scholarly journals ABOUT THE SECURİTY OF BRANCHCACHE TECHNOLOGY

InterConf ◽  
2021 ◽  
pp. 374-383
Author(s):  
Natiq Quliyev ◽  
Matlab Mammadov
Keyword(s):  
Network Traffic ◽  
Internet Traffic ◽  
Local Networks

Today, computers are mostly used online. In this regard, the loading of local networks and especially Internet traffic is observed. Optimization of local networks and especially Internet traffic is one of the most pressing issues of our time. The publication of this article is relevant because BranchCache technology is one of the tools for optimizing network traffic.

scholarly journals GAN Tunnel: Network Traffic Steganography by Using GANs to Counter Internet Traffic Classifiers

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 125345-125359
Author(s):  
Sina Fathi-Kazerooni ◽  
Roberto Rojas-Cessa
Keyword(s):  
Network Traffic ◽  
Internet Traffic

scholarly journals Attack Detection in IoT using Machine Learning

2021 ◽  
Vol 11 (3) ◽  
pp. 7273-7278
Author(s):  
M. Anwer ◽  
M. U. Farooq ◽  
S. M. Khan ◽  
W. Waseemullah
Keyword(s):  
Machine Learning ◽  
Network Traffic ◽  
Learning Algorithm ◽  
Attack Detection ◽  
Supervised Machine Learning ◽  
Detection Methods ◽  
Support Vector ◽  
Web Traffic ◽  
Local Networks ◽  
Iot Devices

Many researchers have examined the risks imposed by the Internet of Things (IoT) devices on big companies and smart towns. Due to the high adoption of IoT, their character, inherent mobility, and standardization limitations, smart mechanisms, capable of automatically detecting suspicious movement on IoT devices connected to the local networks are needed. With the increase of IoT devices connected through internet, the capacity of web traffic increased. Due to this change, attack detection through common methods and old data processing techniques is now obsolete. Detection of attacks in IoT and detecting malicious traffic in the early stages is a very challenging problem due to the increase in the size of network traffic. In this paper, a framework is recommended for the detection of malicious network traffic. The framework uses three popular classification-based malicious network traffic detection methods, namely Support Vector Machine (SVM), Gradient Boosted Decision Trees (GBDT), and Random Forest (RF), with RF supervised machine learning algorithm achieving far better accuracy (85.34%). The dataset NSL KDD was used in the recommended framework and the performances in terms of training, predicting time, specificity, and accuracy were compared.

scholarly journals Internet Capacity, Network Traffic And Net Neutrality

2010 ◽  
Vol 14 (5) ◽  
Author(s):  
Chris Rose
Keyword(s):  
Network Traffic ◽  
Internet Traffic ◽  
Net Neutrality ◽  
Online Content

The majority of Internet traffic is exchanged directly on private lines through mutual peering agreements, and although bandwidth usage is growing by about 50% per year, content publishers have invested in services, such as Akamai to ensure online content is transmitted faster. This means some traffic is already being sent on less crowded connections, and some are cached downstream. So, what about Net Neutrality.  Is all Internet traffic really created equal, or is some traffic more equal than others?

scholarly journals Effective Packet Number for 5G IM WeChat Application at Early Stage Traffic Classification

2017 ◽  
Vol 2017 ◽  
pp. 1-22 ◽  
Author(s):  
Muhammad Shafiq ◽  
Xiangzhan Yu
Keyword(s):  
Machine Learning ◽  
Mutual Information ◽  
Network Traffic ◽  
Early Stage ◽  
Statistical Tests ◽  
Internet Traffic ◽  
Machine Learning Algorithms ◽  
Experimental Results ◽  
Traffic Classification ◽  
Network Traffic Classification

Accurate network traffic classification at early stage is very important for 5G network applications. During the last few years, researchers endeavored hard to propose effective machine learning model for classification of Internet traffic applications at early stage with few packets. Nevertheless, this essential problem still needs to be studied profoundly to find out effective packet number as well as effective machine learning (ML) model. In this paper, we tried to solve the above-mentioned problem. For this purpose, five Internet traffic datasets are utilized. Initially, we extract packet size of 20 packets and then mutual information analysis is carried out to find out the mutual information of each packet onnflow type. Thereafter, we execute 10 well-known machine learning algorithms using crossover classification method. Two statistical analysis tests, Friedman and Wilcoxon pairwise tests, are applied for the experimental results. Moreover, we also apply the statistical tests for classifiers to find out effective ML classifier. Our experimental results show that 13–19 packets are the effective packet numbers for 5G IM WeChat application at early stage network traffic classification. We also find out effective ML classifier, where Random Forest ML classifier is effective classifier at early stage Internet traffic classification.

scholarly journals Online P2P Internet Traffic Classification and Mitigation Based on Snort and ML

2019 ◽  
Vol 4 (10) ◽  
pp. 131-137 ◽  
Author(s):  
Haitham Ahmed Jamil ◽  
Bushra M. Ali ◽  
Mosab Hamdan ◽  
Ahmed E. Osman
Keyword(s):  
Machine Learning ◽  
Network Traffic ◽  
Computation Time ◽  
Internet Traffic ◽  
Classification Performance ◽  
Transport Layer ◽  
Traffic Classification ◽  
Shared Resources ◽  
Internet Applications ◽  
Learning Classifier

Peer to peer applications have modified the nature of internet traffic.   It will consume high internet bandwidth and affect the performance of traditional traffic internet applications.   Therefore, the management and monitoring activity of internet traffic is the important activities involved in the optimization.   In order to detect and mitigate the P2P traffic, port, payload, and transport layer based methods were developed in the past.  Nevertheless, the performances of these methods were not up to the expectation.  Machine Learning (ML) is one of the promising methods to identify and mitigate the traffic of the Internet.   However, the classification accuracy is inconsistent.   The reason for the inconsistency is the relevant training datasets generation and feature selection.   In this research, a technique based on signature-based and ML is proposed to develop a model for online P2P traffic detection and mitigation.   The proposed work can be employed to evaluate the robustness of the online P2P machine learning classifier based on real network traffic traces containing flows labelled by SNORT tool and from special shared resources.  Analysis and validation were carried out on traffic traces of University Technology Malaysia.   The period of traffic was 2011 and 2013.   The output of research is revealing that the proposed work has spent less computation time for classification.  This method gives 99.7% accuracy which equals the classification performance attained for P2P using deep packet inspector. The findings show that classifying network traffic at the flow level can differentiate P2P over non-P2P (nP2P) with high confidence for online P2P mitigation.

Transporting TDM Service on Metropolitan Bus-Based Optical Packet Switching Networks

2008 ◽  
pp. 653-662
Author(s):  
Viet Hung Nguyen ◽  
Tülin Atmaca
Keyword(s):  
Network Traffic ◽  
Packet Switching ◽  
Service Providers ◽  
Internet Traffic ◽  
Traffic Volume ◽  
Optical Packet Switching ◽  
Video Traffic ◽  
Voice Traffic ◽  
Optical Packet Switching Networks ◽  
Packet Switching Networks

Today’s telecommunication world is seeing dramatic changes in network infrastructures and services. These changes are mainly driven by the ever-growing rate of network traffic. Global Internet traffic is doubling each year due to both tremendous growth in the number of users and rapid increase of bandwidth accessible by each user (e.g., the Global Internet Geography report (2004) stated that in Asia, Internet traffic growth was about 400 percent in the year 2004). Not only is network traffic growing at an unprecedented speed, but the traffic mix is changing greatly. The traditional voice traffic volume has now become very small relative to the huge volume of data and video traffic, due to the deployment of Gigabit technologies in the access part of the service providers’ networks.

scholarly journals DHCP Hierarchical Failover (DHCP-HF) Servers over a VPN Interconnected Campus

2019 ◽  
Vol 3 (1) ◽  
pp. 18 ◽  
Author(s):  
Lucas Trombeta ◽  
Nunzio Torrisi
Keyword(s):  
Network Traffic ◽  
Virtual Machines ◽  
Fault Tolerant ◽  
Recovery Strategy ◽  
Local Networks ◽  
Geographic Locations ◽  
Proposed Model ◽  
Point To Point ◽  
The Impact ◽  
Dynamic Host Configuration

This work presents a strategy to scale out the fault-tolerant dynamic host configuration protocol (DHCP) algorithm over multiple interconnected local networks. The proposed model is open and used as an alternative to commercial solutions for a multi-campus institution with facilities in different regions that are interconnected point-to-point using a dedicated link. When the DHCP scope has to be managed and structured over multiple geographic locations that are VPN connected, it requires physical redundancy, which can be provided by a failover server. The proposed solution overcomes the limitation placed on the number of failover servers as defined in the DHCP failover (DHCP-F) protocol, which specifies the use of one primary and one secondary server. Moreover, the presented work also contributes to improving the DHCP-F specification relative to a number of practical workarounds, such as the use of a virtualized DHCP server. Therefore, this research assumes a recovery strategy that is based on physical servers distributed among different locations and not centralized as clustered virtual machines. The proposed method was evaluated by simulations to investigate the impact of this solution in terms of network traffic generated over the VPN links in order to keep the failover service running using the proposed approach.

Two Methods for Active Detection and Prevention of Sophisticated ARP-Poisoning Man-in-the-Middle Attacks on Switched Ethernet LANs

2011 ◽  
Vol 3 (3) ◽  
pp. 50-60 ◽  
Author(s):  
Kenan Kalajdzic ◽  
Ahmed Patel ◽  
Mona Taghavi
Keyword(s):  
Network Traffic ◽  
Multiple Solutions ◽  
Denial Of Service ◽  
Local Networks ◽  
Switched Ethernet ◽  
Man In The Middle ◽  
Novel Methods ◽  
Active Detection

This paper describes two novel methods for active detection and prevention of ARP-poisoning-based Man-in-the-Middle (MitM) attacks on switched Ethernet LANs. As a stateless and inherently insecure protocol, ARP has been used as a relatively simple means to launch Denial-of-Service (DoS) and MitM attacks on local networks and multiple solutions have been proposed to detect and prevent these types of attacks. MitM attacks are particularly dangerous, because they allow an attacker to monitor network traffic and break the integrity of data being sent over the network. The authors introduce backwards compatible techniques to prevent ARP poisoning and deal with sophisticated stealth MitM programs.

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