Out-network traffic terms: A case study in LinkedIn 1

2014 ◽  
Author(s):  
Ali Alibeigi ◽  
Asefeh Asemi ◽  
AbuBakar Munir ◽  
Adeleh Asemi
Keyword(s):  
Network Traffic

A case study on the representativeness of public DoS network traffic data for cybersecurity research

2020 ◽  
Author(s):  
Marta Catillo ◽  
Antonio Pecchia ◽  
Massimiliano Rak ◽  
Umberto Villano
Keyword(s):  
Network Traffic ◽  
Traffic Data

Research of the Method for Traffic Generating Based on Genetic Algorithm

2014 ◽  
Vol 701-702 ◽  
pp. 3-7
Author(s):  
Liu Bo
Keyword(s):  
Genetic Algorithm ◽  
Network Traffic ◽  
Large Scale ◽  
Network Reliability ◽  
Actual Usage ◽  
Large Scale Network ◽  
Scale Network ◽  
Self Similar ◽  
Optimal Values

It has great impact on result of the network test or simulation if the test simulated traffic is corresponding to real situation. The network traffic is the superposition of different traffic streams in the actual usage of the network. But because of the complexity and time-consumption to generate different traffic streams, it is difficult to generate the network traffic in the simulation for the large scale network. This paper proposes a kind of method for traffic generating based on genetic algorithm .According to building the self-similar traffic model ,the optimal values of the model’s parameters has been obtained. A case study shows the effectiveness of the method for the network reliability.

scholarly journals Encrypted Network Traffic Analysis of Secure Instant Messaging Application: A Case Study of Signal Messenger App

2021 ◽  
Vol 11 (17) ◽  
pp. 7789
Author(s):  
Asmara Afzal ◽  
Mehdi Hussain ◽  
Shahzad Saleem ◽  
M. Khuram Shahzad ◽  
Anthony T. S. Ho ◽  
...  
Keyword(s):  
Network Traffic ◽  
Instant Messaging ◽  
Vital Role ◽  
Media Messages ◽  
Security Services ◽  
Network Traffic Analysis ◽  
End To End ◽  
Network Forensic ◽  
Encrypted Traffic

Instant messaging applications (apps) have played a vital role in online interaction, especially under COVID-19 lockdown protocols. Apps with security provisions are able to provide confidentiality through end-to-end encryption. Ill-intentioned individuals and groups use these security services to their advantage by using the apps for criminal, illicit, or fraudulent activities. During an investigation, the provision of end-to-end encryption in apps increases the complexity for digital forensics investigators. This study aims to provide a network forensic strategy to identify the potential artifacts from the encrypted network traffic of the prominent social messenger app Signal (on Android version 9). The analysis of the installed app was conducted over fully encrypted network traffic. By adopting the proposed strategy, the forensic investigator can easily detect encrypted traffic activities such as chatting, media messages, audio, and video calls by looking at the payload patterns. Furthermore, a detailed analysis of the trace files can help to create a list of chat servers and IP addresses of involved parties in the events. As a result, the proposed strategy significantly facilitates extraction of the app’s behavior from encrypted network traffic which can then be used as supportive evidence for forensic investigation.

scholarly journals Method of Evaluating and Predicting Traffic State of Highway Network Based on Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Jiayu Liu ◽  
Xingju Wang ◽  
Yanting Li ◽  
Xuejian Kang ◽  
Lu Gao
Keyword(s):  
Deep Learning ◽  
Prediction Model ◽  
Network Traffic ◽  
Short Term Memory ◽  
Real Data ◽  
Hebei Province ◽  
Discriminant Model ◽  
Traffic State ◽  
Highway Network

The accurate evaluation and prediction of highway network traffic state can provide effective information for travelers and traffic managers. Based on the deep learning theory, this paper proposes an evaluation and prediction model of highway network traffic state, which consists of a Fuzzy C-means (FCM) algorithm-based traffic state partition model, a Long Short-Term Memory (LSTM) algorithm-based traffic state prediction model, and a K-Means algorithm-based traffic state discriminant model. The highway network in Hebei Province is employed as a case study to validate the model, where the traffic state of highway network is analyzed using both predicted data and real data. The dataset contains 536,823 pieces of data collected by 233 continuous observation stations in Hebei Province from September 5, 2016, to September 12, 2016. The analysis results show that the model proposed in this paper has a good performance on the evaluation and prediction of the traffic state of the highway network, which is consistent with the discriminant result using the real data.

scholarly journals Automated Contextual Anomaly Detection for Network Interface Bandwidth Utilisation: A Case Study in Network Capacity Management

2021 ◽  
Author(s):  
Esmaeil Zadeh ◽  
Stephen Amstutz ◽  
James Collins ◽  
Craig Ingham ◽  
Marian Gheorghe ◽  
...  
Keyword(s):  
Anomaly Detection ◽  
Service Provider ◽  
Network Traffic ◽  
Network Capacity ◽  
Capacity Management ◽  
Network Interface ◽  
Management Process ◽  
Traffic Stream ◽  
Small Team

We present a contextual anomaly detection methodology utilised for the capacity management process of a managed service provider that administers networks for large enterprises. We employ an ensemble of forecasts to identify anomalous network traffic. Stream of observations, upon their arrival, are compared against these baseline forecasts and alerts generated only if the anomalies are sustained. The results confirm that our approach significantly reduces false alerts, triggering rather more accurate and meaningful alerts to a level that could be proactively consumed by a small team. We believe our methodology makes a useful contribution to the applications enabling proactive capacity management.

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