Network Traffic Monitoring and Analysis

The lack of current network dynamics studies that evaluate the effects of new application and protocol deployment or long-term studies that observe the effect of incremental changes on the Internet, and the change in the overall stability of the Internet under various conditions and threats has made network monitoring challenging. A good understanding of the nature and type of network traffic is the key to solving congestion problems. In this paper we describe the architecture and implementation of a scalable network traffic moni-toring and analysis system. The gigabit interface on the monitoring system was configured to capture network traffic and the Multi Router Traffic Grapher (MRTG) and Webalizer produces graphical and detailed traffic analysis. This system is in use at the Obafemi Awolowo University, IleIfe, Nigeria; we describe how this system can be replicated in another environment.

This chapter covers the foundations of Network Traffic Monitoring and Analysis (NTMA) by providing a thorough review of its fundamental ideas while highlighting the crucial part it plays in evaluating network activity and performance. Coupled with the massive influx of users onto our networks, activities such as web browsing and video streaming have gained increasing popularity, further necessitating measures and standards for maintaining the quality of service (QoS) of network environments. It is thus crucial to forecast network traffic parameters, such as upload and download rates, that have a direct influence on QoS. This is followed by exposure to cutting-edge NTMA approaches currently being used by worldwide organizations and major telecommunications operators. Moreover, an overview of NTMA approaches for categorizing and forecasting network traffic is also given. This chapter thus focuses on the theoretical aspects of the framework upon which the NTMA proceedings in this book are based.

Wildcard rules are implemented in various important networking scenarios, including QoS, firewall, access control, and network traffic monitoring and analysis. However, there are cross-rule dependencies between wildcard rules, which both increase significant overhead and affect the semantic correctness of packet classification when caching rules. Considerable efforts have been made to mitigate the impacts of the dependency issue in rule caching, but it is still a bottleneck for cache systems. In this paper, we show how to give applications the flexibility of completely dependency-free wildcard rule caching by decoupling the cached rules and their dependent rules. Our FreeCache scheme has wide applicability to packet classification devices with wildcard rule caching. We validate the effectiveness of FreeCache through two respects: (1) Implementing various cache algorithms (e.g., LSTM) and cache replacement algorithms (e.g., ARC, LIRS) that are difficult to use in dependency-bound situations in the cache system with FreeCache. (2) Developing a prototype in a Software-Defined Network (SDN), where hybrid OpenFlow switches use TCAM as cache and RAM as auxiliary memory. Our experimental results reveal that FreeCache improves the cache performance by up to 60.88% in the offline scenario. FreeCache also offers the promise of applying any existing caching algorithms to wildcard rule caching while guaranteeing the properties of semantic correctness and equivalence.

At Fermilab, we have prototyped a GPU-accelerated network performance monitoring system, called G-NetMon, to support large-scale scientific collaborations. In this work, we explore new opportunities in network traffic monitoring and analysis with GPUs. Our system exploits the data parallelism that exists within network flow data to provide fast analysis of bulk data movement between Fermilab and collaboration sites. Experiments demonstrate that our G-NetMon can rapidly detect sub-optimal bulk data movements.

The problem of monitoring and characterizing network traffic arises in the context of a variety of network management functions. For example, consider the five functions defined in the OSI Network Management Framework [20.1], i.e., configuration management, performance management, fault management, accounting management and security management. Traffic monitoring is used in configuration management for tasks such as estimating the traffic demands between different points in the network, so that network capacity can be allocated to these demands. In performance management, traffic monitoring can be used to determine whether the measured traffic levels exceed the allocated network capacity, thus causing congestion or delays. When a fault occurs in the network, traffic monitoring is used in fault management to help locate the source of the fault, based on changes in the traffic levels through the surrounding network elements. In accounting management, traffic monitoring is needed to measure the network usage by each customer, so that costs can be charged accordingly in terms of the volume and type of traffic generated. Finally, network traffic monitoring can be used in security management to identify unusual traffic flows, which may be caused by a denial-of-service attack or other forms of misuse.

Netflow-based network traffic analysis is one of today's mainstream network traffic monitoring and analysis techniques. However, port-based traffic identification that employed in Netflow-based protocol analysis is inaccurate. Netflow cannot provide enough details for analysis of site access behaviour. In order to overcome the above limitation, we propose an enhanced Netflow data collection method combining packet capturing and flow technique for accurate application identification. Then we design and implement an enhanced Netflow data collection system, which lays a data foundation for traffic analysis. Finally, the feasibility and effectiveness of the system are verified.

NTLFlowLyzer: Towards generating an intrusion detection dataset and intruders behavior profiling through network and transport layers traffic analysis and pattern extraction

With the rapid increasing of network scale, the size of traffic data also expands a lot. In traditional traffic data analysis, there are some problems, such as high computation complexity, low analysis efficiency, long learning period, and difficulty of development. To address these problems, we design and implement a GPU-accelerated parallel analysis scheme for network traffic - EasyAnalyze. In EasyAnalyze, we introduce GPU parallel computing, Map/Reduce architecture into network traffic analysis, which greatly improves the efficiency but does not increase the difficulty in programming. In the experiments, EasyAnalyze shows very promising results: 1 the speed is 6-17 times faster than conventional serial analysis in network traffic data analysis; and 2 the size of code is only 2% of the mainstream GPU Map/Reduce.

SUMMARYNetwork traffic monitoring and analysis are essential for effective network operation and resource management. In particular, multidimensional analysis for long‐term traffic data is necessary for comprehensive understanding of the traffic trend and effective quality‐of‐service provision considering the extremely dynamic behavior of the current Internet, where various types of traffic occur from high‐speed network links and greatly increasing number of applications. However, only limited analysis results are provided, as the existing network traffic analysis tools and systems are developed and deployed focusing on their own specialized analysis purposes. Consequently, it is difficult to understand the network comprehensively and deeply, which increases the necessity for multilateral analysis of long‐term traffic data. In this paper, we propose a novel traffic analysis model for large volumes of Internet traffic accumulated over a long period of time. TheNetCube, the proposed network traffic analysis model using online analytical processing (OLAP) on a multidimensional data cube, provides an easy and fast way to construct a multidimensional traffic analysis system for comprehensive and detailed analysis of long‐term traffic data by utilizing simple OLAP operations and powerful data‐mining techniques on various abstraction levels of traffic data to complete the analysis purpose. We validate the feasibility and applicability of the proposedNetCubetraffic analysis model by implementing a traffic analysis system and applying it to our campus network. Copyright © 2012 John Wiley & Sons, Ltd.

Big data analysis has become much popular in the present day scenario and the manipulation of big data has gained the keen attention of researchers in the field of data analytics. Analysis of big data is currently considered as an integral part of many computational and statistical departments. As a result, novel approaches in data analysis are evolving on a daily basis. Thousands of transaction requests are handled and processed everyday by different websites associated with e-commerce, e-banking, e-shopping carts etc. The network traffic and weblog analysis comes to play a crucial role in such situations where Hadoop can be suggested as an efficient solution for processing the Netflow data collected from switches as well as website access-logs during fixed intervals.

Address Resolution Protocol (ARP) is a communication protocol to map the computer's addresses to the Media Access Control (MAC) address. In its implementation, ARP is abused, known as ARP poisoning Attack. The impact of ARP poisoning attacks is a deadlock to communicate on the network, identity fraud from addressing a computer through illegal access to steal important and confidential information. Several ARP poisoning attack detection models have been introduced. Still, they depend on application tools requiring complex configuration and mostly state ARP poisoning attacks as normal activity. In this paper, a model for detecting ARP poisoning attacks is proposed using the K-NN classification. The proposed model has a contribution to the feature extraction process based on network traffic flows analysis. The results show that the proposed model can detect ARP poisoning attacks more accurately than some classification algorithms with a TPR value of 97.67% and a detection accuracy of 98.7%.

The customized fifth generation (5G) private net-work is one of the most promising solutions to deal with the challenges of various flexible and differentiated application demands. Before customizing the 5G private network, network traffic analysis is necessary to be conducted. This article first introduces three typical 5G private network deployment modes and the typical application scenarios. Due to the correlation between spatial traffic and temporal traffic in 5G private net-works, characterizing both spatial randomness and temporal randomness is challenging. Thus, a spatiotemporal traffic analytical framework based on stochastic geometry and queueing theory is introduced in this article. This spatiotemporal traffic analytical framework helps guide the traffic analysis of 5G private networks and the deployment of 5G private networks. A case study of the spatiotemporal traffic analysis on a 5G private network is illustrated, where the metrics such as successful transmission probability, data transmission rate, and time delay are analyzed.

High-speed network poses challenges on heavy traffic measurement and analysis, which becomes more serious due to the scale of modern data centers. Cloud platform is an alternative to crack the nut because it gives concurrent considerations to both high-performance computing and high-extensible data storage. Therefore, we use Hadoop to construct a trial cloud platform to tackle the challenges derived from the heavy traffic, and perform network traffic analysis and target packet dissection. Our testbed evaluates the performance against different processing strategies, and shows the statistic results. According to the experimental results, the Hadoop-based platform can fit to process massive traffic data very well, and it's appropriate to manage today's data centers with cloud computing services.

The evolution of modern data centers from traditional hardware-based infrastructures to highly virtualized environments has introduced new complexities in network traffic analysis. Virtual networks, characterized by dynamic changes in both overlay and underlay architectures, necessitate sophisticated methods for accurate anomaly detection and network analysis. This paper investigates the real-world behaviour of network traffic within virtualized environments, identifying the key factors that impact packet dynamics, including VM operations, multi-tenancy, user customization, and hardware adjustments. By defining the frequency and nature of these events, this research provides further details for the creation of accurate packet generation tools. These tools must simulate the dynamic characteristics of virtual networks, enabling reliable and realistic synthetic data generation. The research explains the need for enhanced packet generation methodologies to provide valid training and testing data for digital investigations, anomaly detection, and network simulations. As a result, this paper emphasizes the importance of developing accurate synthetic network traffic that mirrors real-world conditions and provides a valid basement for traffic analysis in virtual networks.

- Performance and Secuity analysis is essential in the communication systems in the digital world we live in today. This paper discusses the creation of a Network Traffic Tracer, a network system written in Python with integrated Wireshark for capturing packets and performing automated traffic analysis. It employs Scapy and PyShark libraries to capture, f ilter, and process network packets to analyze crucial data about the traffic patterns, unusual activities, and possible security threats. Furthermore, it uses GeoLiteCity IP address geolocation database to enable visual mapping of suspicious network activities all over the world. Such visualization is plenty in identifying strange patterns such as unauthorized invasion or flooding the network which is known as Distributed Denial of Service (DDoS). Furthermore, the system supports real-time monitoring along with statistical report generation for traffic anomaly detection to increase the level of secuity and optimize performance of the network. In this paper, we show how effective the combination of automation in Python and traditional network analysis methods is in addressing issues pertaining to security and monitoring of networks.Keywords: Network Traffic Analysis, Python, Wireshark, Cybersecurity, Scapy, PyShark, Anomaly Detection. Keywords: Network Traffic Analysis, Packet Capture, Python, Wireshark, Scapy, PyShark, Cybersecurity, GeoIP, Anomaly Detection