A Honey Net, Big Data and RNN Architecture for Automatic Security Monitoring of Information System

Boukhalfa, Alaeddine; Hmina, Nabil; Chaoui, Habiba

doi:10.1007/978-3-030-11928-7_72

Cited by 3 publications

(2 citation statements)

References 13 publications

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“…Because of the large amount and variety of traffic data exchanged all the time between the local network and the Internet, we have set up a Big Data cluster. The two most used big data management frameworks are Hadoop [14] and Spark [8], they are composed of two components, the first called Hadoop distributed file system (HDFS) is reserved for storing data, the second is reserved for distributed processing of data via the MapReduce program [15]. We used Hadoop because it is more powerful than Spark in terms of data security [16].…”

Section: Big Data Clustermentioning

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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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: Big Data Clustermentioning

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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“…Nowadays, the recurrent neural network (RNN) has gained a lot of attention due to its outstanding performance in solving real-world machine learning problems, especially when it comes to dealing with sequential data and input-output data having different lengths [1][2][3][4][5][6][7][8][9][10][11][12]. Alex Graves in "Supervised Sequence Labelling with Recurrent Neural Networks" shows that RNNs are very powerful sequential learners [13].…”

Section: Introductionmentioning

confidence: 99%

A Review of Recurrent Neural Network Architecture for Sequence Learning: Comparison between LSTM and GRU

S¹,

Nosouhian²,

A³

2021

Preprint

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Deep neural networks (DNNs) have made a huge impact in the field of machine learning by providing unbeatable humanlike performance to solve real-world problems such as image processing and natural language processing (NLP). Convolutional neural network (CNN) and recurrent neural network (RNN) are two typical architectures that are widely used to solve such problems. Time sequence-dependent problems are generally very challenging, and RNN architectures have made an enormous improvement in a wide range of machine learning problems with sequential input involved. In this paper, different types of RNN architectures are compared. Special focus is put on two well-known gated-RNN’s Long Term Short Memory (LSTM) and Gated Recurrent Unit (GRU). We evaluated these models on the task of force estimation system in pouring. In this study, four different models including multi-layers LSTM, multi-layers GRU, single-layer LSTM and single-layer GRU) were created and trained. The result suggests that multi-layer GRU outperformed other three models.

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Network Traffic Analysis using Big Data and Deep Learning Techniques

Boukhalfa

Abdellaoui

Hmina

et al. 2020

2020 IEEE 6th International Conference on Optimization and Applications (ICOA)

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A Honey Net, Big Data and RNN Architecture for Automatic Security Monitoring of Information System

Cited by 3 publications

References 13 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

A Review of Recurrent Neural Network Architecture for Sequence Learning: Comparison between LSTM and GRU

Network Traffic Analysis using Big Data and Deep Learning Techniques

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