Network Packet Loss Research Articles

Optimizing and Analyzing Traffic Data Using Data Mining Techniques

This is the project titled Optimizing and Analyzing of Traffic Data using Data Mining techniques and under this project goals will be defined which may include enhancing network perfomance ,reducing latency and strenghtening security measures. Next, critical network componemts are identified ,such as routers, switches and firewalls which require analysis and optimization. Network traffic data is collected using monitoring tools or packet sniffers, encompassing both inbound and outbound traffic. This data is then analyzed to identify patterns trends and potential security threats. Network bottlenecks and performance issues are also identified. Based on the analysis, optimizaion techniques are implemented. This may involve reconfigurring network settings, adjusting routing protocols or upgrading hard watre components, Simultaneously, security measures are implemented to address identified vulnerabilities .Perfomance improvements are continously monitored and measured using metrics such as network latency, throughput and packet loss. The optimizations are finetuned periodically based on ongoing and new traffic patterns

SSPRD: A Shared-Storage-Based Hardware Packet Reordering and Deduplication System for Multipath Transmission in Wide Area Networks.

To increase bandwidth and overcome packet loss in Wide Area Networks (WANs), per-packet multipath transmission and redundant transmission are increasingly being used as Software-Defined Wide Area Network (SD-WAN) solutions. However, this results in out-of-order and duplicate packets in the destination network. To restore sequential and unique data streams for multiple connections, hardware packet buffers with significant depth are required due to the large delay difference between WAN paths. To address this issue, SSPRD, a shared-storage-based packet reordering and deduplication system using a Field-Programmable Gate Array (FPGA), is proposed. The storage space for packets and sub-buffers is shared by all sessions with dynamic allocation. Packets are stored in the DDR and are sorted by their descriptors in the buffers. We also develop a sub-buffer-based timeout event handling algorithm. While supporting four sessions, SSPRD achieves a deep reorder buffer on hardware, with a depth of up to 15,360 packets per session. Compared with other solutions, SSPRD reduces buffer space usage by 62.5%, and reaches a packet reordering and deduplicating performance of 10 Gbps for 1500-byte packets.

Deep Artificial Immune System With Malicious Node Detection and Secure Routing Protocol in MANET

ABSTRACTIn the context of Mobile Ad Hoc Networks (MANETs), the dynamic and decentralized topology poses significant challenges like unreliable connectivity, limited bandwidth, node mobility, and vulnerability to security threats from malicious nodes. Ensuring secure and energy‐efficient data transmission in such environments is crucial for mission‐critical applications. This research addresses these pressing challenges by introducing a robust routing protocol capable of detecting and mitigating malicious nodes, thereby enhancing MANET's Quality of Service (QoS). The proposed approach, the Dendritic Cell with Adaptive Trust Q‐learning Protocol (dDC‐ATQP), integrates several innovative techniques to tackle these issues. Firstly, the trust evaluation mechanism assesses the behavior of nodes to identify potential malicious actors, mitigating the effect of malicious nodes on system execution. Secondly, the adaptive routing strategy optimizes data transmission paths based on real‐time network conditions, reducing latency and packet loss. To evaluate the effectiveness of this approach, extensive simulations are conducted using a range of performance metrics. The results demonstrate significant improvements over existing methods, including a throughput increase of (79.2% in 50 s), lower end‐to‐end‐delay (0.075 s for 20 nodes), energy consumption of (38.55/J), higher packet delivery ratio (98% for 20 nodes), reduced packet loss ratio (5% for 100 nodes), enhanced security (80% for 70 nodes).

Impact of Wireless Network Packet Loss on Real-Time Video Streaming Application: A Comparative Study of H.265 and H.266 Codecs

The transmission of real-time videos over wireless networks is prone to the negative consequences of packet loss and delay, which can have a potential effect on the video quality during streaming. These impairments can lead to interruptions, buffering, and degradation of visual and auditory elements, resulting in an unsatisfactory user experience. In this paper, we aim to address the challenges associated with packet loss and delay parameters in wireless networks, and propose an approach to alleviate their impact on real-time video transmission. The proposed approach involves utilizing the H.265/H.266 video coding standards. For Versatile Video Coding (VVC), a patch support for VVdeC and VVenC to FFmpeg (Fast Forward Moving Picture Expert Group) is added. As a result, FFmpeg is used to encode, stream and decode all videos. Raw videos of 2K qualities are encoded based on the adaptive quantization (QP) for the above-mentioned codecs. By selecting optimal transmission data based on various network conditions, this approach enhances the Quality of Experience (QoE) for end-users while minimizing resource usage in the wireless network. Furthermore, the proposed approach selects the codec standards according to their bitrates and frame rates. Simulation results indicate that the proposed approach has a significant improvement for real-time video streaming over wireless networks to satisfy the end user experience. The approach also outperforms other related work by gaining a PSNR of +12 dB for H.265 and +13 dB for H.266 when the network packet loss is 1%.

Development of Synchronization Selection Method in IoT with Secure Channel Bidirectional Communication

Background: The rapid development of wireless communications and mobile computation has given rise to the novel Internet of Things (IoT) systems, which is causing considerable research attention and industrial development. However, the lack of synchronization between the timers of IoT devices compromises the network's security. Aim: The purpose of this patent application is to present a technique for synchronizing the timepieces of IoT gadgets and establishing a secure channel for the transmission of data from source to destination. Objective: This study proposes a Synchronization Selection Method (SSM) for IoT systems to enhance network security and reduce packet loss. Methods: The method utilizes time-lay synchronization and RSA algorithm-based secure channel establishment. Time lay is a technique that was developed for IoT devices to achieve efficient clock synchronization of sensor nodes. Before synchronizing the sensor nodes' timings, the cluster leaders initiate the process. Utilizing a finite number of nodes, the proposed method was executed in MATLAB. Results: Time-lay synchronization involves all network nodes synchronizing their clocks with a third-party clock. In the context of time-lay synchronization, the term “third-party clock” refers to a single specific point that contains the time signal that all nodes in the network use as a reference. This third-party clock is outside of the network nodes and acts as the standard for the precise and synchronized time within the network. Therefore, it can be deduced that each of the techniques possesses its advantages and disadvantages. Each of the synchronization techniques has the potential to significantly benefit the IoT by offering smart clock synchronization that is more secure. Experimental results demonstrate that the proposed method improves throughput and reduces packet loss compared to existing techniques. Conclusion: The potential of this patent is highly significant for solving the synchronization problem of IoT devices and enhancing network security with decreased network packet loss. Other: The SSM would be assessed using the parameters of packet loss and throughput.

ANALIZA STATYSTYK SIECI KOMPUTEROWYCH W CELU IDENTYFIKACJI PRZEPŁYWÓW INFORMACJI ZAKŁÓCAJĄCYCH STABILNOŚĆ W WOJSKOWYCH SIECIACH LOKALNYCH

The increasing complexity and scope of military computer networks necessitate robust methods to ensure network stability and security. This study presents a comprehensive analysis of computer network statistics in military local networks to develop a method for detecting information flows that disrupt stability. By leveraging advanced statistical techniques and machine learning algorithms, this research aims to enhance the cybersecurity posture of military local networks globally. Military networks are vital for communication, data exchange, and operational coordination. However, the dynamic nature of network traffic and the persistent threat of cyberattacks pose significant challenges to maintaining network stability. Traditional monitoring techniques often fail to meet the unique requirements of military networks, which demand high levels of security and rapid response capabilities. This study employs a multi-faceted approach to detect anomalies in network traffic, utilizing statistical methods such as Z-score analysis, Principal Component Analysis (PCA), and Autoregressive Integrated Moving Average (ARIMA) models. Machine learning techniques, including Support Vector Machines (SVM), Random Forests, Neural Networks, K-means clustering, and Reinforcement Learning, are also applied to identify patterns indicative of stability-disrupting information flows. The integration of statistical and machine learning methods forms a hybrid model that enhances anomaly detection, providing a robust framework for network security. The research problem is formulated as follows: does data collection include comprehensive network traffic data from various segments of military local area networks, including packet flows, transmission rates, and error rates over a specified period? Statistical analysis identifies patterns in the network traffic, which are then used to train machine learning models to classify normal and abnormal traffic. The research hypothesis states that machine learning models achieve high accuracy in detecting stability-disrupting information flows, with a precision rate exceeding 90%. The models identified several instances of stability-disrupting events, correlating these with known security incidents to validate the effectiveness of the detection method. This study underscores the importance of continuous monitoring and analysis of network statistics to ensure stability and security. The proposed method can be integrated with existing network monitoring and intrusion detection systems, providing a comprehensive approach to network security. Future research can build on these findings to develop more sophisticated models and explore additional factors influencing network stability, including the incorporation of advanced machine learning techniques, such as deep learning, and the exploration of other network metrics, like latency and packet loss. This comprehensive approach aims to enhance the security and operational reliability of military local networks.

Data-Driven Distributed Predictive Control for Voltage Regulation and Current Sharing in DC Microgrids With Communication Constraints.

The use of nonideal communication networks makes communication constraints become a topical issue in the research of dc microgrids. How to design a distributed secondary control scheme for voltage recovery and accurate current sharing in islanded dc microgrids subject to communication constraints is of interest in this article. In order to restore the bus voltage to the rated value, a nonlinear element is first introduced into the primary control layer. Then, the closed-loop system of primary control is modeled as a data-driven time-varying linear system. Based on the established model, considering communication constraints, a distributed secondary predictive control strategy is developed to achieve accurate current sharing. While actively compensating for network delays and packet losses, the proposed method renders mathematical physical models unnecessary for the traditional predictive control, and simultaneously completes the multitask in dc microgrids. Finally, several case studies are conducted on a hardware microgrid experimental platform, which not only verifies the effectiveness of the designed data-driven predictive control strategy but also tests microgrid properties such as the plug-and-play ability.

Scalable Cellular V2X Solutions: Large-Scale Deployment Challenges of Connected Vehicle Safety Networks

Vehicle-to-Everything (V2X) communication is expected to accomplish a long-standing goal of the Connected and Autonomous Vehicle (CAV) community to bring connected vehicles to roads on a large scale. A major challenge, and perhaps the biggest hurdle on the path towards this goal, is the scalability issues associated with it, especially when vehicular safety is concerned. As a major stakeholder, Cellular V2X (C-V2X) community, which is based on the 3rd Generation Partnership Project (3GPP), has long been trying to research on whether vehicular networks are able to support the safety-critical applications in high-density vehicular scenarios. This paper attempts to answer this question by first presenting an overview on the scalability challenges faced by 3GPP Release 14 Long Term Evolution C-V2X (LTE-V2X) using the PC5 sidelink interface for low and heavy-density traffic scenarios. Next, it demonstrates a series of solutions that address network congestion, packet losses, and other scalability issues associated with LTE-V2X to enable this communication technology for commercial deployment. In addition, a brief survey is provided into 3GPP Release 16 5G New Radio V2X (NR-V2X) that utilizes the NR sidelink interface and works as an evolution of C-V2X towards better performance for V2X communications, including new enhanced V2X (eV2X) scenarios that possess ultra-low-latency and high-reliability requirements.

GSHFA-HCP: a novel intelligent high-performance clustering protocol for agricultural IoT in fragrant pear production monitoring

The agriculture Internet of Things (IoT) has been widely applied in assisting pear farmers with pest and disease prediction, as well as precise crop management, by providing real-time monitoring and alerting capabilities. To enhance the effectiveness of agriculture IoT monitoring applications, clustering protocols are utilized in the data transmission of agricultural wireless sensor networks (AWSNs). However, the selection of cluster heads is a NP-hard problem, which cannot be solved effectively by conventional algorithms. Based on this, This paper proposes a novel AWSNs clustering model that comprehensively considers multiple factors, including node energy, node degree, average distance and delay. Furthermore, a novel high-performance cluster protocol based on Gaussian mutation and sine cosine firefly algorithm (GSHFA-HCP) is proposed to meet the practical requirements of different scenarios. The innovative Gaussian mutation strategy and sine–cosine hybrid strategy are introduced to optimize the clustering scheme effectively. Additionally, an efficient inter-cluster data transmission mechanism is designed based on distance between nodes, residual energy, and load. The experimental results show that compared with other four popular schemes, the proposed GSHFA-HCP protocol has significant performance improvement in reducing network energy consumption, extending network life and reducing transmission delay. In comparison with other protocols, GSHFA-HCP achieves optimization rates of 63.69%, 17.2%, 19.56%, and 35.78% for network lifespan, throughput, transmission delay, and packet loss rate, respectively.

Cooperative voltage control in distribution networks considering multiple uncertainties in communication

Delays, jitter, and packet loss in communication networks can impact the performance of cooperative voltage control systems in distribution networks. In distribution systems with a high penetration of renewable energy sources that do not respond promptly, these issues can even lead to system destabilization when voltage surges occur. Considering the interdependence of delay, jitter, and packet loss, the current direct approach of accumulating information entropy may result in the deterioration of dynamic control performance. Based on Copula entropy theory, a new multivariate communication uncertainty metric model is proposed. Using the multivariate Epanechnikov kernel function model, a method has been developed to estimate the multivariate non-independent uncertainty of a communication system. Accurate state estimation is integrated into event-triggered sliding mode control (ETSMC) of the distribution network to facilitate coordinated voltage control and enhance resilience against communication uncertainty. Design criteria for the controller and observer parameters are provided based on Lyapunov stability theory. Simulation results confirm that the proposed ETSMC offers significant improvements in control performance and system resilience to external power disturbances and multivariate communication uncertainty events.

DNS root server resolution anomaly detection

The DNS root server is at the top of the hierarchical structure of the DNS system and is the initial node that bootstraps all DNS queries. If the root server resolves abnormally, all domain name resolutions will fail, and many users cannot access the Internet. For this reason, this paper detects the root server itself resolution anomalies and non-self resolution anomalies by constructing high-confidence root zone file and anomaly judgment rules. First, we use the weighted voting statistics method to build high-confidence root zone file by calculating the confidence of multi-source root zone files. Based on high-confidence root zone files, we construct three types of anomaly detection judgment rules: (1) root-side resolution anomaly judgment rules based on feature value matching, (2) response hijacking judgment rules by correlating response anomaly features and resolution routing information, (3) root zone file synchronization anomaly judgment rules by calculating the relative synchronization delay of multi-source root zone files. Finally, using three anomaly judgment rules, we perform anomaly detection on the root resolution data obtained by active measurement. Our detection results show that root zone file synchronization delay distributions of different root server instances vary greatly. Some instances even show minute-level convergence, resulting in incorrect resolution for some TLDs. We also detect one response hijacking incident for 2 TLDs resolution, caused by the domain takeover mechanism adopted by the ISP to reduce inter-domain traffic settlement and decrease resolution latency. Except for the unresponsive exception caused by network packet loss, no root-side resolution anomaly is found, indicating that there is no artificial manipulation of TLD resolution on the root server and reflecting the responsibility of each root server operator aiming to maintain global Internet interconnection. The detection results show that the detection rules proposed in this paper can effectively achieve the anomaly detection of root server resolution and help to maintain the health and stability of the DNS system.

A Novel Approach to Mitigating Packet Loss in Wireless Communication Networks through Machine Learning-Based Adaptive Error Correction

Wireless communication networks are integral to modern computing, facilitating seamless data transmission across diverse devices. Despite their importance, packet loss remains a persistent challenge, adversely affecting network reliability and performance. This research paper introduces a pioneering approach to tackle this longstanding issue, employing machine learning for adaptive error correction. The proposed method aims to significantly enhance the robustness and efficiency of wireless communication systems, presenting an innovative solution that has not been extensively explored in existing literature. Keywords—Wireless communication networks,Packet loss mitigation,Machine learning,Adaptive error correction ,Reinforcement learning,Dynamic decision-making,Real-time network conditions,Signal strength,Interference levels,Robustness,Throughput improvement,Latency reduction,Experimental setup,Performance metrics,Comparative analysis,Statistical significance,Sensitivity analysis,Ethical considerations,Practical implications,Future directions

Uncovering the Limitations and Insights of Packet Status Prediction Models in IEEE 802.15.4-Based Wireless Networks and Insights from Data Science

Wireless networks play a pivotal role in various domains, including industrial automation, autonomous vehicles, robotics, and mobile sensor networks. This research investigates the critical issue of packet loss in modern wireless networks and aims to identify the conditions within a network’s environment that lead to such losses. We propose a packet status prediction model for data packets that travel through a wireless network based on the IEEE 802.15.4 standard and are exposed to five different types of interference in a controlled experimentation environment. The proposed model focuses on the packetization process and its impact on network robustness. This study explores the challenges posed by packet loss, particularly in the context of interference, and puts forth the hypothesis that specific environmental conditions are linked to packet loss occurrences. The contribution of this work lies in advancing our understanding of the conditions leading to packet loss in wireless networks. Data are retrieved with a single CC2531 USB Dongle Packet Sniffer, whose pieces of information on packets become the features of each packet from which the classifier model will gather the training data with the aim of predicting whether a packet will unsuccessfully arrive at its destination. We found that interference causes more packet loss than that caused by various devices using a WiFi communication protocol simultaneously. In addition, we found that the most important predictors are network strength and packet size; low network strength tends to lead to more packet loss, especially for larger packets. This study contributes to the ongoing efforts to predict and mitigate packet loss, emphasizing the need for adaptive models in dynamic wireless environments.

SDN-Based Congestion Control and Bandwidth Allocation Scheme in 5G Networks

5G cellular networks are already more than six times faster than 4G networks, and their packet loss rate, especially in the Internet of Vehicles (IoV), can reach 0.5% in many cases, such as when there is high-speed movement or obstacles nearby. In such high bandwidth and high packet loss network environments, traditional congestion control algorithms, such as CUBIC and bottleneck bandwidth and round-trip propagation time (BBR), have been unable to balance flow fairness and high performance, and their flow rate often takes a long time to converge. We propose a congestion control algorithm based on bottleneck routing feedback using an in-network control mode called bottleneck routing feedback (BRF). We use SDN technology (OpenFlow protocol) to collect network bandwidth information, and BRF controls the data transmission rate of the sender. By adding the bandwidth information of the bottleneck in the option field in the ACK packet, considering the flow fairness and the flow convergence rate, a bandwidth allocation scheme compatible with multiple congestion control algorithms is proposed to ensure the fairness of all flows and make them converge faster. The performance of BRF is evaluated via Mininet. The experimental results show that BRF provides higher bandwidth utilization, faster convergence rate, and fairer bandwidth allocation than existing congestion control algorithms in 5G cellular networks.

Investigation of virtual reality multimedia interaction technology based on wireless network

<p>In recent years, with the continuous development of wireless communication and virtual reality technology, multimedia interaction technology has received more and more attention. However, due to the limitations of bandwidth, delay, packet loss, and other problems in wireless networks, multimedia interaction technology also faces many challenges. In this paper, the virtual reality multimedia interaction technology will be studied in combination with a wireless network, in which the virtual environment is first constructed. Then, the interaction is carried out through data transmission and user interaction, and then the existing virtual reality multimedia interaction is optimized by using a data compression algorithm. In order to test the effectiveness and performance of virtual reality multimedia interaction under different network environments and determine the optimal network environment, this paper compares the multimedia interaction effect under three network environments using a wireless network, mobile network, and Bluetooth network as the research object, with download speed, loading speed, image quality, smoothness and real-time as the test variables, and 10 VR software as the constraints. The research results indicated that, under the same other conditions, taking real-time performance as an example, the delay time and feedback speed of wireless networks were between 26 ms–37 ms and 105 KB/s–115 KB/s. The delay time and feedback speed of mobile networks (3G/4G/5G, generation) were between 66 ms–75 ms and 46 KB/s–55 KB/s, while the delay time and feedback speed of Bluetooth networks were between 120 ms–130 ms and 25 KB/s–35 KB/s; this indicated that VR multimedia interaction technology in the wireless network had better performance. Virtual reality multimedia interaction technology based on wireless networks is a promising high technology that can bring users a more realistic, vivid, and intuitive communication and entertainment experience. At the same time, it will expand the application scenarios and promote the development and progress of the technology.</p>

Recursive Quantization-based Event Triggered Observer for Networked Systems Under Network Congestion and Packet Loss

Recursive Quantization-based Event Triggered Observer for Networked Systems Under Network Congestion and Packet Loss

A new intelligent cross-domain routing method in SDN based on a proposed multiagent reinforcement learning algorithm

PurposeA cross-domain intelligent software-defined network (SDN) routing method based on a proposed multiagent deep reinforcement learning (MDRL) method is developed.Design/methodology/approachFirst, the network is divided into multiple subdomains managed by multiple local controllers, and the state information of each subdomain is flexibly obtained by the designed SDN multithreaded network measurement mechanism. Then, a cooperative communication module is designed to realize message transmission and message synchronization between the root and local controllers, and socket technology is used to ensure the reliability and stability of message transmission between multiple controllers to acquire global network state information in real time. Finally, after the optimal intradomain and interdomain routing paths are adaptively generated by the agents in the root and local controllers, a network traffic state prediction mechanism is designed to improve awareness of the cross-domain intelligent routing method and enable the generation of the optimal routing paths in the global network in real time.FindingsExperimental results show that the proposed cross-domain intelligent routing method can significantly improve the network throughput and reduce the network delay and packet loss rate compared to those of the Dijkstra and open shortest path first (OSPF) routing methods.Originality/valueMessage transmission and message synchronization for multicontroller interdomain routing in SDN have long adaptation times and slow convergence speeds, coupled with the shortcomings of traditional interdomain routing methods, such as cumbersome configuration and inflexible acquisition of network state information. These drawbacks make it difficult to obtain global state information about the network, and the optimal routing decision cannot be made in real time, affecting network performance. This paper proposes a cross-domain intelligent SDN routing method based on a proposed MDRL method. First, the network is divided into multiple subdomains managed by multiple local controllers, and the state information of each subdomain is flexibly obtained by the designed SDN multithreaded network measurement mechanism. Then, a cooperative communication module is designed to realize message transmission and message synchronization between root and local controllers, and socket technology is used to ensure the reliability and stability of message transmission between multiple controllers to realize the real-time acquisition of global network state information. Finally, after the optimal intradomain and interdomain routing paths are adaptively generated by the agents in the root and local controllers, a prediction mechanism for the network traffic state is designed to improve awareness of the cross-domain intelligent routing method and enable the generation of the optimal routing paths in the global network in real time. Experimental results show that the proposed cross-domain intelligent routing method can significantly improve the network throughput and reduce the network delay and packet loss rate compared to those of the Dijkstra and OSPF routing methods.

The Weighted Group Consensus for a Kind of Discrete Heterogeneous Multi-Agent Systems With Time Delay and Packet Loss in Cooperative-Competitive Networks Based on Self-Adaptive Controller

The Weighted Group Consensus for a Kind of Discrete Heterogeneous Multi-Agent Systems With Time Delay and Packet Loss in Cooperative-Competitive Networks Based on Self-Adaptive Controller

Telemedicine network latency management system in 5G telesurgery: a feasibility and effectiveness study.

Network latency is the most important factor affecting the performance of telemedicine. The aim of the study is to assess the feasibility and efficacy of a novel network latency management system in 5G telesurgery. We conducted 20 telesurgery simulation trials (hitching rings to columns) and 15 remote adrenalectomy procedures in the 5G network environment. Telemedicine Network Latency Management System and the traditional "Ping command" method (gold standard) were used to monitor network latency during preoperative simulated telesurgery and formal telesurgery. We observed the working status of the Telemedicine Network Latency Management System and calculated the difference between the network latency data and packet loss rate detected by the two methods. In addition, due to the lower latency of the 5G network, we tested the alert function of the system using the 4G network with relatively high network latency. The Telemedicine Network Latency Management System showed no instability during telesurgery simulation trials and formal telesurgery. After 20 telesurgery simulation trials and 15 remote adrenalectomy procedures, the p-value for the difference between the network latency data monitored by the Telemedicine Network Latency Management System and the "Ping command" method was greater than 0.05 in each case. Meanwhile, the surgeons reported that the Telemedicine Network Latency Management System had a friendly interface and was easy to operate. Besides, when the network latency exceeded a set threshold, a rapid alarm sounded in the system. The Telemedicine Network Latency Management System was simple and easy to operate, and it was feasible and effective to use it to monitor network latency in telesurgery. The system had an intuitive and concise interface, and its alarm function increased the safety of telesurgery. The system's own multidimensional working ability and information storage capacity will be more suitable for telemedicine work.

An efficient hexadecimal network flow watermark method for tracking attack traffic

Network flow watermark technology is a traffic marking technique that embeds watermark information into the characteristics of network flows to mark and trace attack flows generated by network attackers. However, with the development of network attack techniques, the time and number of packets required for network attacks have decreased. Existing network flow watermark technologies fail to balance watermark robustness and efficiency, resulting in poor practicality. To address this issue, this paper proposes an efficient hexadecimal network flow watermark method. The method introduces an efficient interval watermark algorithm and utilizes an interval synchronization algorithm to self-learn watermark parameters, thereby improving the encoding efficiency of the watermark. The design of watermark start and end markers ensures the practicality of network watermarks, enabling traceability and source attribution of attack flows in real network environments. The proposed method is experimentally tested using real network traffic, and the results demonstrate that even in the presence of a network jitter, the watermark detection success rate of this scheme remains above 95%. Compared to other network flow watermark schemes, the hexadecimal network flow watermark proposed in this paper achieves a 50% improvement in encoding and decoding efficiency while ensuring robustness. It also exhibits excellent resistance to network jitter, packet loss, and false packet insertion.