A study on improvement of internet traffic measurement and analysis using Hadoop system

Ibrahim, Lena T.; Hassan, Rosilah; Ahmad, Kamsuriah; Asat, Asrul Nizam

doi:10.1109/iceei.2015.7352545

Cited by 5 publications

(6 citation statements)

References 10 publications

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“…It thus allows traces to be processed without any previous transformation while avoiding performance penalties of reading files sequentially in the framework. A similar direction is followed by Ibrahim et al [56], who develop traffic analysis algorithms based on MapReduce and a new input API to explore pcap files on Hadoop.…”

Section: Data Storagementioning

confidence: 97%

A Survey on Big Data for Network Traffic Monitoring and Analysis

D’Alconzo

Drago

Morichetta

et al. 2019

IEEE Trans. Netw. Serv. Manage.

124

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Network Traffic Monitoring and Analysis (NTMA) represents a key component for network management, especially to guarantee the correct operation of large-scale networks such as the Internet. As the complexity of Internet services and the volume of traffic continue to increase, it becomes difficult to design scalable NTMA applications. Applications such as traffic classification and policing require real-time and scalable approaches. Anomaly detection and security mechanisms require to quickly identify and react to unpredictable events while processing millions of heterogeneous events. At last, the system has to collect, store, and process massive sets of historical data for post-mortem analysis. Those are precisely the challenges faced by general big data approaches: Volume, Velocity, Variety, and Veracity. This survey brings together NTMA and big data. We catalog previous work on NTMA that adopt big data approaches to understand to what extent the potential of big data is being explored in NTMA. This survey mainly focuses on approaches and technologies to manage the big NTMA data, additionally briefly discussing big data analytics (e.g., machine learning) for the sake of NTMA. Finally, we provide guidelines for future work, discussing lessons learned, and research directions.

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Section: Data Storagementioning

confidence: 97%

A Survey on Big Data for Network Traffic Monitoring and Analysis

D’Alconzo

Drago

Morichetta

et al. 2019

IEEE Trans. Netw. Serv. Manage.

124

View full text Add to dashboard Cite

show abstract

“…The ability allows organizations to preserve the value of data that was already considered as not useful [5]. There is various Internet-related work, but it is restricted to some Hadoop data without full coverage and gives you the ability to track and measure this significant data with MapReduce [6]. A previous study recommends some of the tips of Hadoop cluster optimization after the analyses of the existing problems in the Hadoop data processing platform [7].…”

Section: A Hadoop Overviewmentioning

confidence: 99%

Analysis of How Scalable Features in Hadoop / MapReduce by Internet Traffic Management

Jacob¹,

Tharwat²,

Fudzee³

et al. 2023

Int. J. Adv. Sci. Eng. Inf. Technol.

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Internet traffic monitoring is to measure and analyze the network bottlenecks to manage the online data are transferring processes efficiently. Various tools have been developed by using internet traffic measurement and internet traffic analysis tools, such as Hadoop. Activity measurement and adaptive examination represent the dynamics of information exchange. On the other hand, information exchange and dynamics measure movement in light of the system assets that can be accessed depending on the characteristics of the exchanged information. The main aim of this work is to apply scalable features of internet traffic measurement and analysis using Hadoop to understand the effects of these features on the speed of transferring data. This gives a new vision or opportunity to dynamically adapting the most suitable traffic measurement and analysis feature according to network capabilities and environment. This research employs Hadoop/Map Reduce as scalable internet traffic measurement and analysis tools. The simulation was conducted by using five personal computers; one as a server and four virtual computers as network nodes. Each computer has 2GB memory and 100GB storage. Five types of data segmentation are utilized 10 MB, 40MB, 64MB, 200MB, and500MB. The speed of the network is calculating in a megabit per second (Mbs) based upon the network speed on the number of allocated PCs (100 Mbs/4). The simulation is conducted to test the data transfer time based on various selections of network capabilities such as transferring extensive data through a network of medium and heavy usage.

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“…At present, research efforts have been devoted to explore Internet traffic measurement technology to identify abnormal network traffic . Many measurement results have revealed that network traffic has multifractal characteristics over small time scale.…”

Section: Related Workmentioning

confidence: 99%

“…At present, research efforts have been devoted to explore Internet traffic measurement technology to identify abnormal network traffic. [20][21][22] Many measurement results have revealed that network traffic has multifractal characteristics over small time scale. Riedi et al 23 found that the TCP trace is clearly multifractal with excellent scaling of the comparably large scaling region of 4 to 5 orders of magnitude.…”

Section: Related Workmentioning

confidence: 99%

Identifying LDoS attack traffic based on wavelet energy spectrum and combined neural network

Liu

Wang

2017

Int J Communication

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Summary As a special type of denial of service (DoS) attacks, the TCP‐targeted low‐rate denial of service (LDoS) attacks have the characteristics of low average rate and strong concealment, so it is difficult to identify such attack traffic. As multifractal characteristics exist in network traffic, a new identification approach based on wavelet transform and combined neural network is proposed to classify normal network traffic and LDoS attack traffic. Wavelet energy spectrum coefficients extracted from the sampled traffic are used for multifractal analysis of traffic over different time scale. The combined neural network is designed to classify these multiscale spectrum coefficients that show different multifractal characteristics belonging to normal network traffic and LDoS attack traffic. Test results of test‐bed experiments indicate that the proposed approach can identify LDoS attack traffic accurately.

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A study on improvement of internet traffic measurement and analysis using Hadoop system

Cited by 5 publications

References 10 publications

A Survey on Big Data for Network Traffic Monitoring and Analysis

A Survey on Big Data for Network Traffic Monitoring and Analysis

Analysis of How Scalable Features in Hadoop / MapReduce by Internet Traffic Management

Identifying LDoS attack traffic based on wavelet energy spectrum and combined neural network

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