A GPU-accelerated parallel network traffic analysis system

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.

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

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 desire to conceptualize network traffic in a prevailing communication network is a facet for many types of network research studies. In this research, real traffic traces collected over trans-Pacific backbone links (the MAWI repository, providing publicly available anonymized traces) are analyzed to study the underlying traffic patterns. All data analysis and visualization is carried out using Matlab (Matlab is a trademark of The Mathworks, Inc.). At packet level, we first measure parameters such as distribution of packet lengths, distribution of protocol types, and then fit following analytical models. Next, the concept of flow is introduced and flow based analysis is studied. We consider flow related parameters such as top ports seen, duration of the flow, distribution of flow lengths, and number of flows with different timeout values and provide analytical models to fit the flow lengths. Further, we study the amount of data flowing between source-destination pairs. Finally, we focus on TCP-specific aspects of captured traces such as retransmissions and packet round-trip times. From the results obtained, we infer the Zipf-type nature of distribution for number of flows, heavy-tailness of flow sizes and the contribution of well-known ports at packet and flow level. Our study helps a network analyst to farther the knowledge and helps optimize the network resources, while performing efficient traffic engineering.

With the continuous development of data mining technology, to apply the data mining techniques to transportation sector will provide service to transportation scientifically and reasonably. In intelligent transportation, the analysis of traffic flow data is very important, how to analyze the traffic data intelligently is more difficult problem, so using a new data mining techniques to replace the traditional data analysis and interpretation methods is very necessary and meaningful, clustering algorithm is the collection of physical or abstracting objects into groups of similar objects from the multiple classes of processes. This paper describes all kinds of the data mining clustering algorithms, clustering algorithm is proposed in the method of dealing with traffic flow data, and applied to the actual traffic data processing, and finally the clustering algorithm is applied to each of highway toll station Various types of car traffic volume data analysis.

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.

Modern networks generate a massive amount of traffic data streams. Analyzing this data is essential for various purposes, such as network resources management and cyber-security analysis. There is an urgent need for data analytic methods that can perform network data processing in an online manner based on the arrival of new data. Online machine learning (OL) techniques promise to support such type of data analytics. In this paper, we investigate and compare the OL techniques that facilitate data stream analytics in the networking domain. We also investigate the importance of traffic data analytics and highlight the advantages of online learning in this regard, as well as the challenges associated with OL-based network traffic stream analysis, e.g., concept drift and the imbalanced classes. We review the data stream processing tools and frameworks that can be used to process such data online or on-the-fly along with their pros and cons, and their integrability with de facto data processing frameworks. To explore the performance of OL techniques, we conduct an empirical evaluation on the performance of different ensemble- and tree-based algorithms for network traffic classification. Finally, the open issues and the future directions in analyzing traffic data streams are presented. This technical study presents valuable insights and outlook for the network research community when dealing with the requirements and purposes of online data streams analytics and learning in the networking domain.

Formalizing computational intensity of big traffic data understanding and analysis for parallel computing

With the exponential increase in the urban population, urban transportation systems are confronted with numerous challenges. Traffic congestion is common, traffic accidents happen frequently, and traffic environments are deteriorating. To alleviate these issues and improve the efficiency of urban transportation, accurate traffic forecasting is crucial. In this study, we aim to provide a comprehensive overview of the overall architecture of traffic forecasting, covering aspects such as traffic data analysis, traffic data modeling, and traffic forecasting applications. We begin by introducing existing traffic forecasting surveys and preliminaries. Next, we delve into traffic data analysis from traffic data collection, traffic data formats, and traffic data characteristics. Additionally, we summarize traffic data modeling from spatial representation, temporal representation, and spatio-temporal representation. Furthermore, we discuss the application of traffic forecasting, including traffic flow forecasting, traffic speed forecasting, traffic demand forecasting, and other hybrid traffic forecasting. To support future research in this field, we also provide information on open datasets, source resources, challenges, and potential research directions. As far as we know, this paper represents the first comprehensive survey that focuses specifically on the overall architecture of traffic forecasting.

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.

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.

- 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

With the advent of the era of traffic information technology, traffic data appears to be extremely large, and the traditional way of managing highway crashes has become difficult to adapt to the many implications of the complicated data. In this paper, we design a highway traffic safety assessment system based on crash data mining and modeling. By constructing a visualized highway traffic crash data analysis and mining platform, which is used as a basis to gather traffic crash analysis solutions and build a think tank, a safety assessment system is formed that integrates traffic crash information visualization, crash data analysis and mining technology and comprehensive solutions for crash risk prediction. The system provides crash data pre-processing solutions, crash pattern mining solutions, factor analysis solutions and crash risk prediction solutions. Based on the above solution, the basic framework and seven main functional modules of the highway traffic safety assessment system are designed and implemented. The system achieves systematic and automated data processing, pattern mining, factor analysis and risk prediction, and aims to make highway traffic safety management more efficient.

Network Traffic Analysis (NTA) helps identify security threats, monitor network performance, and plan for future capacity. While real-time analysis is ideal, it can be difficult due to high data volume and complexity. Large amounts of traffic require parsing, and real-time data may miss hidden threats. Post-analysis can address these challenges. It hardly depends on choosing an effective and appropriate storage solutions. A variety of storage systems exist, each employing different approaches and formats to retain data. This article explores the applications of various storage systems for NTA results. Three different types of storage systems considered, including Greenplum, Nebula graph and OpenSearch. A comparative approach is employed, analyzing the same dataset across various storage systems.This allows to examine how different database structures and query capabilities influence the efficiency and accuracy of NTA. The resulting insights will not only provide valuable guidance for selecting the optimal storage solution for specific NTA tasks, but also serve as a foundation for future research in this area.