A computational intelligence based approach for computer network traffic shaping

2013 ◽  
Author(s):  
Ulisses Cavalca ◽  
Caio Mesquista ◽  
Adriano C. M. Pereira ◽  
Eduardo G. Carrano
Keyword(s):  
Computational Intelligence ◽  
Network Traffic ◽  
Computer Network ◽  
Traffic Shaping

Computer Network Traffic Analysis Using SNMP Protocol and LVQ Networks

2016 ◽  
pp. 215-219 ◽  
Author(s):  
Ivan Nunes da Silva ◽  
Danilo Hernane Spatti ◽  
Rogerio Andrade Flauzino ◽  
Luisa Helena Bartocci Liboni ◽  
Silas Franco dos Reis Alves
Keyword(s):  
Network Traffic ◽  
Computer Network ◽  
Traffic Analysis ◽  
Network Traffic Analysis ◽  
Snmp Protocol

Network Situational Awareness

2015 ◽  
pp. 334-349 ◽  
Author(s):  
Tom Fairfax ◽  
Christopher Laing ◽  
Paul Vickers
Keyword(s):  
Real Time ◽  
Network Traffic ◽  
Affective Computing ◽  
Situational Awareness ◽  
Computer Network ◽  
Emergent Properties ◽  
Self Organized ◽  
Traffic Volumes ◽  
Self Organized Criticality ◽  
Intelligent Information

This chapter treats computer networks as a cyber warfighting domain in which the maintenance of situational awareness is impaired by increasing traffic volumes and the lack of immediate sensory perception. Sonification (the use of non-speech audio for communicating information) is proposed as a viable means of monitoring a network in real time and a research agenda employing the sonification of a network's self-organized criticality within a context-aware affective computing scenario is given. The chapter views a computer network as a cyber battlespace with a particular operations spectrum and dynamics. Increasing network traffic volumes are interfering with the ability to present real-time intelligence about a network and so suggestions are made for how the context of a network might be used to help construct intelligent information infrastructures. Such a system would use affective computing principles to sonify emergent properties (such as self-organized criticality) of network traffic and behaviour to provide effective real-time situational awareness.

Network Traffic and Data

2011 ◽  
pp. 60-103
Author(s):  
Yu Wang
Keyword(s):  
Intrusion Detection ◽  
Network Traffic ◽  
Computer Network ◽  
Transmission Control Protocol ◽  
Processing Unit ◽  
Data Types ◽  
Specific Data ◽  
Related Data ◽  
Central Processing ◽  
Intrusion Detection And Prevention

In this chapter we will focus on examining computer network traffic and data. A computer network combines a set of computers and physically and logically connects them together to exchange information. Network traffic acquired from a network system provides information on data communications within the network and between networks or individual computers. The most common data types are log data, such as Kerberos logs, transmission control protocol/Internet protocol (TCP/IP) logs, Central processing unit (CPU) usage data, event logs, user command data, Internet visit data, operating system audit trail data, intrusion detection and prevention service (IDS/IPS) logs, Netflow1 data, and the simple network management protocol (SNMP) reporting data. Such information is unique and valuable for network security, specifically for intrusion detection and prevention. Although we have already presented some essential challenges in collecting such data in Chapter I, we will discuss traffic data, as well as other related data, in greater detail in this chapter. Specifically, we will describe system-specific and user-specific data types in Sections System- Specific Data and User-Specific Data, respectively, and provide detailed information on publicly available data in Section Publicly Available Data.

scholarly journals KLASIFIKASI PENGGUNAAN PROTOKOL KOMUNIKASI PADA TRAFIK JARINGAN MENGGUNAKAN ALGORITMA K-NEAREST NEIGHBOR

2016 ◽  
Vol 16 (1) ◽  
pp. 67
Author(s):  
Komang Kompyang Agus Subrata ◽  
I Made Oka Widyantara ◽  
Linawati Linawati
Keyword(s):  
Quality Of Service ◽  
Network Traffic ◽  
Network Architecture ◽  
Nearest Neighbor ◽  
Computer Network ◽  
Data Communication ◽  
Data Capture ◽  
K Nearest Neighbor ◽  
High Level

ABSTRACT—Network traffic internet is data communication in a network characterized by a set of statistical flow with the application of a structured pattern. Structured pattern in question is the information from the packet header data. Proper classification to an Internet traffic is very important to do, especially in terms of the design of the network architecture, network management and network security. The analysis of computer network traffic is one way to know the use of the computer network communication protocol, so it can be the basis for determining the priority of Quality of Service (QoS). QoS is the basis for giving priority to analyzing the network traffic data. In this study the classification of the data capture network traffic that though the use of K-Neaerest Neighbor algorithm (K-NN). Tools used to capture network traffic that wireshark application. From the observation of the dataset and the network traffic through the calculation process using K-NN algorithm obtained a result that the value generated by the K-NN classification has a very high level of accuracy. This is evidenced by the results of calculations which reached 99.14%, ie by calculating k = 3. Intisari—Trafik jaringan internet adalah lalu lintas ko­mu­nikasi data dalam jaringan yang ditandai dengan satu set ali­ran statistik dengan penerapan pola terstruktur. Pola ter­struktur yang dimaksud adalah informasi dari header paket data. Klasifikasi yang tepat terhadap sebuah trafik internet sa­ngat penting dilakukan terutama dalam hal disain perancangan arsitektur jaringan, manajemen jaringan dan keamanan jari­ngan. Analisa terhadap suatu trafik jaringan komputer meru­pakan salah satu cara mengetahui penggunaan protokol komu­nikasi jaringan komputer, sehingga dapat menjadi dasar pe­nen­tuan prioritas Quality of Service (QoS). Dasar pemberian prio­ritas QoS adalah dengan penganalisaan terhadap data trafik jaringan. Pada penelitian ini melakukan klasifikasi ter­hadap data capture trafik jaringan yang di olah menggunakan Algoritma K-Neaerest Neighbor (K-NN). Apli­kasi yang digu­nakan untuk capture trafik jaringan yaitu aplikasi wireshark. Hasil observasi terhadap dataset trafik jaringan dan melalui proses perhitungan menggunakan Algoritma K-NN didapatkan sebuah hasil bahwa nilai yang dihasilkan oleh klasifikasi K-NN memiliki tingkat keakuratan yang sangat tinggi. Hal ini dibuktikan dengan hasil perhi­tungan yang mencapai nilai 99,14 % yaitu dengan perhitungan k = 3. DOI: 10.24843/MITE.1601.10

scholarly journals Wireless Network Traffic Analysis and Troubleshooting using Raspberry Pi

2018 ◽  
Vol 7 (2.15) ◽  
pp. 58
Author(s):  
Mohamad Nur Haziq Mohd Safri ◽  
Wan Nor Shuhadah Wan Nik ◽  
Zarina Mohamad ◽  
Mumtazimah Mohamad
Keyword(s):  
Wireless Network ◽  
Network Traffic ◽  
Computer Network ◽  
Low Cost ◽  
Access Point ◽  
Traffic Analysis ◽  
Raspberry Pi ◽  
Network Access ◽  
Actual Problem ◽  
Network Traffic Analysis

In the past five decades, computer network has kept up growing with the increases of its complexity. In such situation, the management, monitoring and maintenance of such computer network requires special attention to ensure optimal network access capability is achieved. Wireless network traffic analysis is a process of recording, studying and analyzing packets in wireless network for network performance analysis purposes. In some cases, the quality of network access performance can be very low without knowing the actual problem. Therefore, in this paper, the performance of wireless network traffic is proposed to be analyzed by using a Raspberry Pi which further able to send an alert to network admin to lessen the downtime. Raspberry Pi is a low cost, a small and portable size of a computer board that can be used to plug-in to monitor, keyboard, mouse, pen drive, etc. In this project, a MyTraceroute (MTR) program is installed on the Raspberry Pi to capture the IP of the Access Point (AP) and show packets loss percentage in the network. The results will be saved in the form of text file and sent to network admin by using email. The solution proposed in this paper is able to support solution to a problem on efficient monitoring, managing and maintaining wireless network traffics.    

scholarly journals Detection of Abnormalities in Real-Time Computer Network Traffic Empowered by Machine Learning

2021 ◽  
Vol 10 (3) ◽  
pp. 2072-2079
Keyword(s):  
Machine Learning ◽  
Anomaly Detection ◽  
Real Time ◽  
Network Traffic ◽  
Computer Network ◽  
Data Gathering ◽  
Network Systems ◽  
Data Set ◽  
Synthetic Network ◽  
Scientific Papers

This research discloses how to utilize machine learning methods for anomaly detection in real-time on a computer network. While utilizing machine learning for this task is definitely not a novel idea, little literature is about the matter of doing it in real-time. Most machine learning research in PC network anomaly detection depends on the KDD '99 data set and means to demonstrate the proficiency of the algorithms introduced. The emphasis on this data set has caused a lack of scientific papers disclosing how to assemble network data, remove features, and train algorithms for use inreal-time networks. It has been contended that utilizing the KDD '99 dataset for anomaly detection is not appropriate for real-time network systems. This research proposes how the data gathering procedure will be possible utilizing a dummy network and generating synthetic network traffic by analyzing the importance of One-class SVM. As the efficiency of k-means clustering and LTSM neural networks is lower than one-class SVM, that is why this research uses the results of existing research of LSTM and k-means clustering for the comparison with reported outcomes of a similar algorithm on the KDD '99 dataset. Precisely, without engaging KDD ’99 data set by using synthetic network traffic, this research achieved the higher accuracy as compared to the previous researches.

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