Real-time traffic classification based on cosine similarity using sub-application vectors

Cihangir Beşiktaş; Haci Ali Mantar

doi:10.1007/978-3-642-28534-9_10

Real-time traffic classification based on cosine similarity using sub-application vectors

Cihangir Beşiktaş^*, Haci Ali Mantar

^*Bu çalışma için yazışmadan sorumlu yazar

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › bilirkişi

4 Atıf (Scopus)

Özet

Internet traffic classification has a critical role on network monitoring, quality of service, intrusion detection, network security and trend analysis. The conventional port-based method is ineffective due to dynamic port usage and masquerading techniques. Besides, payload-based method suffers from heavy load and encryption. Due to these facts, machine learning based statistical approaches have become the new trend for the network measurement community. In this short paper, we propose a new statistical approach based on DBSCAN clustering and weighted cosine similarity. Our experimental test results show that the proposed approach achieves very high accuracy.

Orijinal dil	İngilizce
Ana bilgisayar yayını başlığı	Traffic Monitoring and Analysis - 4th International Workshop, TMA 2012, Proceedings
Sayfalar	89-92
Sayfa sayısı	4
DOI'lar	https://doi.org/10.1007/978-3-642-28534-9_10
Yayın durumu	Yayınlandı - 2012
Harici olarak yayınlandı	Evet
Etkinlik	4th International Workshop on Traffic Monitoring and Analysis, TMA 2012 - Vienna, Austria Süre: 12 Mar 2012 → 12 Mar 2012

Yayın serisi

Adı	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Hacim	7189 LNCS
ISSN (Basılı)	0302-9743
ISSN (Elektronik)	1611-3349

???event.eventtypes.event.conference???

???event.eventtypes.event.conference???	4th International Workshop on Traffic Monitoring and Analysis, TMA 2012
Ülke/Bölge	Austria
Şehir	Vienna
Periyot	12/03/12 → 12/03/12

Belgeye Erişim

10.1007/978-3-642-28534-9_10

Diğer Dosyalar ve Linkler

Link to publication in Scopus

Alıntı Yap

Beşiktaş, C., & Mantar, H. A. (2012). Real-time traffic classification based on cosine similarity using sub-application vectors. In Traffic Monitoring and Analysis - 4th International Workshop, TMA 2012, Proceedings (pp. 89-92). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Hac. 7189 LNCS). https://doi.org/10.1007/978-3-642-28534-9_10

Beşiktaş, Cihangir ; Mantar, Haci Ali. / Real-time traffic classification based on cosine similarity using sub-application vectors. Traffic Monitoring and Analysis - 4th International Workshop, TMA 2012, Proceedings. 2012. pp. 89-92 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{99833d529826436b94a30418e41852b4,

title = "Real-time traffic classification based on cosine similarity using sub-application vectors",

abstract = "Internet traffic classification has a critical role on network monitoring, quality of service, intrusion detection, network security and trend analysis. The conventional port-based method is ineffective due to dynamic port usage and masquerading techniques. Besides, payload-based method suffers from heavy load and encryption. Due to these facts, machine learning based statistical approaches have become the new trend for the network measurement community. In this short paper, we propose a new statistical approach based on DBSCAN clustering and weighted cosine similarity. Our experimental test results show that the proposed approach achieves very high accuracy.",

keywords = "cosine similarity, DBSCAN, packet inspection, Traffic classification",

author = "Cihangir Be{\c s}ikta{\c s} and Mantar, {Haci Ali}",

year = "2012",

doi = "10.1007/978-3-642-28534-9_10",

language = "English",

isbn = "9783642285332",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

pages = "89--92",

booktitle = "Traffic Monitoring and Analysis - 4th International Workshop, TMA 2012, Proceedings",

note = "4th International Workshop on Traffic Monitoring and Analysis, TMA 2012 ; Conference date: 12-03-2012 Through 12-03-2012",

}

Beşiktaş, C & Mantar, HA 2012, Real-time traffic classification based on cosine similarity using sub-application vectors. in Traffic Monitoring and Analysis - 4th International Workshop, TMA 2012, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), hac. 7189 LNCS, pp. 89-92, 4th International Workshop on Traffic Monitoring and Analysis, TMA 2012, Vienna, Austria, 12/03/12. https://doi.org/10.1007/978-3-642-28534-9_10

Real-time traffic classification based on cosine similarity using sub-application vectors. / Beşiktaş, Cihangir; Mantar, Haci Ali.
Traffic Monitoring and Analysis - 4th International Workshop, TMA 2012, Proceedings. 2012. p. 89-92 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Hac. 7189 LNCS).

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › bilirkişi

TY - GEN

T1 - Real-time traffic classification based on cosine similarity using sub-application vectors

AU - Beşiktaş, Cihangir

AU - Mantar, Haci Ali

PY - 2012

Y1 - 2012

N2 - Internet traffic classification has a critical role on network monitoring, quality of service, intrusion detection, network security and trend analysis. The conventional port-based method is ineffective due to dynamic port usage and masquerading techniques. Besides, payload-based method suffers from heavy load and encryption. Due to these facts, machine learning based statistical approaches have become the new trend for the network measurement community. In this short paper, we propose a new statistical approach based on DBSCAN clustering and weighted cosine similarity. Our experimental test results show that the proposed approach achieves very high accuracy.

AB - Internet traffic classification has a critical role on network monitoring, quality of service, intrusion detection, network security and trend analysis. The conventional port-based method is ineffective due to dynamic port usage and masquerading techniques. Besides, payload-based method suffers from heavy load and encryption. Due to these facts, machine learning based statistical approaches have become the new trend for the network measurement community. In this short paper, we propose a new statistical approach based on DBSCAN clustering and weighted cosine similarity. Our experimental test results show that the proposed approach achieves very high accuracy.

KW - cosine similarity

KW - DBSCAN

KW - packet inspection

KW - Traffic classification

UR - http://www.scopus.com/inward/record.url?scp=84858389149&partnerID=8YFLogxK

U2 - 10.1007/978-3-642-28534-9_10

DO - 10.1007/978-3-642-28534-9_10

M3 - Conference contribution

AN - SCOPUS:84858389149

SN - 9783642285332

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 89

EP - 92

BT - Traffic Monitoring and Analysis - 4th International Workshop, TMA 2012, Proceedings

T2 - 4th International Workshop on Traffic Monitoring and Analysis, TMA 2012

Y2 - 12 March 2012 through 12 March 2012

ER -

Beşiktaş C, Mantar HA. Real-time traffic classification based on cosine similarity using sub-application vectors. In Traffic Monitoring and Analysis - 4th International Workshop, TMA 2012, Proceedings. 2012. p. 89-92. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-642-28534-9_10

Real-time traffic classification based on cosine similarity using sub-application vectors

Özet

Yayın serisi

???event.eventtypes.event.conference???

Belgeye Erişim

Diğer Dosyalar ve Linkler

Parmak izi

Alıntı Yap