scholarly journals Call Details Record Analysis: A Spatiotemporal Exploration toward Mobile Traffic Classification and Optimization

Information ◽  
2019 ◽  
Vol 10 (6) ◽  
pp. 192 ◽  
Author(s):  
Kashif Sultan ◽  
Hazrat Ali ◽  
Adeel Ahmad ◽  
Zhongshan Zhang
Keyword(s):  
Mobile Networks ◽  
Network Traffic ◽  
Spatiotemporal Analysis ◽  
Traffic Classification ◽  
Traffic Patterns ◽  
Machine Learning Technique ◽  
Proposed Model ◽  
Learning Technique ◽  
Effective Network ◽  
Operational Efficacy

The information contained within Call Details records (CDRs) of mobile networks can be used to study the operational efficacy of cellular networks and behavioural pattern of mobile subscribers. In this study, we extract actionable insights from the CDR data and show that there exists a strong spatiotemporal predictability in real network traffic patterns. This knowledge can be leveraged by the mobile operators for effective network planning such as resource management and optimization. Motivated by this, we perform the spatiotemporal analysis of CDR data publicly available from Telecom Italia. Thus, on the basis of spatiotemporal insights, we propose a framework for mobile traffic classification. Experimental results show that the proposed model based on machine learning technique is able to accurately model and classify the network traffic patterns. Furthermore, we demonstrate the application of such insights for resource optimisation.

scholarly journals N-Gram, Semantic-Based Neural Network for Mobile Malware Network Traffic Detection

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Huiwen Bai ◽  
Guangjie Liu ◽  
Weiwei Liu ◽  
Yingxue Quan ◽  
Shuhua Huang
Keyword(s):  
Neural Network ◽  
Mobile Networks ◽  
Network Traffic ◽  
Recurrent Network ◽  
Application Layer ◽  
Proposed Model ◽  
Mobile Malware ◽  
Series Of Experiments ◽  
N Gram ◽  
Traffic Detection

Mobile malware poses a great challenge to mobile devices and mobile communication. With the explosive growth of mobile networks, it is significant to detect mobile malware for mobile security. Since most mobile malware relies on the networks to coordinate operations, steal information, or launch attacks, evading network monitor is difficult for the mobile malware. In this paper, we present an N-gram, semantic-based neural modeling method to detect the network traffic generated by the mobile malware. In the proposed scheme, we segment the network traffic into flows and extract the application layer payload from each packet. Then, the generated flow payload data are converted into the text form as the input of the proposed model. Each flow text consists of several domains with 20 words. The proposed scheme models the domain representation using convolutional neural network with multiwidth kernels from each domain. Afterward, relationships of domains are adaptively encoded in flow representation using gated recurrent network and then the classification result is obtained from an attention layer. A series of experiments have been conducted to verify the effectiveness of our proposed scheme. In addition, to compare with the state-of-the-art methods, several comparative experiments also are conducted. The experiment results depict that our proposed scheme is better in terms of accuracy.

scholarly journals Improving the network traffic classification using the Packet Vision approach

2020 ◽  
Author(s):  
Rodrigo Moreira ◽  
Larissa Rodrigues ◽  
Pedro Rosa ◽  
Flávio Silva
Keyword(s):  
Mobile Networks ◽  
Network Traffic ◽  
State Of The Art ◽  
Security And Privacy ◽  
Network Services ◽  
Network Architectures ◽  
Traffic Classification ◽  
Raw Data ◽  
Network Traffic Classification ◽  
Application Aware

The network traffic classification allows improving the management, and the network services offer taking into account the kind of application. The future network architectures, mainly mobile networks, foresee intelligent mechanisms in their architectural frameworks to deliver application-aware network requirements. The potential of convolutional neural networks capabilities, widely exploited in several contexts, can be used in network traffic classification. Thus, it is necessary to develop methods based on the content of packets transforming it into a suitable input for CNN technologies. Hence, we implemented and evaluated the Packet Vision, a method capable of building images from packets raw-data, considering both header and payload. Our approach excels those found in state-of-the-art by delivering security and privacy by transforming the raw-data packet into images. Therefore, we built a dataset with four traffic classes evaluating the performance of three CNNs architectures: AlexNet, ResNet-18, and SqueezeNet. Experiments showcase the Packet Vision combined with CNNs applicability and suitability as a promising approach to deliver outstanding performance in classifying network traffic.

scholarly journals Voting Classification Method for Network Traffic Prediction

2020 ◽  
Vol 9 (7) ◽  
pp. 510-515
Keyword(s):  
Network Traffic ◽  
Second Phase ◽  
Traffic Classification ◽  
Data Set ◽  
Prediction Analysis ◽  
Proposed Model ◽  
Network Traffic Classification ◽  
Target Set ◽  
The Relationship ◽  
Complex Dataset

The prediction analysis is the approach of data mining which is applied to predict future possibilities based on the current information. The network traffic classification is the major issue of the prediction analysis due to complex dataset. The network traffic techniques have three steps, which are preprocessing, feature extraction and classification. In the phase of pre-processing data set is collected which is processed to removed missing and redundant values. In the second phase, the relationship between attribute and target set is established. In the last phase, the technique of classification is applied for the classification. This research study has been influenced by the different intrusion threats on internet and the ways to detect them. In this research, we have studied and analyzed the famous network traffic data -NSL KDD dataset and its various features. The proposed model is a hybrid of Logistic Regression and Knearest neighbor classifier combined using voting classifier, which aims at classifying the data into malicious and nonmalicious with more accuracy than existing methods.

scholarly journals An Automated ECG Beat Classification System Using Deep Neural Networks with an Unsupervised Feature Extraction Technique

2019 ◽  
Vol 9 (14) ◽  
pp. 2921 ◽  
Author(s):  
Siti Nurmaini ◽  
Radiyati Umi Partan ◽  
Wahyu Caesarendra ◽  
Tresna Dewi ◽  
Muhammad Naufal Rahmatullah ◽  
...  
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Feature Extraction ◽  
Feature Selection ◽  
Deep Neural Networks ◽  
Machine Learning Technique ◽  
Proposed Model ◽  
Learning Technique ◽  
Processing Steps ◽  
Unsupervised Feature Extraction

An automated classification system based on a Deep Learning (DL) technique for Cardiac Disease (CD) monitoring and detection is proposed in this paper. The proposed DL architecture is divided into Deep Auto-Encoders (DAEs) as an unsupervised form of feature learning and Deep Neural Networks (DNNs) as a classifier. The objective of this study is to improve on the previous machine learning technique that consists of several data processing steps such as feature extraction and feature selection or feature reduction. It is also noticed that the previously used machine learning technique required human interference and expertise in determining robust features, yet was time-consuming in the labeling and data processing steps. In contrast, DL enables an embedded feature extraction and feature selection in DAEs pre-training and DNNs fine-tuning process directly from raw data. Hence, DAEs is able to extract high-level of features not only from the training data but also from unseen data. The proposed model uses 10 classes of imbalanced data from ECG signals. Since it is related to the cardiac region, abnormality is usually considered for an early diagnosis of CD. In order to validate the result, the proposed model is compared with the shallow models and DL approaches. Results found that the proposed method achieved a promising performance with 99.73% accuracy, 91.20% sensitivity, 93.60% precision, 99.80% specificity, and a 91.80% F1-Score. Moreover, both the Receiver Operating Characteristic (ROC) curve and the Precision-Recall (PR) curve from the confusion matrix showed that the developed model is a good classifier. The developed model based on unsupervised feature extraction and deep neural network is ready to be used on a large population before its installation for clinical usage.

VPN Encrypted Traffic classification using XGBoost

2021 ◽  
Vol 9 (7) ◽  
pp. 960-966
Keyword(s):  
Network Traffic ◽  
Classification Model ◽  
The Internet ◽  
Classification Algorithms ◽  
Traffic Classification ◽  
Virtual Private Networks ◽  
New Model ◽  
Security Issues ◽  
Proposed Model ◽  
Encrypted Traffic

Classification network traffic are becoming ever more relevant in understanding and addressing security issues inInternet applications. Virtual Private Networks (VPNs) have become one famous communication forms on the Internet. In this study, a new model for traffic classification into VPN or non-VPN is proposed. XGBoost algorithm is used to rank features and to build the classification model. The proposed model overwhelmed other classification algorithms. The proposed model achieved 91.6% accuracy which is the highest registered accuracy for the selected dataset. To illustrate the merit of the proposed model, a comparison was made with sixteen different classification algorithms

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