Please do the calculations!!!! Networked Life: 20 Questions and Answers by Mung
ID: 3850040 • Letter: P
Question
Please do the calculations!!!!
Explanation / Answer
Traffic classification describes the methods of classifying traffic by observing features passively in the traffic, and in line to particular classification goals. There might be some that only have a vulgar classification goal. For example, whether it is bulk transfer, peer to peer file sharing or transaction-orientated. Some others will set a finer-grained classification goal, for instance the exact number of application represented by the traffic. Traffic features included port number, application payload, temporal, packet size and the characteristic of the traffic. There are a vast range of methods to allocate Internet traffic including exact traffic, for instance port (computer networking) number, payload, heuristic or statistical machine learning.[8]
Accurate network traffic classification is elementary to quite a few Internet activities, from security monitoring to accounting and from quality of service to providing operators with useful forecasts for long-term provisioning. Yet, classification schemes are extremely complex to operate accurately due to the shortage of available knowledge to the network. For example, the packet header related information is always insufficient to allow for an precise methodology. Consequently, the accuracy of any traditional method are between 50%-70%.
Bayesian analysis techniques:
Work[9] involving supervised machine learning to classify network traffic. Data are hand-classified (based upon flow content) to one of a number of categories. A combination of data set (hand-assigned) category and descripttions of the classified flows (such as flow length, port numbers, time between consecutive flows) are used to train the classifier. To give a better insight of the technique itself, initial assumptions are made as well as applying two other techniques in reality. One is to improve the quality and separation of the input of information leading to an increase in accuracy of the Naive Bayes classifier technique.
The basis of categorizing work is to classify the type of Internet traffic; this is done by putting common groups of applications into different categories, e.g., "normal" versus "malicious", or more complex definitions, e.g., the identification of specific applications or specific Transmission Control Protocol (TCP) implementations.[10] Adapted from Logg et al.[11]
Survey:
Traffic classification is a major component of automated intrusion detection systems.[12][13][14] They are used to identify patterns as well as indication of network resources for priority customers, or identify customer use of network resources that in some way contravenes the operator’s terms of service. Generally deployed Internet Protocol (IP) traffic classification techniques are based approximately on direct inspection of each packet’s contents at some point on the network. Source address, port and destination address are included in successive IP packet's with similar if not the same 5-tuple of protocol type. ort are considered to belong to a flow whose controlling application we wish to determine. Simple classification infers the controlling application’s identity by assuming that most applications consistently use well known TCP or UDP port numbers. Even though, many candidates are increasingly using unpredictable port numbers. As a result, more sophisticated classification techniques infer application type by looking for application-specific data within the TCP or User Datagram Protocol (UDP) payloads.[15]
Global Internet traffic:
Aggregating from multiple sources and applying usage and bitrate assumptions, Cisco Systems, a major network systems company, has published the following historical Internet