Network Application Layer Research Articles

Multi-source heterogeneous data access management framework and key technologies for electric power Internet of Things

The power Internet of Things (IoT) is a significant trend in technology and a requirement for national strategic development. With the deepening digital transformation of the power grid, China’s power system has initially built a power IoT architecture comprising a perception, network, and platform application layer. However, owing to the structural complexity of the power system, the construction of the power IoT continues to face problems such as complex access management of massive heterogeneous equipment, diverse IoT protocol access methods, high concurrency of network communications, and weak data security protection. To address these issues, this study optimizes the existing architecture of the power IoT and designs an integrated management framework for the access of multi-source heterogeneous data in the power IoT, comprising cloud, pipe, edge, and terminal parts. It further reviews and analyzes the key technologies involved in the power IoT, such as the unified management of the physical model, high concurrent access, multi-protocol access, multi-source heterogeneous data storage management, and data security control, to provide a more flexible, efficient, secure, and easy-to-use solution for multi-source heterogeneous data access in the power IoT.

Review on Information Privacy Protection based on Blockchain Principle

As a distributed general ledger technology, a basic platform of smart contract, and a new distributed computing paradigm, blockchain is bound to have an impact on the development of all walks of life, and drive a new round of technology and application changes. To have an impact on the development of all walks of life and drive a new round of technology and application changes, blockchain technology improves efficiency, reduces costs, and enhances data security. However, it also faces serious information security problems. However, while block technology improves efficiency, reducing costs and improving data security, block technology also faces serious information privacy leakage problems. promote the research, development and application of blockchain in information privacy protection. This paper uses the literature analysis method to analyze the advantages and disadvantages of the blockchain in the information privacy protection from the aspects of the blockchain privacy protection This paper uses the literature analysis method to analyze the advantages and disadvantages of the blockchain in the information privacy protection from the aspects of the blockchain privacy protection , analyze the privacy leakage threat of the blockchain in the network layer, transaction layer and application layer, and summarizes the main technologies of the blockchain in the information privacy protection of data users.

EXAM SURVEILLANCE USING MACHINE LEARNING

Recent publications on video surveillance systems in conferences and journals show that researchers are paying attention. This review's objectives are to look at the most recent works that have been published in journals, suggest a new classification scheme for video surveillance systems, and look into each component of that scheme. Using the article's title and keyword, this paper presents a thorough and organized analysis of the prior literature on video surveillance systems from 2010 to 2011. The review was taken from six online digital libraries. The architecture of video surveillance systems, which consists of six layers: the Concept and Foundation Layer, Network Infrastructure Layer, Processor Layer, Communication Layer, Application Layer, and User Interaction Layer, serves as the foundation for the suggested classification framework. This analysis demonstrates that while real-time aspects of the problem are the subject of many publications and studies, the use of extracted and retrieved information for forensic video surveillance has received less attention. Key Words: CCTV, video surveillance system, exam surveillance, student activity recognition, frameworks, datasets, network infrastructure layer, processing layer, communication layer, application layer, and user interaction layer.

Achieving model explainability for intrusion detection in VANETs with LIME.

Vehicular ad hoc networks (VANETs) are intelligent transport subsystems; vehicles can communicate through a wireless medium in this system. There are many applications of VANETs such as traffic safety and preventing the accident of vehicles. Many attacks affect VANETs communication such as denial of service (DoS) and distributed denial of service (DDoS). In the past few years the number of DoS (denial of service) attacks are increasing, so network security and protection of the communication systems are challenging topics; intrusion detection systems need to be improved to identify these attacks effectively and efficiently. Many researchers are currently interested in enhancing the security of VANETs. Based on intrusion detection systems (IDS), machine learning (ML) techniques were employed to develop high-security capabilities. A massive dataset containing application layer network traffic is deployed for this purpose. Interpretability technique Local interpretable model-agnostic explanations (LIME) technique for better interpretation model functionality and accuracy. Experimental results demonstrate that utilizing a random forest (RF) classifier achieves 100% accuracy, demonstrating its capability to identify intrusion-based threats in a VANET setting. In addition, LIME is applied to the RF machine learning model to explain and interpret the classification, and the performance of machine learning models is evaluated in terms of accuracy, recall, and F1 score.

Research on the Identification of Internet Critical Nodes Based on Multilayer Network Modeling

The research goal of cyberspace security situational awareness analysis is to predict the future security development of the target network by acquiring, understanding, and displaying the security elements in the large-scale network environment. Current cyberspace security situational awareness systems are mostly based on traditional single-layer network topology to analyze the security of the target network's operational posture. However, as the scale of the network continues to expand, the network structure becomes more complex, and the information fusion in multiple fields in practical applications deepens, the single-layer topology model can no longer meet the analysis requirements. In this paper, we construct a multilayer network topology model for cyberspace security situational awareness by integrating multidimensional information in the physical device layer network, business application layer network, and user role layer network. Meanwhile, to eliminate the limitations of traditional node importance indicators, a node importance assessment indicator that integrates topological centrality and node dependency factor is proposed in conjunction with model characteristics: multilayer dependency CRITIC indicator ( MDCI ). On the one hand, MDCI fits a variety of evaluation metrics through the CRITIC multi-attribute decision method to comprehensively assess the importance of nodes in network centrality, and on the other hand, MDCI better aggregates the important contributions of nodes in each network layer based on node dependency factor to coordinate multilayer network information. The experimental results show that MDCI has better ordering monotonicity and generates more stable metric sequences, and can effectively cause large-scale failures in multilayer network while destroying fewer physical device components, which can be better adapted to the critical node identification needs of multilayer network.

Automatic gas dynamic volumetric system for aquatic products' MA preservation

As there are many links in the cold chain logistics process, which takes a long time, the processed aquatic products are easily damaged and deteriorated. And the growth and reproduction of microorganisms in aquatic products can be inhibited by MA (Modified Atmosphere) preservation, thus extending the shelf life of aquatic products. In this context, we have developed an automatic gas dynamic volumetric system for aquatic products' MA preservation. It can control the operation platform by a 4G network cell phone client and realize remote monitoring function. Particularly, there are the main contents and conclusions of the study: 1) Combined with MA preservation, intelligent control and network communication technologies, we overcame the difficulties of remote monitoring for gas dynamic volumetric. Then, according to MA preservation and automatic gas dynamic volumetric, we designed a logical structure of the system, including the specific distribution of the sensing layer, network transmission layer and application layer. 2) We established the mathematical model for the system based on the step response method, and designed the analog acquisition, control and gas dynamic volumetric system; In addition, in this system, we realized the gas dynamic volumetric based on the embedded PID control algorithm and mass flow controller; Then, we used 4G network for remote data transmission and 4G DTU as network data transceiver, and realized remote monitoring function of mobile client through cell phone terminal applet software. 3) The results were shown by testing the stability, accuracy and communication of the system: the gas concentration error of the system floated at ± 0.25% when the gas dynamic volumetric system was stable, which was 12 times more accurate than before, and the gas dynamic volumetric speed based on embedded PID control algorithm was increasing by approximately about 50%, which greatly improved the working conditions and production efficiency.

IoT Based Approach for Load Controlling and Smart Home Security Systems

Abstract: Appliance load monitoring in smart homes has been gaining importance due to its significant advantages in achieving an energy efficient smart grid. The methods to manage such processes can be classified into hardware-based methods, including intrusive load monitoring (ILM) and software-based methods referring to non-intrusive load monitoring (NILM). ILM is based on low-end meter devices attached to home appliances in opposition to NILM techniques, where only a single point of sensing is needed. Although ILM solutions can be relatively expensive, they provide higher efficiency and reliability than NILMs. Moreover, future solutions are expected to be hybrid, combining the benefits of NILM along with individual power measurement by smart plugs and smart appliances. This paper proposes a novel ILM approach for load monitoring that aims to develop an activity recognition system based on IoT architecture. The proposed IoT architecture consists of the appliances layer, perception layer, communication network layer, middleware layer, and application layer. The main function of the appliance recognition module is to label sensor data and allow the implementation of different home applications. Keywords: Smart Homes , Home Security, Load Controlling .

Design of a wireless communication system for distribution monitoring

According to the requirements of wireless communication in medium and low voltage distribution monitoring system, this paper designs a wireless signal networking method at medium and low voltage distribution side, and analyzes the networking mechanism between each protocol layer, including important wireless avoidance mechanism, networking mechanism of network layer and application layer. Aiming at the problem of network connectivity, a wireless networking algorithm based on optimal distance is proposed, and other common wireless networking algorithms are studied at the same time. The networking algorithms are simply evaluated and compared, and the advantages of this strategy in wireless networking are verified by experimental simulation and analysis.

An Adaptive Protection of Flooding Attacks Model for Complex Network Environments

Currently, online organizational resources and assets are potential targets of several types of attack, the most common being flooding attacks. We consider the Distributed Denial of Service (DDoS) as the most dangerous type of flooding attack that could target those resources. The DDoS attack consumes network available resources such as bandwidth, processing power, and memory, thereby limiting or withholding accessibility to users. The Flash Crowd (FC) is quite similar to the DDoS attack whereby many legitimate users concurrently access a particular service, the number of which results in the denial of service. Researchers have proposed many different models to eliminate the risk of DDoS attacks, but only few efforts have been made to differentiate it from FC flooding as FC flooding also causes the denial of service and usually misleads the detection of the DDoS attacks. In this paper, an adaptive agent-based model, known as an Adaptive Protection of Flooding Attacks (APFA) model, is proposed to protect the Network Application Layer (NAL) against DDoS flooding attacks and FC flooding traffics. The APFA model, with the aid of an adaptive analyst agent, distinguishes between DDoS and FC abnormal traffics. It then separates DDoS botnet from Demons and Zombies to apply suitable attack handling methodology. There are three parameters on which the agent relies, normal traffic intensity, traffic attack behavior, and IP address history log, to decide on the operation of two traffic filters. We test and evaluate the APFA model via a simulation system using CIDDS as a standard dataset. The model successfully adapts to the simulated attack scenarios’ changes and determines 303,024 request conditions for the tested 135,583 IP addresses. It achieves an accuracy of 0.9964, a precision of 0.9962, and a sensitivity of 0.9996, and outperforms three tested similar models. In addition, the APFA model contributes to identifying and handling the actual trigger of DDoS attack and differentiates it from FC flooding, which is rarely implemented in one model.

Research on feature selection algorithm of unknown wireless protocol based on mutual information

Aiming at the problem of unknown wireless protocol identification methods that focus on the application layer, but the wireless network application layer data is difficult to capture, and the existing method research stays at the signal level and the identification effect is poor, this paper proposes an identification method for feature selection of wireless protocol data from the data link layer. This paper extracts the protocol features of the data frame set by improving the AC algorithm and the association rule algorithm, using mutual information theory, and combining the definition of information correlation and redundancy with the normalization idea, further screening and verification of frame features are carried out to achieve effective extraction of protocol features, effectively improve the accuracy of feature sets, and thereby improve the efficiency and accuracy of protocol recognition.

Passive Wireless Sensor Network‐Based In‐Transit Health Status Monitoring for Railway Transportation

In this paper, passive wireless sensor network technology is used to conduct in‐depth research and analysis on the monitoring of the in‐transit health status of railway transportation. Safety detection sensors, train communication networks, and other onboard devices organically constitute a train sensor network with comprehensive state sensing, information aggregation, and business collaboration. First, this paper analyzes the three‐layer architecture of the sensing layer, network layer, and application layer of the train operation status safety detection sensor network. Based on analyzing the feasibility of Ethernet application in an onboard communication network, ring, multiring, and ladder‐type sensor network structure schemes are designed. Next, the real‐time performance and reliability of various sensor network structures are analyzed. First, the characteristics of bandwidth demand, priority, and importance of information transmission of each monitoring object are analyzed; then, the system utility function is established according to the bandwidth usage efficiency and communication demand of information transmission of each monitoring object; finally, the bandwidth allocation optimization model is solved by using a particle swarm optimization algorithm, and the remaining bandwidth resources are dynamically rationed on demand while ensuring the complete transmission of basic information of each monitoring object. Rail transit technology has profoundly affected and changed the travel and lifestyle of residents in the new era and will provide strong infrastructure support for the smooth advancement of new urbanization construction and development strategies. The remaining bandwidth resources are dynamically allocated on the premise of ensuring the complete transmission of basic information of each monitoring object. In summary, this paper presents a complete study on the optimal allocation of resources for wireless sensor networks dedicated to rail transit condition monitoring, starting from the perspectives of routing protocol optimization, data fusion rate optimization, and bandwidth dynamic allocation optimization to achieve the efficient use of limited resources. The achieved research results enrich the research content in the field of wireless monitoring of rail transit system conditions at home and abroad in a certain sense and will also provide theoretical and empirical support for practical practitioners, managers, and scholars of service condition monitoring of rail transit systems.

Developing a Framework for Data Communication in a Wireless Network using Machine Learning Technique

The emergence of Internet of Things (IoT) has become a huge innovation for utilizing the enormous power of wireless media. The adaptation of smart devices, with intelligent networking, has greatly enhanced the traffic of the IoT environment. The present security mechanism is primarily focusing on specific areas such as content filtering, monitoring techniques, and anomaly detection. A vulnerability reflects the inability of a network that allows an attacker to detect the extent of existing mechanism of security. The existing techniques focused on specific attacks rather than monitoring the whole network. However, there is a demand for a framework to govern and protect data and services in IoT network. Anomaly detection framework is a resource intensive activity to protect data and services of IoT / Wireless Sensor Networks (WSN). It supports application layer of IoT network and traces it frequently to find the existence of malicious activities. In this study, researchers proposed an anomaly detection framework to safeguard against wireless attacks. The proposed framework has employed a machine learning technique to detect the traces of wireless attacks. It supports IoT based networks to monitor the functionalities of the resources. In addition, it discusses the open challenges in IoT networks with possible solutions. Researchers employed a test bed for evaluating the proposed framework. The outcome of the study shows that the proposed framework provides better services with more security.

IoT Based Approach for Load Monitoring and Activity Recognition in Smart Homes

Appliance load monitoring in smart homes has been gaining importance due to its significant advantages in achieving an energy efficient smart grid. The methods to manage such processes can be classified into hardware-based methods, including intrusive load monitoring (ILM) and software-based methods referring to non-intrusive load monitoring (NILM). ILM is based on low-end meter devices attached to home appliances in opposition to NILM techniques, where only a single point of sensing is needed. Although ILM solutions can be relatively expensive, they provide higher efficiency and reliability than NILMs. Moreover, future solutions are expected to be hybrid, combining the benefits of NILM along with individual power measurement by smart plugs and smart appliances. This paper proposes a novel ILM approach for load monitoring that aims to develop an activity recognition system based on IoT architecture. The proposed IoT architecture consists of the appliances layer, perception layer, communication network layer, middleware layer, and application layer. The main function of the appliance recognition module is to label sensor data and allow the implementation of different home applications. Three different classifier models are tested using real data from the UK-DALE dataset: feed-forward neural network (FFNN), long short-term memory (LSTM), and support vector machine (SVM). The developed activities of daily living (ADL) algorithm maps each ADL to a set of criteria depending on the appliance used. The features are extracted according to the consumption in Watt-hours and the times where they are switched on. In the FFNN and the LSTM networks, the accuracy is above 0.9 while around 0.8 for the SVM network. Other experiments are performed to evaluate the classifier model using a new test set. A sensitivity analysis is also carried out to study the impact of the group size on the classifier accuracy.

Improved Network Traffic by Attacking Denial of Service to Protect Resource Using Z-Test Based 4-Tier Geomark Traceback (Z4TGT)

Network security plays a vital role in protecting the resources available in the network against various threats. There are vulnerabilities in every system connected to the network. Due to these, unauthorized users try to access and utilize the system resources with some intention. Denial of Service is one of the major attacks, which makes the system resources unavailable to the intended users of the system. Distributed denial of service is invoked by a group of attackers who are either compromised or deliberately demolish the services offered to the victim system. Existing research work on DDoS handling strategies has focused on those with some limitations. In this proposed research work named Z-test based 4-Tier Geomark Traceback (Z4TGT), DDoS attack at various layers of data transmission is detected. The origin of the Source of the attack is traced back using the telephone prefix code incorporated with the Z4TGT mechanism. This mechanism helps to mitigate the attack packets that occur in the victim machine. Z-test based statistical analysis helps to eliminate the type II error by clearly defining the null and alternate hypothesis about the possibilities of the attack. Hence, a negligible false positive rate is achieved. The protection offered by the Z4TGT is available at the network layer and application layer. The average detection rate of 98.75% is achieved with the help of Z-test based statistical analysis. The performance of the victim machine is analyzed before and after the attack mitigation process by considering the throughput and attack rate. A mitigation strategy is effectively performed with the minimum collateral damage of 8%

Retracted: Design of multimedia blockchain privacy protection system based on distributed trusted communication

AbstractBlockchain is a hash chain with chain structure formed by data blocks in chronological order. With the promotion of bitcoin, the research and application of blockchain, the underlying core technology of bitcoin, is growing rapidly, which is considered to be the fifth generation of Internet disruptive technology after mobile Internet. At present, there are various identity management applications and platforms based on blockchain, but while blockchain has gradually become the cornerstone of the value Internet, it also brings great challenges to the privacy protection needs of users. This article analyzes the privacy protection mechanism of blockchain network layer, transaction layer and application layer, including malicious node detection and access restriction technology in network layer, mixed currency technology and encryption technology in blockchain transaction layer. In this article, distributed communicable network technology and multimedia data security technology are used to build a blockchain security system. We designed the efficient system to accurately analyze the data and the information protection. The experimental results show that the proposed method has better data security guarantee ability.

Optimizing multicast routing tree on application layer via an encoding-free non-dominated sorting genetic algorithm

The task of Application Layer Multicast (ALM) routing is to establish a proper routing tree on application layer network, which transmits data packages from a source to multiple destinations. ALM routing tree is instable due to the end-hosts’ departure or failure. In view of this, we formulated the ALM routing as a multi-objective optimization problem, where the aim is to optimize the ALM routing tree’s topological structure for minimizing its transmission delay and instability simultaneously. A novel encoding-free non-dominated sorting genetic algorithm is proposed for solving our formulated optimization problem. For achieving encoding-free, genotypes are directly represented as tree-like phenotypes in our proposed algorithm. Accordingly, the genetic operators acting on genotypes, like crossover and mutation, need to be redesigned to adapt the tree-like genotypes. The worst-case time complexity of the proposed algorithm is theoretically analyzed and exhaustive simulation results manifest that our proposed algorithm is capable of obtaining high-quality Pareto front. Besides, several interesting issues, such as how to select the final solution out of the obtained Pareto front and the reason why GP is not used, are also discussed.

A collaborative approach for improving the security of vehicular scenarios: The case of platooning

Autonomous vehicles platooning has received considerable attention in recent years, due to its potential to significantly benefit road transportation, improving traffic efficiency, enhancing road safety and reducing fuel consumption. The Vehicular ad hoc Networks and the de facto vehicular networking standard IEEE 802.11p communication protocol are key tools for the deployment of platooning applications, since the cooperation among vehicles is based on a reliable communication structure. However, vehicular networks can suffer different security threats. Indeed, in collaborative driving applications, the sudden appearance of a malicious attack can mainly compromise: (i) the correctness of data traffic flow on the vehicular network by sending malicious messages that alter the platoon formation and its coordinated motion; (ii) the safety of platooning application by altering vehicular network communication capability. In view of the fact that cyber attacks can lead to dangerous implications for the security of autonomous driving systems, it is fundamental to consider their effects on the behavior of the interconnected vehicles, and to try to limit them from the control design stage.To this aim, in this work we focus on some relevant types of malicious threats that affect the platoon safety, i.e. application layer attacks (Spoofing and Message Falsification) and network layer attacks (Denial of Service and Burst Transmission), and we propose a novel collaborative control strategy for enhancing the protection level of autonomous platoons. The control protocol is designed and validated in both analytically and experimental way, for the appraised malicious attack scenarios and for different communication topology structures. The effectiveness of the proposed strategy is shown by using PLEXE, a state of the art inter-vehicular communications and mobility simulator that includes basic building blocks for platooning. A detailed experimental analysis discloses the robustness of the proposed approach and its capabilities in reacting to the malicious attack effects.

Augmented lightweight security scheme with access control model for wireless medical sensor networks

Advances made in the wearable and biosensor has attracted the applicability and usability of the healthcare applications. The biosensors collect the human physiological data from a remote area using wireless communication medium. Different form of security implications have to be exhibited in wireless body area networks (WBAN). This paper researches on enhancing the network layer and application layer of the WBAN in healthcare environment. The objective of the each phase is to enhance the data accuracy of the collected data with minimized delay. In the first phase, we have constructed attack model for Sybil, sinkhole and wormhole attacks and detected those attacks under different constraints. Secondly, an efficient packet transmission model which helps to transmit the sensed data based on their emergency using weighted product model. After devising network layer, a lightweight security scheme, an improved elliptic curve cryptography (ECC) that permits the data access for the authorized users. Experimental analysis is carried out for each phase under pre-defined simulation parameters which explores better results. The framed attack model detects the three eminent attacks in terms of false positive rate and false negative rate which helps the data controller for message transmission process. Likewise, we have also achieved 97% packet delivery ratio with 0.8 ms packet dropping ratio for better packet transmission model than the existing TOPSIS model. Time taken for key generation and secret key exchange using our proposed ECC achieved 2.4 and 0.96 ms. The overall system proves that our research model achieves scalable and fairness medical sensor networks system.

Vulnerability Scanning System Used in the Internet of Things for Intelligent Devices

This paper presents and implements an integrated set of the system and method called vulnerability scanning for the Internet of things, including the scanning scheme of the background application service and the front desk vulnerability. The scheme not only has the advantages of passive scanning and active scanning, but can scan the security holes in the internet-connected intelligent devices commonly used in Wi-Fi, Bluetooth LE, ZigBee and RFID communications. By the vulnerability scanning system to the network layer and application layer traffic analysis, it can accurately scan some specific vulnerabilities existing in smart devices, as well as be a real-time online vulnerability analysis system given suggestion.

Design and Development of Collaborative Detection and Taxonomy of DDoS Attacks Using ESVM

Distributed Denial of Service (DDoS) assault is a ceaseless basic risk to the web. Application layer DDoS Attack is gotten from the lower layers. Application layer based DDoS assaults utilize honest to goodness HTTP asks for after foundation of TCP three-way handshaking and overpowers the casualty assets, for example, attachments, CPU, memory, circle, database transfer speed. Arrange layer based DDoS assaults sends the SYN, UDP and ICMP solicitations to the server and debilitates the transfer speed. An oddity discovery system is proposed in this paper to identify DDoS assaults utilizing Enhanced Support Vector Machine (ESVM). The Application layer DDoS Attack, for example, HTTP Flooding, DNS Spoofing and Network layer DDoS Attack, for example, Port Scanning, TCP Flooding, UDP Flooding, ICMP Flooding, Land Flooding. Session Flooding is taken as test tests for ESVM. The Normal client gets to conduct characteristics is taken as preparing tests for ESVM. The movement from the testing tests and preparing tests are Cross Validated and the better arrangement exactness is acquired. Application and Network layer DDoS assaults are arranged with order exactness of 99 % with ESVM.