Network Traffic Analysis using Big Data and Deep Learning Techniques

Boukhalfa, Alaeddine; Abdellaoui, Abderrahim; Hmina, Nabil; Chaoui, Habiba

doi:10.1109/icoa49421.2020.9094455

Cited by 2 publications

(2 citation statements)

References 18 publications

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“…Due to the large amount and variety of data that can be collected across the network, it has become difficult to process them with the old analysis methods and tools of security [17], contrariwise, Machine Learning methods have the capacity to extract information hidden in this large volume and variety of data [18], that's why we will use them to process network traffic. So, the analysis machine is also a machine on the local network, on which we have installed software that will launch Machine Learning algorithms, in order to process the data already stored in the Big Data cluster.…”

Section: Analysis Machinementioning

confidence: 99%

Parallel processing using big data and machine learning techniques for intrusion detection

Boukhalfa

Hmina

Chaoni

2020

IJ-AI

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Currently, information technology is used in all the life domains, multiple devices produce data and transfer them across the network, these transfers are not always secured, they can contain new menaces invisible by the current security devices. Moreover, the large amount and variety of the exchanged data cause difficulties related to the detection time. To solve these issues, we suggest in this paper, a new approach based on storing the large amount and variety of network traffic data employing Big Data techniques, and analyzing these data with Machine Learning algorithms, in a distributed and parallel way, in order to detect new hidden intrusions with less processing time. According to the results of the experiments, the detection accuracy of the Machine Learning methods reaches 99.9 %, and their processing time has been reduced considerably by applying them in a parallel and distributed way, which proves that our proposed model is effective for the detection of new intrusions.

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Section: Analysis Machinementioning

confidence: 99%

Parallel processing using big data and machine learning techniques for intrusion detection

Boukhalfa

Hmina

Chaoni

2020

IJ-AI

Self Cite

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“…These are new measures concerning the capture, research, sharing, storage, analysis and presentation of this data. As shown in Figure 3, Big Data is characterized by five characteristics called 5Vs [3]: -Volume: it means a large amount of data to process.…”

Section: Big Datamentioning

confidence: 99%

Survey on Security Monitoring and Intrusion Detection in the Big Data Environment

Boukhalfa¹

2020

IJATCSE

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Recently, new cities have appeared called smart cities (SC) that use electronic devices to manage and transfer information. These electronic devices are interconnected via a new generation of the internet called internet of things (IoT), and are generating and transferring continuously a large mass and variety of data between them, these data transfers can contain new intrusions. However, the current information security devices cannot identify these new intrusions, and have problems with the large amount and diversity of produced data. To solve these issues, new approaches have been proposed for security monitoring and intrusion detection in a Big Data environment. In this paper, we will give an overview on these proposals and analyze them, to identify their weaknesses in order to help researchers improve information security in the future.

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Network Traffic Analysis using Big Data and Deep Learning Techniques

Cited by 2 publications

References 18 publications

Parallel processing using big data and machine learning techniques for intrusion detection

Parallel processing using big data and machine learning techniques for intrusion detection

Survey on Security Monitoring and Intrusion Detection in the Big Data Environment

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