model proposed earlier, the design includes a smart home and a Smart Grid environment where a comparison between Zigbee and WiMax-based models can be performed. The Smart Grid Test Bed has been implemented using OPNET and Power Line Communication is proposed in this book. It is being designed to allow Test Bed experiments in four layers among seven OSI layers. This chapter is organized as follows: the physical layer and datalink layer for Smart Grid Test Bed in Section 1; the transport layer for Smart Grid Test Bed in Section 2; and finally, application layer for Smart Grid Test Bed in Section 3.

An XGB-Based Reliable Transmission Method in the mMTC Scenarios

Massive machine-type communications (mMTCs) for Internet of things are being developed thanks to the fifth-generation (5G) wireless systems. Narrowband Internet of things (NB-IoT) is an important communication technology for machine-type communications. It supports many different protocols for communication. The reliability and performance of application layer communication protocols are greatly affected by the retransmission time-out (RTO) algorithm. In order to improve the reliability and performance of machine-type communications, this study proposes a novel RTO algorithm UDP-XGB based on the user datagram protocol (UDP) and NB-IoT. It combines traditional algorithms with machine learning. The simulation results show that real round-trip time (RTT) is close to the RTO, which is obtained by this algorithm, and the reliability and performance of machine-type communications have improved.

Research on Design and Application of National Fitness System

In order to improve the physical quality of the national people, a national fitness system is designed and applied to practice. Design the overall architecture of the national fitness system, including the perception layer, network layer, and application layer. The perception layer mainly uses Internet of Things gateway, central machine, wireless perception node, and fitness data dashboard to obtain fitness data. The network layer mainly uses WiFi, 4G, Ethernet, and other public networks to transmit fitness data, fitness guidance data, and equipment operation and maintenance data. The application layer provides data storage, device management, user management, and client services. On this basis, through the collection of users’ fitness data rating data, the data are transformed into fitness data rating matrix, and the matrix is analyzed and calculated to realize the intelligent recommendation of fitness data and complete the design of national fitness data recommendation algorithm. The test results show that the system can meet the requirements of normal use, good compatibility, and user score is high and has high practical application value.

Sports Parameter Acquisition Based on Internet of Things and Wavelet Analysis

In this study, a sports parameter acquisition model based on the internet of things and wavelet analysis is studied to improve the accuracy and timeliness of human sports parameter acquisition. A motion parameter acquisition model including a sensing layer, transmission layer, and application layer is designed. The acceleration sensor and temperature sensor in the information acquisition node in the sensing layer are used to collect the motion parameter data, which are uploaded to the application layer by the network in the transmission layer. The received data are denoised by the wavelet analysis method through the data processing unit in this layer and then sent to the ZigBee coordinator for coordination. The results show that the model can achieve the effective acquisition of different sports parameters of different moving objects and analyze the actual movement of moving objects according to the acquisition results. In the acquisition process, the signal burr can be effectively removed, the signal noise can be reduced, the high signal-to-noise ratio signal can be output, and the accuracy of acquisition is improved. It has high timeliness, stable performance, and strong practical application, which can provide an effective guarantee for users to monitor sports parameter data in real time.

Cloud Computing Image Recognition System Assists the Construction of the Internet of Things Model of Administrative Management Event Parameters

In order to successfully apply the Internet of Things and cloud computing to the administrative management of spatial structures and realize the systematization, digitization, and intelligence of administrative management, this article draws on research experience in related fields and considers the data characteristics and computing tasks of administrative management. The whole cycle of transmission, storage, postprocessing, and visualization is the main line of research, and a cloud computing-based spatial structure administrative management IoT system is constructed. First, by summarizing the application status of the Internet of Things, the general Internet of Things system is summarized into three levels, and combined with the specific work in the spatial structure administrative management, the overall framework of the spatial structure administrative management of the Internet of Things system is proposed, and the functional sublayers are carried out. Secondly, in response to the above problems, through the traditional image recognition system research and practical application investigation, in order to meet the user’s requirements for the computing efficiency and recognition accuracy of the image recognition system, an image recognition system in the cloud computing environment is proposed. It proposes a fuzzy evaluation algorithm of health grade hierarchy analysis optimized for the index system and scoring system and a calculation method that uses time series to identify regular outliers. The optical image pixel-level fusion method and the infrared and visible image fusion method based on complementary information are proposed, and the image fusion software is developed. Finally, in order to enable the application layer to use cluster resources to efficiently and intelligently process massive monitoring data containing redundancy, heterogeneity, anomalies, and many other defects, according to the calculation process of each specific task of data preprocessing and postprocessing in the application layer, demonstrations are made one by one. After analysis, it is concluded that vertical storage of data blocks according to different sensor channels is the optimal strategy.