scholarly journals Multistep-ahead neural-network predictors for network traffic reduction in distributed interactive applications

2007 ◽  
Vol 17 (4) ◽  
pp. 16 ◽  
Author(s):  
Aaron Mccoy ◽  
Tomas Ward ◽  
Seamus Mcloone ◽  
Declan Delaney
Keyword(s):  
Neural Network ◽  
Network Traffic ◽  
Interactive Applications ◽  
Traffic Reduction

scholarly journals Attention based multi-component spatiotemporal cross-domain neural network model for wireless cellular network traffic prediction

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Qingtian Zeng ◽  
Qiang Sun ◽  
Geng Chen ◽  
Hua Duan
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Network Traffic ◽  
Cellular Network ◽  
Critical Issue ◽  
Traffic Prediction ◽  
Weekly Cycle ◽  
Cross Domain ◽  
Cellular Traffic

AbstractWireless cellular traffic prediction is a critical issue for researchers and practitioners in the 5G/B5G field. However, it is very challenging since the wireless cellular traffic usually shows high nonlinearities and complex patterns. Most existing wireless cellular traffic prediction methods lack the abilities of modeling the dynamic spatial–temporal correlations of wireless cellular traffic data, thus cannot yield satisfactory prediction results. In order to improve the accuracy of 5G/B5G cellular network traffic prediction, an attention-based multi-component spatiotemporal cross-domain neural network model (att-MCSTCNet) is proposed, which uses Conv-LSTM or Conv-GRU for neighbor data, daily cycle data, and weekly cycle data modeling, and then assigns different weights to the three kinds of feature data through the attention layer, improves their feature extraction ability, and suppresses the feature information that interferes with the prediction time. Finally, the model is combined with timestamp feature embedding, multiple cross-domain data fusion, and jointly with other models to assist the model in traffic prediction. Experimental results show that compared with the existing models, the prediction performance of the proposed model is better. Among them, the RMSE performance of the att-MCSTCNet (Conv-LSTM) model on Sms, Call, and Internet datasets is improved by 13.70 ~ 54.96%, 10.50 ~ 28.15%, and 35.85 ~ 100.23%, respectively, compared with other existing models. The RMSE performance of the att-MCSTCNet (Conv-GRU) model on Sms, Call, and Internet datasets is about 14.56 ~ 55.82%, 12.24 ~ 29.89%, and 38.79 ~ 103.17% higher than other existing models, respectively.

scholarly journals Real-Time Detection of Dictionary DGA Network Traffic Using Deep Learning

2021 ◽  
Vol 2 (2) ◽  
Author(s):  
Kate Highnam ◽  
Domenic Puzio ◽  
Song Luo ◽  
Nicholas R. Jennings
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Real Time ◽  
Network Traffic ◽  
Short Term Memory ◽  
Domain Names ◽  
Control Networks ◽  
Detection Techniques ◽  
Lstm Network ◽  
And Control

AbstractBotnets and malware continue to avoid detection by static rule engines when using domain generation algorithms (DGAs) for callouts to unique, dynamically generated web addresses. Common DGA detection techniques fail to reliably detect DGA variants that combine random dictionary words to create domain names that closely mirror legitimate domains. To combat this, we created a novel hybrid neural network, Bilbo the “bagging” model, that analyses domains and scores the likelihood they are generated by such algorithms and therefore are potentially malicious. Bilbo is the first parallel usage of a convolutional neural network (CNN) and a long short-term memory (LSTM) network for DGA detection. Our unique architecture is found to be the most consistent in performance in terms of AUC, $$F_1$$ F 1 score, and accuracy when generalising across different dictionary DGA classification tasks compared to current state-of-the-art deep learning architectures. We validate using reverse-engineered dictionary DGA domains and detail our real-time implementation strategy for scoring real-world network logs within a large enterprise. In 4 h of actual network traffic, the model discovered at least five potential command-and-control networks that commercial vendor tools did not flag.

scholarly journals LA-GRU: Building Combined Intrusion Detection Model Based on Imbalanced Learning and Gated Recurrent Unit Neural Network

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Binghao Yan ◽  
Guodong Han
Keyword(s):  
Neural Network ◽  
Intrusion Detection ◽  
False Alarm ◽  
False Alarm Rate ◽  
Network Traffic ◽  
Detection Performance ◽  
Imbalanced Learning ◽  
Traffic Distribution ◽  
Proposed Model ◽  
Gated Recurrent Unit

The intrusion detection models (IDMs) based on machine learning play a vital role in the security protection of the network environment, and, by learning the characteristics of the network traffic, these IDMs can divide the network traffic into normal behavior or attack behavior automatically. However, existing IDMs cannot solve the imbalance of traffic distribution, while ignoring the temporal relationship within traffic, which result in the reduction of the detection performance of the IDM and increase the false alarm rate, especially for low-frequency attacks. So, in this paper, we propose a new combined IDM called LA-GRU based on a novel imbalanced learning method and gated recurrent unit (GRU) neural network. In the proposed model, a modified local adaptive synthetic minority oversampling technique (LA-SMOTE) algorithm is provided to handle imbalanced traffic, and then the GRU neural network based on deep learning theory is used to implement the anomaly detection of traffic. The experimental results evaluated on the NSL-KDD dataset confirm that, compared with the existing state-of-the-art IDMs, the proposed model not only obtains excellent overall detection performance with a low false alarm rate but also more effectively solves the learning problem of imbalanced traffic distribution.

scholarly journals Robot Communication: Network Traffic Classification Based on Deep Neural Network

2021 ◽  
Vol 15 ◽  
Author(s):  
Mengmeng Ge ◽  
Xiangzhan Yu ◽  
Likun Liu
Keyword(s):  
Neural Network ◽  
Network Traffic ◽  
Short Term Memory ◽  
Traffic Classification ◽  
Classification Framework ◽  
Network Traffic Classification ◽  
Scalar Input ◽  
Memory Network ◽  
Encrypted Traffic ◽  
Robot Communication

With the rapid popularization of robots, the risks brought by robot communication have also attracted the attention of researchers. Because current traffic classification methods based on plaintext cannot classify encrypted traffic, other methods based on statistical analysis require manual extraction of features. This paper proposes (i) a traffic classification framework based on a capsule neural network. This method has a multilayer neural network that can automatically learn the characteristics of the data stream. It uses capsule vectors instead of a single scalar input to effectively classify encrypted network traffic. (ii) For different network structures, a classification network structure combining convolution neural network and long short-term memory network is proposed. This structure has the characteristics of learning network traffic time and space characteristics. Experimental results show that the network model can classify encrypted traffic and does not require manual feature extraction. And on the basis of the previous tool, the recognition accuracy rate has increased by 8%

scholarly journals Lightweight Convolutional Neural Network Based Intrusion Detection System

2020 ◽  
pp. 808-817
Author(s):  
Vinh Pham ◽  
◽  
Eunil Seo ◽  
Tai-Myoung Chung
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Intrusion Detection ◽  
Convolutional Neural Network ◽  
Network Traffic ◽  
Detection System ◽  
Image Data ◽  
Training Data ◽  
Deep Packet Inspection ◽  
Detection Model

Identifying threats contained within encrypted network traffic poses a great challenge to Intrusion Detection Systems (IDS). Because traditional approaches like deep packet inspection could not operate on encrypted network traffic, machine learning-based IDS is a promising solution. However, machine learning-based IDS requires enormous amounts of statistical data based on network traffic flow as input data and also demands high computing power for processing, but is slow in detecting intrusions. We propose a lightweight IDS that transforms raw network traffic into representation images. We begin by inspecting the characteristics of malicious network traffic of the CSE-CIC-IDS2018 dataset. We then adapt methods for effectively representing those characteristics into image data. A Convolutional Neural Network (CNN) based detection model is used to identify malicious traffic underlying within image data. To demonstrate the feasibility of the proposed lightweight IDS, we conduct three simulations on two datasets that contain encrypted traffic with current network attack scenarios. The experiment results show that our proposed IDS is capable of achieving 95% accuracy with a reasonable detection time while requiring relatively small size training data.

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