An Integrated Method for Application-level Internet Traffic Classification

Smart city networks involve many applications that impose specific Quality of Service (QoS) requirements, thus representing a challenging scenario for network management. Solutions aiming to guarantee QoS support have not been deployed in large-scale networks. Traffic classification is a mechanism used to manage different aspects, including QoS requirements. However, conventional traffic classification methods, such as the port-based method, are inefficient because of their inability to handle dynamic port allocation and encryption. Traffic classification using machine learning has gained research interest as an alternative method to achieve high performance. In fact, machine learning embeds intelligence into network functions, thus improving network management. In this study, we apply machine learning algorithms to predict network traffic classification. We apply four supervised learning algorithms: support vector machine, random forest, k-nearest neighbors, and decision tree. We also apply a port-based method of traffic classification based on applications’ popular assigned port numbers. Then, we compare the results of this method to those obtained from the machine learning algorithms. The evaluation results indicate that the decision tree algorithm provides the highest average accuracy among the evaluated algorithms, at 99.18%. Moreover, network traffic classification using machine learning provides more accurate results and higher performance than the port-based method.

This article discusses an important problem in network management and security—accurate and efficient classification of internet traffic. The need for real-time processing, as well as the complexity and everchanging nature of the network traffic, calls for both high accuracy and computational efficiency. To meet these demands, this study proposes a new approach that incorporates the Online Sequential Extreme Learning Machine (OSELM) and Wavelet Transform, which has been optimized by the Artificial Bee Cloning Algorithm. The proposed OSELM-Wavelet method uses Daubechies 4 wavelet transform to capture relevant frequency-domain features and, at the same time, preserve raw time-domain signals. This method improves the feature set by capturing time and frequency domain features simultaneously, boosting the input data by 800 features. Classification is carried out via the OSELM framework which supports efficient online training and inference and is thus suitable for real-time applications. The Artificial Bee Clony Algorithm is used to optimize the OSELM model parameters and improve the classification accuracy. This algorithm is inspired by the intelligent foraging behavior of bees and can effectively explore and exploit the parameter space for optimal solutions. The experiments bee algorithm-optimized OSELM-Wavelet conducted for internet traffic classification tasks demonstrated high accuracy and robustness.It outperforms conventional statistical and machine learning methods, particularly in situations demanding rapid adaptation and online learning. To summarize, the article combines OSELM-Wavelet with Artificial Bee Cloning Algorithm for Internet traffic classification, presenting a new solution. It aids in precisely and swiftly classifying traffic with minimal computation, thereby improving network security and management.

In an era of rapid urbanization, the quality of network services in urban environments is becoming increasingly important. This article explores efforts to improve network service quality in several sampang cities through an evaluation of Quality of Service (QoS) and user perceptions of Quality of Experience (QoE). The research is based on a survey conducted at various locations in sampang city to collect data on the reliability, speed, and availability of network services. The survey involved a diverse sample of users, covering a wide range of ages, professions, and levels of technology usage. In addition, we also analyzed users' perceptions of service quality based on their experiences of using the network in an urban environment. The results highlighted several key challenges in improving QoS, such as user density, signal interference, and network infrastructure limitations. However, we also found that strategies such as the use of advanced network technologies, such as 5G and beamforming, as well as traffic prioritization based on application type, can help to significantly improve QoS. In addition, we found a strong positive correlation between improved QoS and improved QoE, suggesting that improved quality of service has a direct impact on improving user experience. These findings provide valuable insights for network operators and policy makers in their efforts to improve network services in dense urban environments. The research also recommends further investment in network technologies and the development of policies that support more effective management of network traffic to ensure optimal quality of service.

Realistic network environments are commonly emulated by Internet application developers to test both the performance and the correctness of their applications. In this paper, we argue that emulating just realistic network environments may be inadequate for detecting hard-to-detect faults that have very low occurrence probabilities but potentially high impact if exposed in the real world. Specifically, we conduct extensive experiments to study the correctness testing capabilities of realistic and unlikely network environments. Our results show that small investments in emulating unlikely network environments may help in quickly detecting otherwise hard-to-detect faults.

Quality of Service (QoS) and Inter-domain traffic control are the current research topics in the fields of computer communication and IP Telephony in Local Area Network (LAN), Wide Area Network (WAN) and Wi-Fi network. By appropriate design and implementation of QoS and traffic control in a corporate network a significant amount of bandwidth and thereby money can be saved. Performance and reliability are also enhanced in this process. In the current QoS framework emphasis has only been put on layer 2 and layer 3, using traffic classification, scheduling and provisioning. Also, there is no effective means of measuring an agency's QoS implementation level. In this paper we have proposed a new QoS framework which includes QoS at all 7 layers of OSI reference model. We have also introduced a new technique, Quality of Service Quotient (QoSQ) for measuring an agency's QoS posture. We have implemented QoS in a NSW government agency following this new QoS framework and have provided ‘bandwidth’ usage data for both pre and post QoS implementation. The data shows considerable subscribed bandwidth savings (55%) and increased average bandwidth usage (30%).

With the exponential explosion of high traffic demand in wireless networks, the heterogeneous network (HetNet) is considered as one promoting method for improving the throughput of network and quality of service (QoS) of users. The HetNet consists of different cells, which operate at different transmit power. The small cells can be used to offload traffic from macro cells. Nevertheless, the uses of small cells also bring some problems. One problem is how efficiently an handover (HO) of small cells is performed in mobility management, when small cells are deployed in coverage area of a macro cell. In this condition, the various problems faced by users include frequent and unnecessary HO, ping-pong effect, which severely degrade the quality of traffic and users experience. Many existing works propose optimal solutions to improve the performance of long-term evolution (LTE) HetNets, which only consider the one of users’ speeds and traffic classifications. In this paper, we propose an optimal HO algorithm to meet the margin coverage problems. Our proposed algorithm considers the different conditions of user speed and traffic classification. Our proposed scheme avoids users with high speed in handing off to home-evolved nodeB (HeNB) and makes them limited to evolved nodeB (eNB). Simulation results show that our proposed algorithm improves the performance in in terms of the downlink throughput, physical downlink shared channel (PDSCH) utilization, system delay, voice end-to-end delay and voice jitter.

Computer networks transport simultaneously several flows, fact that makes necessary a multiplexing mechanism. Transport procedures affect the traffic flows; reason for which the traffic has to be characterized and quality of service (QoS) requirements need to be established. Traffic types and their QoS requirements impose the implementation of QoS methods and architectures. Several frameworks have been developed in order to improve the network services but each one has several disadvantages. This paper presents the architecture, design and implementation of a new end-to-end QoS framework with self-adaptive bandwidth reconfiguration.

Self-similarity is one of the most important characteristics of real network traffic. Constructing an accurate model can reflect the real network environment precisely. It is also an effective measure to predict the upcoming network traffic, thus ensuring the quality and reliability of network service. In this paper, we present a cluster-based distributed system, which is based on FGN(Fraction Gaussian Noise) algorithm. With the purpose to generate large-scale traffic and simulate the real network environment. We offer a detailed description for this system in the following three aspects: algorithm, architecture and implementation. Experiments have proved that the cluster-based system can generate large-scale similar network traffic with designated Hurst parameter

In this paper, we propose the China-US international data placement laboratory (iDPL) based on an inter-continental testbed for data placement research. iDPL is able to support various data placement research due to its scalability and flexibility in deploying the experiments in the real network environment. The core design of iDPL leverages reliable workflow management and lightweight I/O protocol to allow complex experiment setup and on-the-fly experiment deployment. It is also extensible to plugin different network profiling tools such as iperf. We expect the powerful measurement capability of iDPL promotes research study on the intelligent data placement policies which adapt to the uncertainty of the wide-area network and guarantee the quality of service (QoS) of the big data applications. As a case study, we setup a set of data placement experiments to measure the end-to-end network performance constantly among several sites between China and US using different data placement tools. The experiments have been running for more than one year, and its measurement data is public available (http://mickey.buaa.edu.cn:8080/). We believe the measurement data is valuable for both network and big data researchers to understand the performance disparity between the raw network and the actual data placement, which provides useful insights to design big data applications with performance awareness. We encourage more researchers to deploy their own data placement experiments on iDPL, expediting the research direction of intelligent data placement with real network environment.

Recently, the usage of Internet through mobile devices has surpassed the access through wired network. It makes extremely difficult for the network operators to manage their network in resource efficient manner. Software Defined Networking (SDN), a key 5G enabling technology, makes network management easier for the network operators through logically centralized control plane. One of the benefits of SDN is specific treatment for different network traffic type, however it requires an efficient traffic classification solution. Comprehensive research has been done on deep packet inspection or machine learning based traffic classification methods which are unsuitable for delay intolerant networks due much overhead. This paper proposes a simple traffic classification mechanism using different IP addresses which are assigned to Mobile Node (MN). Different applications in MN uses one of the assigned IP addresses based on the type of network traffic they generate. Centralized SDN controller uses these IP addresses to classify the network traffic. This method is useful for various network functions such as load balancing, security and mobility. In this paper we take mobility as a use case and through emulation results establish that traffic classification significantly improves MN mobility performance.

Quality of Service (QoS) management in home networks becomes a complex function when dealing with dynamic evolution of user requirements and network services. Autonomous management of QoS asks for decision models able to incorporate this evolving nature of requirements and services. In this paper, we propose an extensible framework that integrates a semantic space for user requirements and preferences aimed at guiding an autonomous QoS decision model. The use of this framework is illustrated in the context of QoS management in home networks.

Accurate network traffic classification is fundamental to numerous network activities, from quality of service to providing operators with useful forecasts for long-term provisioning. In this paper, we apply the discretization method in Naive Bayes for Internet traffic identification and compare the result with that of previously applied Naive Bayes kernel estimation in AUCKLAND VI and Entry data sets. Our results show that discretization is more robust and accurate than kernel estimation. The average accuracy is improved to 97.93% and outperforms the kernel estimation by up to 4.2% in Entry data sets. For AUCKLAND VI data sets, the average accuracy is improved to 90.37% from 34.17%. We also find that discretization method for Naive Bayes is more efficient than kernel method during classification.

The question of quality of service in networks of the latest generation is studied. The impact of heterogeneous traffic on the main quality of service indicators reflecting the probability of packet loss and delivery delay over time is evaluated. A model of quality of service in information communication networks has been developed to ensure low traffic delay. Taking into account the concept of quality in information communication networks, the issue of quality of service in networks of the latest generation becomes important from the standpoint of ensuring high reliability and reliability of information transmission, which significantly affects the quality of services provided to the user. This, of course, determines the need to control various parameters of equipment and communication lines, which are determined both by the features of the transmission system and by the parameters of leased channels and tracts. The basis for determining the quality of service, as mentioned above, is the provision of the international concept of quality of service (QoS). According to the requirements of the compliance control model proposed in the concept, the approach to quality indicators is structurally related to the OSI reference model. The main advantages of modern technologies of information communication networks are undoubtedly 5G technologies, which already support such resource-intensive services as massive machine-to-machine communication, ultra-reliable communication and low-latency communication. All of them require high data transfer rates, low millisecond latency, and connectivity of millions of devices. At the same time, it is necessary to ensure a high level of quality of service (QoS), which plays an important role in 5G technologies. The proposed model makes it possible to evaluate not only the quality of service of the combined traffic flow of Internet of Things, tactile Internet, and augmented reality services, but also the delivery delay and the probability of packet loss. The simulation results showed that the parameters of the quality of service of the traffic of the Internet of Things and other modern applications differ significantly during the service of the aggregate flow. When studying the service of aggregated traffic, it was established that the probability of packet loss is higher for it than for Internet of Things traffic. In addition, this difference becomes more significant when the intensity of the incoming load increases.

Network traffic classification is the process of analyzing traffic flows and associating them to different categories of network applications. Network traffic classification represents an essential task in the whole chain of network security. Some of the most important and widely spread applications of traffic classification are the ability to classify encrypted traffic, the identification of malicious traffic flows, and the enforcement of security policies on the use of different applications. Passively monitoring a network utilizing low-cost and low-complexity wireless local area network (WLAN) devices is desirable. Mobile devices can be used or existing office desktops can be temporarily utilized when their computational load is low. This reduces the burden on existing network hardware. The aim of this paper is to investigate traffic classification techniques for wireless communications. To aid with intrusion detection, the key goal is to passively monitor and classify different traffic types over WLAN to ensure that network security policies are adhered to. The classification of encrypted WLAN data poses some unique challenges not normally encountered in wired traffic. WLAN traffic is analyzed for features that are then used as an input to six different machine learning (ML) algorithms for traffic classification. One of these algorithms (a Gaussian mixture model incorporating a universal background model) has not been applied to wired or wireless network classification before. The authors also propose a ML algorithm that makes use of the well-known vector quantization algorithm in conjunction with a decision tree—referred to as a TRee Adaptive Parallel Vector Quantiser. This algorithm has a number of advantages over the other ML algorithms tested and is suited to wireless traffic classification. An average F-score (harmonic mean of precision and recall) > 0.84 was achieved when training and testing on the same day across six distinct traffic types.

Automatic traffic classification is increasingly important in networking due to the current trend of encrypting transport information (e.g., behind HTTP encrypted tunnels) which prevent intermediate nodes to access end-to-end transport headers. This paper proposes an architecture for supporting Quality of Service (QoS) in hybrid terrestrial and SATCOM networks based on automated traffic classification. Traffic profiles are constructed by machine-learning (ML) algorithms using the series of packet sizes and arrival times of QUIC connections. Thus, the proposed QoS method does not require explicit setup of a path (i.e. it provides soft QoS), but employs agents within the network to verify that flows conform to a given traffic profile. Results over a range of ML models encourage integrating ML technology in SATCOM equipment. The availability of higher computation power at low-cost creates the fertile ground for implementation of these techniques.