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With the development of IoT technology and the improvement of people's living standards, smart locks have become prevalent in ordinary households. However, for enterprises and institutions requiring unified management and control capabilities for networked smart locks, the technology has been relatively lagging, and there is a lack of established brands. Existing products are mostly based on wireless low-power network protocols, leading to common issues of low stability, limited network scalability, and high maintenance efforts. This paper addresses the problems existing in current networked lock systems and leverages the centralized office characteristics of enterprises and institutions. By integrating IoT technology and Power over Ethernet- (PoE-) centralized power supply, we have developed a networked smart lock system with unified management and control capabilities. This system is primarily designed for applications in offices, collective dormitories, and hotels of enterprises, institutions, and schools. The test results demonstrate that even with a scale of over tens of thousands of networked locks under unified control, the system maintains fast response times and low packet loss rates. It also incorporates energy-saving and environmentally friendly features, as well as easy automatic updates, facilitating convenient postdeployment maintenance. Thus, it effectively meets the requirements for centralized management and control in different application scenarios with centralized use of rooms. In conclusion, this research has led to the development of a unified management and control networked smart lock system, which is well suited for enterprises, institutions, and schools. Its scalability, speed, energy efficiency, and ease of maintenance make it an excellent solution for various application scenarios requiring centralized management and control of access. [ABSTRACT FROM AUTHOR]

In order to solve the problem that there are a large number of unknown protocols on the network, which affect the network management and network security in varying degrees, an unknown binary protocol identification method is proposed. On the premise that the center cluster of unknown protocols is obtained by the clustering algorithm, the unknown protocols for network traffic are identified by combining one-class of classification with one-dimensional CNN classification technology. At first, a class of classification algorithm is used to select the unknown protocols, and then, the labeled protocol data obtained by clustering are used to train the one-dimensional CNN model, and the classified binary protocol packets are directly used as the input of the one-dimensional convolution neural network. After the classification of the CNN model, the unknown protocols are finally identified. The experimental results show that the proposed classification and recognition method is better than the traditional CNN and SVM algorithms, and the maximum frequency pooling is better than the traditional pooling method. [ABSTRACT FROM AUTHOR]

Cloud computing is a vast revolution in information technology (IT) that inhibits scalable and virtualized sources to end users with low infrastructure cost and maintenance. They also have much flexibility and these resources are supervised by various management organizations and provided over the Internet by known standards, formats, and networking protocols. Legacy protocols and underlying technologies consist of vulnerabilities and bugs which open doors for intrusion by network attackers. Attacks as distributed denial of service (DDoS) are one of most frequent attacks, which impose heavy damage and affect performance of the cloud. In this research work, DDoS attack detection is easily identified in an optimized way through a novel algorithm, namely, the proposed gradient hybrid leader optimization (GHLBO) algorithm. This optimized algorithm is responsible to train a deep stacked autoencoder (DSA) that detects the attack in an efficient manner. Here, fusion of features is carried out by deep maxout network (DMN) with an overlap coefficient, and augmentation of data is carried out by the oversampling process. Furthermore, the proposed GHLBO is generated by integrating the gradient descent and hybrid leader-based optimization (HLBO) algorithm. Also, this proposed method is assessed by various performance metrics, such as the true positive rate (TPR), true negative rate (TNR), and testing accuracy with values attained as 0.909, 0.909, and 0.917, accordingly. [ABSTRACT FROM AUTHOR]

Matrix multiplication has been implemented in various programming languages, and improved performance has been reported in many articles under various settings. Matrix multiplication is of paramount interest to machine learning, a lightweight matrix-based key management protocol for IoT networks, animation, and so on. There has always been a need and an interest for improved performance in terms of algorithm implementation. In this work, the authors compared the run times of matrix multiplication in popular languages such as C++, Java, and Python. This analysis showed that Python's implementation was poor while Java was relatively slower compared to the C++ implementation. All the aforementioned languages use a row-major scheme, and hence, there are many cache misses encountered when implemented through simple looping. In contrast, the authors show that by changing the loop order, more performance gains are possible. Moreover, we evaluated the performance of matrix multiplication by comparing the execution time under various loop settings. The authors observed tremendous performance gains due to better spatial locality. In addition, the authors also implemented a parallel version of the same algorithm using OpenMP with eight logical cores and achieved a speed-up of seven times compared to the serial implementation. [ABSTRACT FROM AUTHOR]

In recent years, research has combined the connection of agricultural equipment to increase crop growth rates and lower planting costs by refining the entire planting process. IT-enabled agriculture has beneficial effects on this industry and is yet a source of debate in academic circles. Trending network technologies like WSN and IoT have never been easy to develop and use in agriculture. The growth rate was not increased using outdated, conventional methods and technologies. Additionally, the rapid population expansion cannot meet human demands and expectations. Survey Methodology. The existence of IoT in agriculture was investigated and reported in this review. The paper describes the different IoT-agriculture network protocols. This study clarifies how the Internet affects agriculture and its underlying mechanisms. It also discusses how the growth rate is boosted when both sectors work together. This study intends to explore a platform that offers an infrastructure to link devices using the network protocol used in agriculture. In this study, several contemporary network difficulties relating to agriculture are also covered. Conclusion. The results of this study can be used as a guide for creating particular network protocols for the agriculture industry. [ABSTRACT FROM AUTHOR]

Network communication protocol reverse engineering is useful for network security, including protocol fuzz testing, botnet command infiltration, and service script generation. Many models have been proposed to generate field boundary, field semantic, state machine, and some other format information from network trace and program execution for text-based protocol and hybrid protocols. However, how to extract format information from network trace data for binary-based protocol still remains a challenging issue. Existing network-trace-based models focus on text-based and hybrid protocols, using tokenization and some other heuristic rules, like field identification, to perform reverse engineering, which makes it hard to apply to binary-based protocol. In this paper, we propose a whole mechanism for binary-based protocol reverse engineering based on auto-encoder models and other clustering algorithms using only network trace data. After evaluation, we set some metrics and compare our model with existing other models, showing its necessity to the field of protocol reverse engineering. [ABSTRACT FROM AUTHOR]

In order to apply wireless sensor networks to mechanical vibration monitoring, the author proposes a wireless network topology with multiple data collection points for mechanical vibration monitoring. This structure reduces the transmission load of the data collection point, increases the data transmission rate of the network, balances the energy dissipation in the network, and utilizes the general wireless sensor network hardware platform. The network transmission protocol and related auxiliary mechanisms are designed and implemented, and a wireless vibration monitoring test platform is constructed. The transmission performance of the network structure with multiple data collection points is evaluated through the actual test. The experimental results show that by using the wireless sensor network topology with multiple data collection points, it can meet the requirements of continuous transmission of vibration data obtained by 1 kHz sampling. Conclusion. The system performance of the wireless sensor network based on this network structure has been improved under the condition of general hardware, and the network structure of multiple data collection points shows good performance in the process of high-speed data transmission. [ABSTRACT FROM AUTHOR]

Background. Hypertension, as a high risk factor of cardiovascular disease, has led to a significant upward trend in the population and incidence of the disease. Hypertension patients need to take antihypertensive drugs for life, and therefore people gradually pay more attention to the adverse reactions of antihypertensive drugs. This study protocol outlines a plan to assess the adverse reaction of the different antihypertensive drugs and acupuncture in order for clinical application. Objective. To compare the side effects of different antihypertensive drugs and acupuncture in the treatment of hypertension. Methods and analysis. We will search the databases containing CNKI, Wan-Fang database, Chinese Scientific Journal Database(VIP), PubMed, Cochrane, and Embase, and randomized controlled trials (RCTs) of commonly used antihypertensive drugs or acupuncture for primary hypertension will be obtained. Then, Stata14.0 and Gemtc will be used to assess the statistics. Ethics and dissemination. Since no personal patient consent will be required in the study, there is no ethical approval. The results of this reporting will be submitted to a peer-reviewed publication. PROSPERO registration number: CRD42020152703. [ABSTRACT FROM AUTHOR]

Link quality prediction is a fundamental component of the wireless network protocols and is essential for routing protocols in wireless sensor networks (WSNs). Effective link quality prediction can select high-quality links for communication and improve the reliability of data transmission. In order to improve the accuracy of the link quality prediction model and reduce the model complexity, the link quality prediction model based on the light gradient boosting machine (LightGBM-LQP) is proposed in this paper. Specifically, agglomerative hierarchical clustering and manual division are combined to grade the link quality and obtain the labels of samples. Then, light gradient boosting machine (LightGBM) classification algorithm and Focal Loss are used to estimate the link quality grades. In order to reduce the impact of data imbalance, Borderline-SMOTE is employed to oversample the minority link quality samples. Finally, LightGBM-LQP predicts link quality grade at the next moment with historical link quality information. The experimental results on data collected from a real-world WSNs show that the proposed model has better prediction accuracy and shorter predicting time compared to related models. [ABSTRACT FROM AUTHOR]

The main challenge of the MANET network is the continuous management of information, each device, to ensure the correct direction of traffic. MANET consists of a peer-to-peer connection; the network is self-repairing and at the same time self-formed. The network can be used in the field of road safety, in sensors in the home, healthcare, robotics, etc. The aim of this work was to study the MANET network and its protocols and especially the AODV protocol, the choice of peer in the AODV protocol, and the subsequent improvement of this mechanism. The work deals with ETX metrics and its subsequent implementation in the AODV protocol. The work includes simulations of the AODV protocol and subsequently also simulations of the AODV-ETX protocol. The implementation of the proposed result simulation takes place in Network Simulator 3 tool. The work contains a comparison of the AODV and AODV-ETX protocols. The simulation results are shown in tables and plotted. The AODV-ETX has better properties compared to the AODV protocol, but with a larger number of nodes over 20, these properties approach the AODV protocol. The packet loss, packet delay, and permeability are considered for performance evaluations. [ABSTRACT FROM AUTHOR]

With the development of society, humans are becoming more and more dependent on the Internet. And there are exploitable vulnerabilities in network sharing protocol vulnerabilities that will cause great risks to individuals and society. Therefore, vulnerability mining technology has developed into an important research problem in the field of information security. To this end, this paper uses fuzzy testing method for vulnerability mining of network protocols. The fuzzy testing technique performs vulnerability mining by sending a large amount of abnormal data to the test target and monitoring whether the software system is working properly. The vulnerability mining approach in this paper prioritizes the need to analyze and model the protocol format and generate a large number of test cases by using fuzzy test values to vary the boundaries of different parts of the protocol. These test cases are then sent to the test target, and the network state and process state of the test target are monitored in real time. Finally, if the test cases trigger a vulnerability, the system automatically records the test case information as well as the vulnerability information. The test cases evaluated by machine learning as easy to trigger vulnerabilities are sent to the test H-target, which can save the time of vulnerability mining for everyone and improve the efficiency of vulnerability mining. The vulnerability mining technology researched in this paper is of great significance to network security, which can prevent problems before they occur, discover vulnerabilities in the network in time, take effective measures to prevent them, and possibly avoid the spread of some major network vulnerability viruses. [ABSTRACT FROM AUTHOR]

The current sports feedback system has high application value and is widely used in physical education courses, but it has problems such as large feedback error, long response time, and incomplete data, leading to poor effect of sports feedback system. In order to improve the effectiveness of sports feedback system, a sports feedback system based on video digital processing is designed. By constructing the overall architecture of video digital sports feedback system, the positioning module, interface module, power supply circuit module, sampling module, input module, signal extraction module, and video digital processing module are designed. Using information communication mechanism, TCP/IP network protocol, serial communication, and shared memory to improve video digital processing, experimental results show that the designed system has good error convergence of 0.1, short response time of 32 s, and high data integrity of 94%, which has strong practicability. [ABSTRACT FROM AUTHOR]

The minimal-degree distributed denial-of-service attack takes advantage of flaws in the adaptive mechanisms of network protocols, which could have a big impact on network service quality. It is very hard to find, has a low attack rate, and comes at a set time. Detection methods that have been used before have problems because they only use one type of detection and are not very good at identifying the object. In the end, a way to detect many sorts of minimal DDoS assaults that use deep hybrid learning is suggested. To construct multi-type limited DDoS threat data sets and mimic diverse sorts DDoS assaults and legitimate traffic in varying situations in the 5G setting, collect congestion at the networking entry and extract flow feature info are considered. From a statistical threshold and feature engineering point of view, these data sets show how many sorts of minimal DDoS assaults are there. This study aims to develop a deep hybrid learning-based multi-type low-rate DDoS attack detection solution for 5G networks which is the novel model that is recently deployed, and a hybrid deep learning algorithm was used to train the algorithm offline, and the algorithm's performance was compared to that of the LSTM-Light GBM and LSTM-RF algorithms. The CNN-RF revealing model was then used to detect minimal DDoS assaults at the gateway, so that multiple attacks could be detected at the same time. It can identify 4 sorts of low-rate DDoS assaults like Slow-Headers, Slow-Body, Slow-Read, and Shrew assaults, in a 120-second window. The false intercept rate is 11.03 percent. This means that 96.22 percent of traffic could be found. Using the strategy suggested can help cut down on the traffic concentration of minimal DDoS attacks at the net ingress. It can also be used in real-world situations. [ABSTRACT FROM AUTHOR]

Wireless sensor network (WSN) is an emerging technology used in emergency scenarios. There are a number of possible threats to WSNs because they use unsupervised IP addresses. Securing networks with unattended sensors is a real challenge nowadays. Sensor nodes lack power and storage, making them incompatible with normal security checks. It will be vital to make advancements in sensor network architecture and protocol design. There will be more vulnerability to attack if there is a lack of security. Especially, one key attack is node replication which induces the sensor node to acts as an original node, collecting data from the network and sending it to the attacker. In dynamic WSN, detecting an assault is difficult to find replica nodes. Therefore, this paper proposes a Strategic Security System (SSS) to discover replica nodes in static and dynamic distributed WSNs. It is mainly focused on enhancing detection accuracy, time delay, and communication overhead. The present system includes Single Stage Memory Random Walk with Network Division (SSRWND) and a Random-walk-based approach to detect clone attacks (RAWL). The proposed system has less memory and better detection accuracy. [ABSTRACT FROM AUTHOR]

In order to enhance the ability of wireless sensor networks to resist various security threats and reduce the limitations caused by the characteristics of wireless sensor networks and sensor nodes, this paper proposes a secure routing protocol for wireless sensor networks based on trust management. Combined with the relevant parameters of wireless sensor network, the simulation experiment is carried out with MATLAB. Aiming at the trust management part of the wireless sensor network security protocol proposed in this paper, the malicious attack environment such as sensor node attributes is simulated to verify the resistance of this model to relevant malicious attacks. For the trust management-based wireless sensor network security routing protocol proposed in this paper, the model included in the protocol is compared to the existing security routing model, combining the characteristics of average simulation transmission, network life, and average routing update time. Experiments show that the model has better routing performance and has improved by an average of about 20%. We offer a new solution to solve the problem of wireless routing security. [ABSTRACT FROM AUTHOR]

A large amount of data would be generated during the process of microseismic monitoring, and data transmission with the cable has some shortcomings such as poor anti-interference, time-consuming, laborious, high cost, and low construction efficiency. On the other hand, the influence of signal diffraction and the multipath effect will greatly cut down the energy of the signal during the wireless transmission in the complex mountainous area. The wireless transmission system based on Wi-Fi is designed in this paper, which includes the base station and data transceiver. The maximum distance and transmission rate at point-to-point communication of the base station are up to 1000 m and 150 Mbps, respectively, and it provides the communication backbone line for the network. The data transceiver adopts the network protocol based on Ad Hoc, and the transmission distance is up to 200 m at the condition of complex topography and lush mountains. We have applied the system to in-site microseismic monitoring with 28 geophones of the coalbed methane fracturing in South Chongqing. It achieved a good performance with a transmission rate of 1.2 Mbps, a time delay of 1.95 ms, and a signal strength of up to −52.3 dBm for real-time data transmission in the field. The results show that the system has the advantages of low BER, fast transmission speed, long communication distance, and stable and safety hardware and has a great value for more applications of microseismicity in complex topography. [ABSTRACT FROM AUTHOR]

Small and medium-sized enterprises (SMEs) are an indispensable part of the development of the market economy, and they occupy a major position in the national economic system. Nowadays, the information construction of SMEs is becoming more and more important. Having an informationized accounting system can speed up the economic development of SMEs. So, this article designs a new type of accounting system, mainly for SMEs. Therefore, this article is based on sensor monitoring and cloud computing to optimize the informatization construction of the accounting system of SMEs. This paper proposes a cloud computing SOA architecture to build a cloud computing-based accounting system, and then combines the wireless sensor network routing protocol in the wireless sensor network system and the method of measuring the distance of the sensor monitoring node, and the wireless sensor network is applied to the cloud computing-based accounting system. Then, designed the enterprise information construction investigation experiment to formulate the rules applicable to SMEs, and then tested the data detection ability of the new accounting system by testing the performance of the sensor network protocol. Finally, the data obtained from the analysis of the weight of the enterprise cloud service is used to optimize the new accounting system, and the performance of the final optimized accounting system is compared with the traditional system. Experiments show that the accuracy of data monitoring by an accounting system based on sensor monitoring and cloud computing has increased by 13.84% compared to traditional accounting systems; compared with the traditional accounting system, the data processing efficiency of the accounting system based on sensor monitoring and cloud computing has increased by 14.63%. [ABSTRACT FROM AUTHOR]

The next generation of mobile networks and communications (5G networks) has a very strong ability to compute, store, and so on. Group-oriented applications demonstrate their potential ability in resource-constrained information systems (RISs) towards 5G. The security issues in RIS towards 5G have attracted great attention. For example, how to conduct fair and orderly multiparty communication in an intelligent transportation system (ITS). One of the main challenges for secure group-oriented applications in RIS towards 5G is how to manage RIS communications fairly in multiparty applications. In other words, when the users cannot transmit their messages simultaneously, the order of their communication can cause security concerns in multiparty applications. A feasible solution to the problem is for the group of users to follow a specific order to transmit their messages. Otherwise, some users may take advantage over other users if there has no agreeable order to be followed. In this paper, we propose a novel cryptographic primitive, called multiparty drawing-straw (MDS) protocol, which can be used by a group of users to determine the order of the group to participate in the multiparty applications. Our scheme is based on Pedersen's verifiable secret sharing (VSS), which is a well-known scheme. Our proposed protocol is fair since the output is uniformly distributed, and this is an attractive feature for secure multiparty applications in RIS towards 5G. [ABSTRACT FROM AUTHOR]

The widely used network protocols play a crucial role in various systems. However, the protocol vulnerabilities caused by the design of the network protocol or its implementation by programmers lead to multiple security incidents and substantial losses. Hence, it is important to study the protocol fuzzing in order to ensure its correctness. However, the challenges of protocol fuzzing are the mutation of protocol messages and the deep interactivity of the protocol implementation. This paper proposes a model-based grey-box fuzzing approach for protocol implementations, including the server-side and client-side. The proposed method is divided into two phases: automata learning based on the minimally adequate teacher (MAT) framework and grey-box fuzzing guided by the learned model and code coverage. The StateFuzzer tool used for evaluation is presented to demonstrate the validity and feasibility of the proposed approach. The server-side fuzzing can achieve similar or higher code coverage and vulnerability discovery capability than those of AFLNET and StateAFL. Considering the client, the results show that it achieves 1.5X branch coverage (on average) compared with the default AFL, and 1.3X branch coverage compared with AFLNET and StateAFL, using the typical implementations such as OpenSSL, LibreSSL, and Live555. The StateFuzzer identifies a new memory corruption bug in Live555 (2021-08-25) and 14 distinct discrepancies based on differential testing. [ABSTRACT FROM AUTHOR]

Bluetooth low energy is an almost ubiquitous technology currently embedded in billions of power-constrained Internet of Things devices around the world. The Bluetooth mesh profile, released by the Bluetooth Special Interest Group in July 2017, allows Bluetooth low energy devices to form a mesh network, further enabling smart home and building applications where long-range connectivity is required. However, the current release of Bluetooth mesh profile still has power and deployment constraints that limit its applicability. To explore the viability of Bluetooth mesh profile in home/building automation applications, we built the Smart Doorbell: a proof-of-concept Bluetooth mesh profile–based visitor notification system for office spaces. The Smart Doorbell was implemented using a mesh network topology with nodes distributed across office building floors, serving as a real Internet of Things deployment and as a testbed for mesh network protocols. Similar Bluetooth mesh profile evaluations found in literature use mostly development kits and/or synthetic traffic in artificial settings; we contribute by using the Smart Doorbell, a system as close as possible to a minimum viable product, to evaluate power consumption and responsiveness as a proxy for product viability. This article presents the architecture of the Smart Doorbell, the viability evaluation results, and a direct comparison with FruityMesh, a competing Bluetooth low energy mesh network protocol. Overall, the fact that Bluetooth mesh profile devices can directly communicate with a user's mobile phone (using Bluetooth low energy) considerably eases deployment and provisioning. However, the use of flooding to forward messages across the mesh network increases power consumption, precluding the use of battery-powered nodes on the network's backbone and severely limiting the applicability of Bluetooth mesh profile in building automation. [ABSTRACT FROM AUTHOR]

This paper presents an in-depth study and analysis of the model of higher education using distributed hardware tracking intervention of information technology. The MEC-based dynamic adaptive video stream caching technology model is proposed. The model dynamically adjusts the bit rate by referring to the broadband estimation and cache occupancy data to ensure users have a smooth experience effect. Simulation results show that the model has fewer transcoding times and generates lower latency than the traditional model, which is suitable for dual-teacher classroom scenarios and further improves the quality of the user's video viewing experience. The model uses an edge cloud collaborative architecture to migrate the rendering technology to an edge server closer to the user side, enabling real-time interaction, computation, and rendering, reducing the time of data transmission as well as computation time. According to the blended learning-based adaptive intervention model, three rounds of teaching practice are conducted to validate the effectiveness of the intervention model in terms of both student process performance and outcome performance, thereby improving learning adaptability and improving learning effect. Teachers' teaching has a significant impact on learning motivation (β = 0.311 , p < 0.01), which in turn affects learning adaptability. Teachers use scientific teaching methods to stimulate students' learning motivation, mobilize enthusiasm, and improve learning adaptability. Under the communication topology of the system as a directed graph, a multi-intelligent system dynamic model with grouping is established; i.e., the intragroup intelligence has the same dynamics but is different between groups, and all system dynamics are unknown. The proposed novel policy iterative algorithm is used to learn the optimal control protocol and achieve optimal consistency control. The effectiveness of the algorithm is demonstrated by the simulation experimental results. The simulation results show that the model has lower latency and energy consumption compared to the cloud rendering model, which is suitable for the safety education classroom scenario and solves the outstanding problems of network connection rate and cloud service latency. [ABSTRACT FROM AUTHOR]

One of the important symbols of a country's level of social progress and the continuous spread of civilization throughout the world includes the level of national physique and health. People's living standards have been significantly improved, and a moderately prosperous society has been preliminarily realized. The national physique should be improved, especially the teenagers who are in the rapid and golden period of physical and psychological development. But not everything develops according to wishes. For the past 20 years, the physical health of Chinese students has been in a downward trend. Therefore, it is urgent to analyze and study the data of adolescent health monitoring and physical fitness test. Through the analysis of D-S evidence theory composition rules, SVM network protocol, and other technologies, the accuracy of adolescent physique monitoring data has been improved by 38.4%, enhanced students' willingness to exercise, 65% of students have enhanced physical health awareness, and a network data platform has been established, which can clearly reflect the physical health of students and summarize all monitoring data information. Teenagers are the future builders and successors of the country, and they play a pivotal role in the entire country. The analysis of the status quo of adolescent physical fitness test data is related to the strength of the country, the rise and fall of the nation, the happiness of the family, and the future of the individual. [ABSTRACT FROM AUTHOR]

Internet of things (IoT) and cloud computing are combined to form a cloud computing data center, and cloud computing provides virtualization, storage, computing, and other support services for IoT applications. Data is the foundation and core of cloud IoT platform applications, and massive multisource heterogeneous IoT data aggregation and storage have basic requirements such as real-time, security, and scalability. This paper focuses on the aggregation and storage methods of massive heterogeneous cloud IoT data, solving the multisource data aggregation problem caused by inconsistent protocols and the heterogeneous data storage problem caused by inconsistent data types. A heterogeneous network protocol adaptation and data aggregation method is proposed for the multisource data aggregation problem caused by protocol inconsistency. A protocol adaptation layer is set up in the IoT virtual gateway to achieve compatibility with multiple types of data aggregation protocols, ensuring adaptive access to different types of IoT nodes, and on this basis, data is transmitted to the cloud IoT platform through a unified interface, shielding the variability of IoT sensing devices. Given the problems of device forgery and malicious tampering in the data aggregation process, we propose a fast authentication and data storage method for IoT devices based on "API key" and implant the device authentication API key into the protocol adaptation layer of the virtual gateway to realize the source authentication of IoT nodes and ensure the authenticity of data. [ABSTRACT FROM AUTHOR]

Under the influence of the global epidemic, various businesses have moved online one after another. With the rise of emerging industries such as online medical treatment, online education, and online conference, the proportion of attacks in the network service industry has increased year by year. UDP-FLOOD is still the primary scenario of DDoS attacks. Among them, with a large number of attack resources and most of them are high configuration servers, NTP (Network Time Protocol) reflection has become the most common UDP reflection attack method, accounting for 59% of the overall distribution. Therefore, establishing an efficient NTP attack detection system is a very important content to prevent network malicious attacks. At present, NTP-attacking based defensed methods mainly include IP filtering, hop mapping, and response packet detection, but they all have obvious weaknesses. Among them, the IP detection scheme can only detect historical attack IP, the implementation of hop mapping scheme is complex, and the resource overhead of response packet detection scheme is too large. Therefore, this paper proposes a nonlinear detection algorithm based on AHP multidimensional matrix quad information entropy. Through simulation experiments, the change of quad information entropy of attack intensity from 10% to 100% is counted. The detection rate based on the traditional target IP and target port algorithm is only 50% and 60%, which is significantly lower than this algorithm. Experiments show that the detection rate of this algorithm is higher. [ABSTRACT FROM AUTHOR]

This paper analyzes the typical abnormal behaviors such as hidden and invisible attacks of network protocols and proposes a new method to perceive and mine the abnormal behaviors of protocols so as to evaluate the operational security of protocols. Aiming at the problems of long latency and difficulty to expose the hidden behavior of network protocols, a scheme combining dynamic stain analysis and instruction clustering analysis is proposed. The proposed method cannot only quickly distinguish the hidden behavior but also accurately grasp the hidden behavior instruction sequence. Experimentation results show that the proposed method can mine unknown protocols for abnormal behavior with high accuracy. [ABSTRACT FROM AUTHOR]

Based on the principle of cluster wireless sensor network, this article introduces typical routing protocols in wireless sensors, and wireless sensor network protocol in detail analyzes their advantages and disadvantages and addresses their shortcomings. First, in the clustering network, a uniform clustering protocol with multiple hops in the circular network is proposed. The circular network is divided into rings of equal width, and clusters of equal size are set on different rings. Secondly, the ordinary nodes on each layer of the ring send the collected data to the auxiliary intelligent nodes in the cluster in a single-hop manner, and the auxiliary intelligent nodes located on the outer ring transfer the data to the auxiliary intelligent nodes located on the adjacent inner ring. Finally, on the basis of studying the clustering network protocol, this paper proposes a new clustering routing algorithm, a multihop adaptive clustering routing algorithm. The simulation results show that the algorithm can effectively extend the life of the network, save network energy consumption, and achieve network load balance. At the same time, the initial energy of the auxiliary intelligent node is set according to the energy consumption of the ordinary node and the relative distance between the auxiliary intelligent node and the base station on each layer of the ring. The theoretical and simulation results prove that, compared with the clustered network and auxiliary intelligent nodes, the clustered network can extend the life of the network. [ABSTRACT FROM AUTHOR]

The reliability of the modelling system mainly depends on the simulation of underwater acoustic channel characteristics. The reliability of simulator is improved because of the use of Bellhop in NS-Miracle. World Ocean Simulation System (WOSS) can retrieve the data of marine environment by accessing the database of seabed depth, sound speed profile and seabed sediment, and transmit them to Bellhop simulator automatically, so that the model is closer to the actual underwater acoustic transmission channel than not using WOSS. In order to verify the reliability of NS2/NS-Miracle simulation system with WOSS, a centralized underwater sensor network with five nodes is simulated on the integrated simulation system. The characteristic empirical model, Bellhop Ray-Tracing model, and WOSS combined with Bellhop model are, respectively, adopted to simulate underwater acoustic channel. The results of three types of simulation, such as average throughput, average delay, and packet error rate, and simulation time are very close under the same condition. It proves that the accuracy of integrated simulation system is as excellent as that of NS-Miracle. However, WOSS can automatically acquire the actual sea environment parameters and provide them to the simulator, which can improve the authenticity of the simulation system. Furthermore, three MAC protocols, Aloha-CS, CSMA/CA, and DACAP, are simulated on the integrated simulation system under the same condition including ocean environment, network topology, and parameters. The results show that the performance of CSMA/CA is greater than the other protocols in such networks. It also proves that the integrated simulation system can accurately simulate the relevant characteristics of the MAC protocol. [ABSTRACT FROM AUTHOR]

Network attacks show a trend of increased attack intensity, enhanced diversity, and more concealed attack methods, which put forward higher requirements for the performance of network security equipment. Unlike the SDN (software defined network) switch with a fixed-function data plane, switches with programmable data planes can help users realize more network protocols. Programming Protocol-independent Packet Processors (P4) is proposed to define the operations of the data plane and to implement user's applications, e.g., data center networks, security, or 5G. This paper provides a review of research papers on solving network security problems with P4-based programmable data plane. The work can be organized into two parts. In the first part, the programming language P4, P4 program, architectures, P4 compilers, P4 Runtime, and P4 target are introduced according to the workflow model. The advantages of P4-based programmable switching in solving network security are analyzed. In the second part, the existing network security research papers are divided into four parts according to the perspectives of passive defense, active defense, and combination of multiple technologies. The schemes in each category are compared, and the core ideas and limitations are clarified. In addition, a detailed comparison is made for the research on the performance of P4 targets. Finally, trends and challenges related to the P4-based programmable data plane are discussed. [ABSTRACT FROM AUTHOR]

The widespread application of wireless communication technology brings great convenience to people, but security and privacy problems also arise. To assess and guarantee the security of wireless networks and user devices, discovering and identifying wireless devices become a foremost task. Currently, effective device identification is still a challenging issue, as device fingerprinting requires huge training datasets and is difficult to expand, and rule-based identification is not accurate and reliable enough. In this paper, we propose WND-Identifier, a universal and extensible framework for the identification of wireless devices, which can generate high-precision device labels (vendor, type, and product model) efficiently without user interaction. We first introduce the concept of device-info-related network protocols. WND-Identifier makes full use of the natural language features in such protocol messages and combines with the device description in the welcome page, thereby utilizing extraction rules to generate concrete device labels. Considering that the device information in the protocol messages may be incomplete or forged, we further take advantage of the application logic independence and stability of the device-info-related protocol, so as to build a multiprotocol text classification model, which maps the device to a known label. We conduct experiments in homes and public networks and present three application scenarios to verify the effectiveness of WND-Identifier. [ABSTRACT FROM AUTHOR]

This paper presents an in-depth study and analysis of X-ray inspection of basin insulators by wireless sensing technology. Aiming at the characteristics of low contrast and many kinds of noise in the basin insulator ray image, this paper proposes an X-ray basin insulator image denoising method based on improved 3D block matching. Using the RF microcontroller CC2530 chip as the core hardware and networked by ZigBee protocol, the sensor senses and collects various parameters and transmits this information to the monitoring end in real time through wireless. The method proposes an improved wavelet thresholding denoising method to overcome the pseudo-Gibbs phenomenon caused by the wavelet hard thresholding method in the 3D block matching algorithm cofiltering and retain more details of the image. Aiming at the ringing effect caused by the Wiener filtering method used in the three-dimensional block matching algorithm collaborative filtering, an improved Kalman filtering method based on anisotropic diffusion is proposed, which avoids the ringing effect, and has clear edges and complete details. An improved Kalman filtering method based on anisotropic diffusion is proposed to avoid the ringing effect, and the edges are clear, and the details are complete. The proposed method is a safe, efficient, accurate, and feasible method for detecting defects in basin insulators by combining X-ray and improved wireless image sensing technology to detect the internal equipment without disassembling or touching the GIS equipment. [ABSTRACT FROM AUTHOR]

Using the NS2 network simulation tool to teach the "wireless network" course can enhance students' understanding of wireless network protocols through visualization and can also improve students' ability to analyze problems hands on. We built a multimedia wireless network modularized English teaching platform based on NS2 simulation software; made full use of the rich components and network resources provided by NS2 to achieve a high degree of code reuse and reduce the time for developing simulation codes; designed the overall framework of this multimedia wireless network modular English teaching platform based on the B/S structure; and solved the weight coefficient and importance value of the learning input evaluation index, based on the feedback elite teaching optimization algorithm, the improved teaching formula, the increase in the number of exchanges, and the improvement in the learner's performance. The experimental results show that the LEACH and LEACH-C protocols of the clustered NS2 simulation software have more surviving nodes most of the time, which indicates that the clustering routing protocol has better performance. [ABSTRACT FROM AUTHOR]

Health big data has already been the most important big data for its serious privacy disclosure concerns and huge potential value of secondary use. Measurements must be taken to balance and compromise both the two serious challenges. One holistic solution or strategy is regarded as the preferred direction, by which the risk of reidentification from records should be kept as low as possible and data be shared with the principle of minimum necessary. In this article, we present a comprehensive review about privacy protection of health data from four aspects: health data, related regulations, three strategies for data sharing, and three types of methods with progressive levels. Finally, we summarize this review and identify future research directions. [ABSTRACT FROM AUTHOR]

When mobile network enters 5G era, 5G networks have a series of unparalleled advantages. Therefore, the application of 5G network technology in the Internet of Vehicles (IoV) can promote more intelligently vehicular networks and more efficiently vehicular information transmission. However, with the combination of 5G networks and vehicular networks technology, it requires safe and reliable authentication and low computation overhead. Therefore, it is a challenge to achieve such low latency, security, and high mobility. In this paper, we propose a secure and efficient lightweight authentication protocol for vehicle group. The scheme is based on the extended chaotic map to achieve authentication, and the Chinese remainder theorem distributes group keys. Scyther is used to verify the security of the scheme, and the verification results show that the security of the scheme can be guaranteed. In addition, through security analysis, the scheme can not only effectively resist various attacks but also guarantee security requirements such as anonymity and unlinkability. Finally, by performance analysis and comparison, our scheme has less computation and communication overhead. [ABSTRACT FROM AUTHOR]

Based on the improved adaptive filtering method, this paper conducts in-depth discussion and research on embedded graphics and video coding and chooses to improve the adaptive filtering algorithm from three aspects: starting point prediction, search template, and window partitioning. The algorithm is imported into the encoder for video capture and encoding. By capturing videos of different formats, resolutions, and times, the memory size of the video files collected before and after the algorithm optimization is compared, and the optimized algorithm occupies the memory space of the video file in the actual system. The conclusion of less and higher coding rates. The collected video information is stored on a personal computer equipped with a freeness, and external electronic devices only need to download and install the browser, and the collected video information can be accessed in the local area network through the protocol. The improved coding algorithm has higher coding efficiency and can reduce the storage space occupied by the video. [ABSTRACT FROM AUTHOR]

Different schemes have been proposed for increasing network lifetime in mobile ad hoc networks (MANETs) where nodes move uncertainly in any direction. Mobility awareness and energy efficiency are two inescapable optimization problems in such networks. Clustering is an important technique to improve scalability and network lifetime, as it relies on grouping mobile nodes into logical subgroups, called clusters, to facilitate network management. One of the challenging issues in this domain is to design a real-time routing protocol that efficiently prolongs the network lifetime in MANET. In this paper, a novel fuzzy-based Q-learning approach for mobility-aware energy-efficient clustering (FQMEC) is proposed that relies on deciding the behavioral pattern of the nodes based on their stability and residual energy. Also, Chebyshev's inequality principle is applied to get node connectivity for load balancing by taking history from the monitoring phase to increase the learning accuracy. Extensive simulations are performed using the NS-2 network simulator, and the proposed scheme is compared with reinforcement learning (RL). The obtained results show the effectiveness of the proposed protocol regarding network lifetime, packet delivery ratio, average end-to-end delay, and energy consumption. [ABSTRACT FROM AUTHOR]

Blockchain is a mainstream technology in which many untrustworthy nodes work together to maintain a distributed ledger with advantages such as decentralization, traceability, and tamper-proof. The network layer communication mechanism in its architecture is the core of the networking method, message propagation, and data verification among blockchain nodes, which is the basis to ensure blockchain's performance and key features. When blocks are propagated in peer-to-peer (P2P) networks with gossip protocol, the high propagation delay of the protocol itself reduces the propagation speed of the blocks, which is prone to the chain forking phenomenon and causes double payment attacks. To accelerate the propagation speed and reduce the fork probability, this paper proposes a blockchain network propagation mechanism based on proactive network provider participation for P2P (P4P) architecture. This mechanism first obtains the information of network topology and link status in a region based on the internet service provider (ISP), then it calculates the shortest path and link overhead of peer nodes using P4P technology, prioritizes the nodes with good local bandwidth conditions for transmission, realizes the optimization of node connections, improves the quality of service (QoS) and quality of experience (QoE) of blockchain networks, and enables blockchain nodes to exchange blocks and transactions through the secure propagation path. Simulation experiments show that the proposed propagation mechanism outperforms the original propagation mechanism of the blockchain network in terms of system overhead, rate of data success transmission, routing hops, and propagation delay. [ABSTRACT FROM AUTHOR]

With the rapid development of network technology, computer viruses have developed at a fast pace. The threat of computer viruses persists because of the constant demand for computers and networks. When a computer virus infects a facility, the virus seeks to invade other facilities in the network by exploiting the convenience of the network protocol and the high connectivity of the network. Hence, there is an increasing need for accurate calculation of the probability of computer-virus-infected areas for developing corresponding strategies, for example, based on the possible virus-infected areas, to interrupt the relevant connections between the uninfected and infected computers in time. The spread of the computer virus forms a scale-free network whose node degree follows the power rule. A novel algorithm based on the binary-addition tree algorithm (BAT) is proposed to effectively predict the spread of computer viruses. The proposed BAT utilizes the probability derived from PageRank from the scale-free network together with the consideration of state vectors with both the temporal and learning effects. The performance of the proposed algorithm was verified via numerous experiments. [ABSTRACT FROM AUTHOR]

Spontaneous networks lack an a priori communication infrastructure, the neighbors are unknown right after the deployment, and they are used during a period of time and in a certain location. In this paper, we present a new randomized creation model of a spontaneous wireless ad hoc network based on trusted neighbors. The idea is to manage the neighbor discovery with the exchange of identity cards, and the checking of a signature establishes a relationship based on trust of the neighbors. To asses the performance of our randomized trusted network proposal and compare it against an existing deterministic protocol used as reference, we relied on Castalia 3.2 simulator, regarding 4 metrics: time, energy consumption, throughput, and number of discoveries vs packet sent ratio. We found that our proposal outperforms the reference protocol in terms of time, energy, and discoveries vs packet sent ratio in a one-hop setting, while it outperforms the reference protocol regarding all 4 metrics in multihop environments. We also evaluated our proposal through simulations varying the transmission probability and proved that it does not require to know the number of nodes if a fixed transmission probability is set, providing reasonable results. Moreover, our proposal is based on collision detection, it knows when to terminate the process, it does not require a transmission schedule, and it follows more realistic assumptions. In addition, a qualitative comparison is carried out, comparing our proposal against existing protocols from the literature. [ABSTRACT FROM AUTHOR]

In today's security landscape, advanced threats are becoming increasingly difficult to detect as the pattern of attacks expands. Classical approaches that rely heavily on static matching, such as blacklisting or regular expression patterns, may be limited in flexibility or uncertainty in detecting malicious data in system data. This is where machine learning techniques can show their value and provide new insights and higher detection rates. The behavior of botnets that use domain-flux techniques to hide command and control channels was investigated in this research. The machine learning algorithm and text mining used to analyze the network DNS protocol and identify botnets were also described. For this purpose, extracted and labeled domain name datasets containing healthy and infected DGA botnet data were used. Data preprocessing techniques based on a text-mining approach were applied to explore domain name strings with n-gram analysis and PCA. Its performance is improved by extracting statistical features by principal component analysis. The performance of the proposed model has been evaluated using different classifiers of machine learning algorithms such as decision tree, support vector machine, random forest, and logistic regression. Experimental results show that the random forest algorithm can be used effectively in botnet detection and has the best botnet detection accuracy. [ABSTRACT FROM AUTHOR]

Large-scale wireless sensor networks (LSWSNs) are currently one of the most influential technologies and have been widely used in industry, medical, and environmental monitoring fields. The LSWSNs are composed of many tiny sensor nodes. These nodes are arbitrarily distributed in a certain area for data collection, and they have limited energy consumption, storage capabilities, and communication capabilities. Due to limited sensor resources, traditional network protocols cannot be directly applied to LSWSNs. Therefore, the issue of maximizing the LSWSNs' lifetime by working with duty cycle design algorithm has been extensively studied in this paper. Encouraged by annealing algorithm, this work provides a new elite adaptive simulated annealing (EASA) algorithm to prolong LSWSNs' lifetime. We then present a sensor duty cycle models, which can make sure the full coverage of the monitoring targets and prolong the network lifetime as much as possible. Simulation results indicate that the network lifetime of EASA algorithm is 21.95% longer than that of genetic algorithm (GA) and 28.33% longer than that of particle swarm algorithm (PSO). [ABSTRACT FROM AUTHOR]

Wireless sensor technology has penetrated various domains of today's life and plays a vital role in the advanced technology. Numerous researchers have combined this outstanding technology with other fields such as resource mining, industry, healthcare, automobile system, gaming industry, and dramas. However, in traditional resource mining, long mining time leads to incomplete mining results along with low accuracy. In order to improve the effect of resource mining, we have proposed an effective Quantum Generic Algorithm (QGA) based on drama resource mining by using wireless sensor technology. In our proposed scheme, we have combined the RFID technology of wireless sensor with wireless network protocol stack for the purpose of collecting drama resources on the network platform. We have classified the drama resources on the network platform by using QGA based on the results of resources collection. Additionally, we have mined the semantic association features of frequent patterns of the drama resources on the platform and combined with the fuzzy attribute feature detection method. The experimental results show that this method is superior to the traditional methods in terms of resource mining time, mining results' comprehensiveness, and mining results' accuracy, which shows that this method has practical application value. [ABSTRACT FROM AUTHOR]

In the intelligent era of human-computer symbiosis, the use of machine learning method for covert communication confrontation has become a hot topic of network security. The existing covert communication technology focuses on the statistical abnormality of traffic behavior and does not consider the sensory abnormality of security censors, so it faces the core problem of lack of cognitive ability. In order to further improve the concealment of communication, a game method of "cognitive deception" is proposed, which is aimed at eliminating the anomaly of traffic in both behavioral and cognitive dimensions. Accordingly, a Wasserstein Generative Adversarial Network of Covert Channel (WCCGAN) model is established. The model uses the constraint sampling of cognitive priors to construct the constraint mechanism of "functional equivalence" and "cognitive equivalence" and is trained by a dynamic strategy updating learning algorithm. Among them, the generative module adopts joint expression learning which integrates network protocol knowledge to improve the expressiveness and discriminability of traffic cognitive features. The equivalent module guides the discriminant module to learn the pragmatic relevance features through the activity loss function of traffic and the application loss function of protocol for end-to-end training. The experimental results show that WCCGAN can directly synthesize traffic with comprehensive concealment ability, and its behavior concealment and cognitive deception are as high as 86.2% and 96.7%, respectively. Moreover, the model has good convergence and generalization ability and does not depend on specific assumptions and specific covert algorithms, which realizes a new paradigm of cognitive game in covert communication. [ABSTRACT FROM AUTHOR]

Identifier-locator network protocol (ILNP) is a host-based identifier/locator split architecture scheme (ILSA), which depends on address rewriting to support end-to-end mobility and multihoming. The address rewriting is performed by hosts using a network layer logical cache that stores state information related to the communicated hosts, which is called identifier-locator communication cache (ILCC). Since address rewriting is executed on a packet basis in ILNP, ILCC lookups are required at each packet reception and transmission. This leads to a strong correlation between the host's network stack performance and ILCC performance. This paper presents a study of the effect of ILCC size on network stack performance. Within this paper, a direct comparison of the performance of two ILNP prototypes that differ by ILCC management mechanism is conducted. We present ILCC size measurements and study their effects on the host's network stack performance. The results show that ILCC growth caused by correspondents increase has a significant effect on the latency of both network and transport layers. The obtained results show that controlling ILCC size through an effective policy strongly enhances ILNP network stack performance. [ABSTRACT FROM AUTHOR]

In the last decade, energy harvesting wireless sensor network (EHWSN) has been well developed. By harvesting energy from the surrounding environment, sensors in EHWSN remove the energy constraint and have an unlimited lifetime in theory. The long-lasting character makes EHWSN suitable for Industry 4.0 applications that usually need sensors to monitor the machine state and detect errors continuously. Most wireless sensor network protocols have become inefficient in EHWSN due to neglecting the energy harvesting property. In this paper, we propose CPEH, which is a clustering protocol specially designed for the EHWSN. CPEH considers the diversity of the energy harvesting ability among sensors in both cluster formation and intercluster communication. It takes the node's information such as local energy state, local density, and remote degree into account and uses fuzzy logic to conduct the cluster head selection and cluster size allocation. Meanwhile, the Ant Colony Optimization (ACO) as a reinforcement learning strategy is utilized by CPEH to discover a highly efficient intercluster routing between cluster heads and the base station. Furthermore, to avoid cluster dormancy, CPEH introduces the Cluster Head Relay (CHR) strategy to allow the proper cluster member to undertake the cluster head that is energy depletion. We make a detailed simulation of CPEH with some famous clustering protocols under different network scenarios. The result shows that CPEH can effectively improve the network throughput and delivery ratio than others as well as successfully solve the cluster dormancy problem. [ABSTRACT FROM AUTHOR]

An event-triggered-based external consensus control protocol with delay compensation for networked multiagent systems (NMASs) with nonlinear dynamics and network-induced delays is discussed in this paper. An RBF-ARX modelling method is adopted to approximate a nonlinear system. By utilizing the RBF-ARX model, the locally linearized time series model can be obtained to describe the behaviour of agents with nonlinear characteristics. An event-triggered control protocol with communication delay compensation is proposed to reduce the effects of network-induced delays and the times of the agent's control update and communication between others by applying the idea of a prediction strategy. The event-triggered mechanism for each agent only depends on the deviation between its own output and the delay-compensated output of neighboring agents. Then, a distributed event-triggered-based and delay-compensated external consensus control protocol is given. Under this proposed control protocol, both the consensus and stability of the system can be proved. Finally, a numerical simulation is performed to verify the availability of our results. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) is considered one of the world's ruling technologies. Billions of IoT devices connected together through IoT forming smart cities. As the concept grows, it is very challenging to design an infrastructure that is capable of handling large number of devices and process data effectively in a smart city paradigm. This paper proposed a structure for smart cities. It is implemented using a lightweight easy to implement network design and a simpler data format for information exchange that is suitable for developing countries like Pakistan. Using MQTT as network protocol, different sensor nodes were deployed for collecting data from the environment. Environmental factors like temperature, moisture, humidity, and percentage of CO2 and methane gas were recorded and transferred to sink node for information sharing over the IoT cloud using an MQTT broker that can be accessed any time using Mosquitto client. The experiment results provide the performance analysis of the proposed network at different QoS levels for the MQTT protocol for IoT-based smart cities. JSON structure is used to formulate the communication data structure for the proposed system. [ABSTRACT FROM AUTHOR]

Due to the increasing variety of encryption protocols and services in the network, the characteristics of the application are very different under different protocols. However, there are very few existing studies on encrypted application classification considering the type of encryption protocols. In order to achieve the refined classification of encrypted applications, this paper proposes an Encrypted Two-Label Classification using CNN (ETCC) method, which can identify both the protocols and the applications. ETCC is a two-stage two-label classification method. The first stage classifies the protocol used for encrypted traffic. The second stage uses the corresponding classifier to classify applications according to the protocol used by the traffic. Experimental results show that the ETCC achieves 97.65% accuracy on a public dataset (CICDarknet2020). [ABSTRACT FROM AUTHOR]

Industrial control protocol is the basis of communication and interaction of industrial control system, and its security is related to the whole industrial infrastructure. Because many industrial control systems use proprietary protocols, it is necessary to adopt protocol reverse analysis technology to parse them and then detect whether there are secure vulnerabilities in the protocols by means of fuzzy testing. However, most of the existing technologies are designed for common network protocols, and there is no improvement for industrial control protocol. Therefore, we propose a multistage ensemble reverse analysis method, namely, MSERA, which fully considers the design concept of industrial control protocols. MSERA divides the traditional reverse analysis process into three stages and identifies the fields with different semantic characteristics in different stages and combines with field rectification to effectively improve the results of reverse analysis of industrial control protocols. Through the experimental comparison of some public and proprietary industrial control protocols, it is found that MSERA not only outperforms Netzob in the accuracy of field split but also far exceeds Netzob in semantic recognition accuracy. The experimental results show that MSERA is very practical and suitable for reverse analysis of industrial control protocols. [ABSTRACT FROM AUTHOR]

In recent years, with the rapid development of mobile Internet and 5G technology, great changes have been brought to our lives, and human beings have stepped into the era of big data. These new features and techniques in 5G support many different types of mobile applications for users, which makes network security extremely challenging. Among them, more and more applications involve users' private data, such as location information, financial information, and biological information. In order to prevent users' privacy disclosure, most applications choose to use private protocols. However, such private protocols also provide a means for malware and malicious applications to steal users' privacy and confidential data. From a more secure point of view, we need to provide a way for users to know how many private protocols are running on their mobile phones and distinguish which are authorized applications and which are not. Therefore, the analysis and identification of private protocols have become a hot topic in current research. How to extract the characteristics of network protocol effectively and identify the private protocol accurately becomes the most important part of this research. In this paper, we combine genetic algorithm and association rule algorithm and then propose a set of feature extraction algorithm and protocol recognition algorithm for unknown protocols. The experimental analysis based on the actual data shows that these methods can effectively solve the problems of feature extraction and recognition for unknown protocols and can greatly improve the accuracy of private protocol recognition. [ABSTRACT FROM AUTHOR]

The main control objective of a DC microgrid with a multibus structure is to stabilize the bus voltage and maintain the power balance of the whole system. An adaptive droop control strategy for multibus DC microgrid based on consensus algorithm is proposed. It is based on platform multiagent system, which is realized by network protocol. Under the condition of a weak communication network, the bus at all levels can realize regional power autonomy through packet consensus protocol. A hybrid simulation platform composed of Jade, MacSimJX, and Simulink is built to verify the effectiveness of the control strategy. [ABSTRACT FROM AUTHOR]