25:
341:
protocol does for example rely on obfuscation and randomized packet sizes in order to avoid identification. File sharing traffic can be appropriately classified as Best-Effort traffic. At peak times when sensitive traffic is at its height, download speeds will decrease. However, since P2P downloads
221:
This same problem with traffic classification is also present in multimedia traffic. It has been generally proven that using methods based on neural networks, vector support machines, statistics, and the nearest neighbors are a great way to do this traffic classification, but in some specific cases
356:
Some vendors advocate managing clients rather than specific protocols, particularly for ISPs. By managing per-client (that is, per customer), if the client chooses to use their fair share of the bandwidth running P2P applications, they can do so, but if their application is abusive, they only clog
182:
A comprehensive comparison of various network traffic classifiers, which depend on Deep Packet
Inspection (PACE, OpenDPI, 4 different configurations of L7-filter, NDPI, Libprotoident, and Cisco NBAR), is shown in the Independent Comparison of Popular DPI Tools for Traffic Classification.
342:
are often background activities, it affects the subscriber experience little, so long as the download speeds increase to their full potential when all other subscribers hang up their VoIP phones. Exceptions are real-time P2P VoIP and P2P video streaming services who need permanent
313:) that use comparatively small amounts of bandwidth. P2P programs can also suffer from download strategy inefficiencies, namely downloading files from any available peer, regardless of link cost. The applications use
336:
properties in the network (in-order packet delivery, jitter, etc. - typically this is achieved through increased buffering and reliable transport, with the user experiencing increased download time as a result). The
331:
P2P protocols can and are often designed so that the resulting packets are harder to identify (to avoid detection by traffic classifiers), and with enough robustness that they do not depend on specific
328:
determined, through traffic analysis, that P2P traffic accounted for up to 60% of traffic on most networks. This shows, in contrast to previous studies and forecasts, that P2P has become mainstream.
286:
Best-effort traffic is all other kinds of traffic. This is traffic that the ISP deems isn't sensitive to quality of service metrics (jitter, packet loss, latency). A typical example would be
392:
Suppliers' Information Note For The BT Network BT Wholesale - BT IPstream
Advanced Services - End User Speed Control and Downstream Quality of Service - Service Description
214:
Nowadays the traffic is more complex, and more secure, for this, we need a method to classify the encrypted traffic in a different way than the classic mode (based on IP
97:. Upon classifying a traffic flow using a particular protocol, a predetermined policy can be applied to it and other flows to either guarantee a certain quality (as with
218:
by probes in the core network). A form to achieve this is by using traffic descriptors from connection traces in the radio interface to perform the classification.
109:) with a granularity that allows traffic management mechanisms to separate traffic into individual flows and queue, police and shape them differently.
46:
33:
85:. Each resulting traffic class can be treated differently in order to differentiate the service implied for the data generator or consumer.
274:
of these selected uses is guaranteed, or at least prioritized over other classes of traffic. This can be accomplished by the absence of
294:
applications. Traffic management schemes are generally tailored so best-effort traffic gets what is left after time-sensitive traffic.
179:
phase would be a check to see if a packet began with character 19 which was then followed by the 19-byte string 'BitTorrent protocol'.
410:
314:
222:
some methods are better than others, for example: neural networks work better when the whole observation set is taken into account.
435:
558:
338:
603:
636:
594:
The
Register article which refers to Sandvine report - access to the actual report requires registration with Sandvine
353:, serving as a traffic shaper configured to the user's (as opposed to the network operator's) traffic specification.
172:
Matching bit patterns of data to those of known protocols is a simple widely used technique. An example to match the
192:
Relies on statistical analysis of attributes such as byte frequencies, packet sizes and packet inter-arrival times.
38:
302:
195:
Very often uses
Machine Learning Algorithms, as K-Means, Naive Bayes Filter, C4.5, C5.0, J48, or Random Forest
231:
583:
199:
146:
176:
173:
78:
254:
Time-sensitive traffic is traffic the operator has an expectation to deliver on time. This includes
470:"Encrypted Traffic Classification Based on Unsupervised Learning in Cellular Radio Access Networks"
102:
615:
540:
491:
402:
343:
333:
306:
271:
263:
511:"Multimedia Data Flow Traffic Classification Using Intelligent Models Based on Traffic Patterns"
246:
Operators often distinguish two broad types of network traffic: time-sensitive and best-effort.
406:
135:
Does not implement the application-layer payload, so it does not compromise the users' privacy
94:
456:
E. Hjelmvik and W. John, “Statistical
Protocol IDentification with SPID: Preliminary Results”
530:
522:
481:
215:
70:
455:
101:
or media streaming service) or to provide best-effort delivery. This may be applied at the
350:
275:
259:
156:
Detects the applications and services regardless of the port number, on which they operate
630:
495:
305:
applications are often designed to use any and all available bandwidth which impacts
98:
544:
588:
373:
287:
267:
422:
346:
and use excessive overhead and parity traffic to enforce this as far as possible.
278:
for this traffic class, or by prioritizing sensitive traffic above other classes.
486:
469:
389:
310:
106:
74:
510:
526:
405:
on the
Internet and in Corporate Networks, John Wiley & Sons, Inc., 1998.
165:
Signatures must be kept up to date, as the applications change very frequently
357:
their own bandwidth and cannot affect the bandwidth used by other customers.
235:
270:. Traffic management schemes are typically tailored in such a way that the
138:
Useful only for the applications and services, which use fixed port numbers
24:
325:
321:
traffic to discover servers and download directories of available files.
535:
436:"Independent Comparison of Popular DPI Tools for Traffic Classification"
562:
291:
377:
318:
255:
238:
contain logic to identify and mark or classify network packets.
73:
traffic according to various parameters (for example, based on
376:"An Architecture for Differentiated Services" section 2.3.1 -
18:
105:(the point at which traffic enters the network, typically an
93:
Packets are classified to be differently processed by the
434:
Tomasz Bujlow; Valentín Carela-Español; Pere Barlet-Ros.
604:
Identifying the
Message Stream Encryption (MSE) protocol
401:
Ferguson P., Huston G., Quality of
Service: Delivering
141:
Easy to cheat by changing the port number in the system
168:
Encryption makes this method impossible in many cases
205:
It can detect the class of yet unknown applications
349:Some P2P applications can be configured to act as
117:Classification is achieved by various means.
8:
620:Example for client side P2P traffic limiting
69:is an automated process which categorises
16:Categorization of computer network traffic
534:
485:
153:Inspects the actual payload of the packet
49:of all important aspects of the article.
616:"Optimize uTorrent Speeds Jatex Weblog"
366:
45:Please consider expanding the lead to
7:
162:Requires a lot of processing power
14:
132:Supported by many network devices
210:Encrypted traffic classification
23:
584:"P2P swamps broadband networks"
458:, in Proceedings of SNCNW, 2009
37:may be too short to adequately
438:. In press (Computer Networks)
309:-sensitive applications (like
47:provide an accessible overview
1:
198:Fast technique (compared to
509:Canovas, Alejandro (2018).
487:10.1109/ACCESS.2020.3022980
653:
390:SIN 450 Issue 1.2 May 2007
187:Statistical classification
527:10.1109/MNET.2018.1800121
380:definition of classifier.
303:Peer-to-peer file sharing
468:Gijón, Carolina (2020).
242:Typical traffic classes
232:Linux network scheduler
250:Time-sensitive traffic
200:deep packet inspection
147:Deep Packet Inspection
129:Low resource-consuming
113:Classification methods
67:Traffic classification
351:self-limiting sources
326:Sandvine Incorporated
339:encrypted BitTorrent
637:Network performance
423:BitTorrent Protocol
282:Best-effort traffic
174:BitTorrent protocol
81:) into a number of
272:quality of service
264:video conferencing
480:: 167252–167263.
95:network scheduler
64:
63:
644:
621:
619:
612:
606:
601:
595:
593:
579:
573:
572:
570:
569:
555:
549:
548:
538:
506:
500:
499:
489:
465:
459:
453:
447:
446:
444:
443:
431:
425:
420:
414:
399:
393:
387:
381:
371:
216:traffic analysis
71:computer network
59:
56:
50:
27:
19:
652:
651:
647:
646:
645:
643:
642:
641:
627:
626:
625:
624:
614:
613:
609:
602:
598:
581:
580:
576:
567:
565:
557:
556:
552:
508:
507:
503:
467:
466:
462:
454:
450:
441:
439:
433:
432:
428:
421:
417:
400:
396:
388:
384:
372:
368:
363:
300:
284:
252:
244:
228:
212:
202:classification)
189:
150:
123:
115:
91:
83:traffic classes
60:
54:
51:
44:
32:This article's
28:
17:
12:
11:
5:
650:
648:
640:
639:
629:
628:
623:
622:
607:
596:
582:Leydon, John.
574:
550:
521:(6): 100–107.
501:
460:
448:
426:
415:
394:
382:
365:
364:
362:
359:
299:
296:
283:
280:
251:
248:
243:
240:
227:
226:Implementation
224:
211:
208:
207:
206:
203:
196:
193:
188:
185:
170:
169:
166:
163:
160:
157:
154:
149:
144:
143:
142:
139:
136:
133:
130:
127:
122:
119:
114:
111:
90:
87:
62:
61:
41:the key points
31:
29:
22:
15:
13:
10:
9:
6:
4:
3:
2:
649:
638:
635:
634:
632:
617:
611:
608:
605:
600:
597:
591:
590:
585:
578:
575:
564:
560:
554:
551:
546:
542:
537:
532:
528:
524:
520:
516:
512:
505:
502:
497:
493:
488:
483:
479:
475:
471:
464:
461:
457:
452:
449:
437:
430:
427:
424:
419:
416:
412:
411:0-471-24358-2
408:
404:
398:
395:
391:
386:
383:
379:
375:
374:IETF RFC 2475
370:
367:
360:
358:
354:
352:
347:
345:
340:
335:
329:
327:
322:
320:
316:
312:
311:online gaming
308:
304:
297:
295:
293:
289:
281:
279:
277:
273:
269:
265:
261:
257:
249:
247:
241:
239:
237:
233:
225:
223:
219:
217:
209:
204:
201:
197:
194:
191:
190:
186:
184:
180:
178:
175:
167:
164:
161:
158:
155:
152:
151:
148:
145:
140:
137:
134:
131:
128:
125:
124:
120:
118:
112:
110:
108:
104:
103:ingress point
100:
96:
88:
86:
84:
80:
76:
72:
68:
58:
48:
42:
40:
35:
30:
26:
21:
20:
610:
599:
589:The Register
587:
577:
566:. Retrieved
553:
536:10251/116174
518:
514:
504:
477:
473:
463:
451:
440:. Retrieved
429:
418:
397:
385:
369:
355:
348:
330:
323:
317:and regular
301:
298:File sharing
288:peer-to-peer
285:
268:web browsing
253:
245:
229:
220:
213:
181:
171:
121:Port numbers
116:
92:
89:Typical uses
82:
66:
65:
52:
36:
34:lead section
559:"Class Map"
177:handshaking
107:edge device
75:port number
568:2024-02-22
442:2014-11-10
361:References
230:Both, the
496:221913926
324:In 2002,
258:, online
236:Netfilter
39:summarize
631:Category
545:54437310
79:protocol
55:May 2020
276:shaping
543:
494:
409:
266:, and
260:gaming
563:Cisco
541:S2CID
492:S2CID
292:email
515:IEEE
474:IEEE
407:ISBN
378:IETF
319:HTTP
315:ICMP
290:and
256:VoIP
234:and
159:Slow
126:Fast
99:VoIP
531:hdl
523:doi
482:doi
403:QoS
344:QoS
334:QoS
307:QoS
77:or
633::
586:.
561:.
539:.
529:.
519:32
517:.
513:.
490:.
476:.
472:.
262:,
618:.
592:.
571:.
547:.
533::
525::
498:.
484::
478:8
445:.
413:.
57:)
53:(
43:.
Text is available under the Creative Commons Attribution-ShareAlike License. Additional terms may apply.
