Support Vector Machines for TCP traffic classification

By Alice Este, Francesco Gringoli, Luca Salgarelli. 2009

287 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Hardware and Architecture

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Giuseppe Ateniese
2. Jun Zhang
3. Yang Xiang
4. Mohammad Ali Salahuddin
5. Sara Ayoubi
6. Luigi Vincenzo Mancini
7. Raouf Boutaba
8. Wanlei Zhou

In Computer Networks and Communications

1. Jun Zhang
2. Yang Xiang
3. Mohammad Ali Salahuddin
4. Sara Ayoubi
5. Luigi Vincenzo Mancini
6. Giuseppe Ateniese
7. Raouf Boutaba
8. Wanlei Zhou

In Hardware and Architecture

1. Raouf Boutaba
2. Wanlei Zhou
3. Yong Guan
4. Jun Zhang
5. Yang Wang
6. Yang Xiang

Citations in the corpus

• Hacking smart machines with smarter ones: How to extract meaningful data from machine learning classifiers (2013, methodology): “For example, classification and regression models are employed to analyze current and historical trends to make predictions in financial markets [24, 33, 8], to study biological problems [54], to support medical diagnosis [30, 42, 57], to classify network traffic or detect anomalies [22, 28, 39, 12,”
• Network Traffic Classification Using Correlation Information (2013, methodology): “[21] applied oneclass SVMs to traffic classification and presented a simple optimization algorithm for each set of SVM working parameters.”
• A comprehensive survey on machine learning for networking: evolution, applications and research opportunities (2018, methodology): “[140] Supervised SVM LBNL [262], CAIDA [451], proprietary: campus network Packet payload size HTTP, SMTP, POP3, HTTPS, IMAPS, BitTorrent, FTP, MSN, eDonkey, SSL, SMB, Kazaa, Gnutella, NNTP, DNS, LDAP, SSH Number of support vectors cf.”