Abstract
During the past decade, a great number of machine learning (ML)-based methods have been studied for accurate traffic classification. Flow features such as the discretizations of the first five packet sizes (PS) and flow ports (FP) are considered the best discriminators for per-flow classification. For the first time, this letter proposes to treat the first n-bits of a flow (BitFlow) as features and compares its overall performance with the well-known ACAS (automated construction of application signatures) that takes the first n-bytes of a flow (ByteFlow) as features. The results show that BitFlow achieves not only a higher classification accuracy but also 1-3 orders of magnitude faster speed than ACAS in training and classifying. More importantly, this letter also proposes to treat the first n-bits of each of the first few packet payloads (BitPack) as features, which enables a user-adjustable tradeoff between user privacy protection and classification accuracy maximization. The experiments show that BitPack can significantly outperform BitFlow, PS, and FP.
| Original language | English (US) |
|---|---|
| Article number | 7393470 |
| Pages (from-to) | 704-707 |
| Number of pages | 4 |
| Journal | IEEE Communications Letters |
| Volume | 20 |
| Issue number | 4 |
| DOIs | |
| State | Published - Apr 1 2016 |
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Keywords
- bits as features
- ML
- Traffic classification
ASJC Scopus subject areas
- Electrical and Electronic Engineering
- Computer Science Applications
- Modeling and Simulation
Cite this
Bits Learning : User-Adjustable Privacy Versus Accuracy in Internet Traffic Classification. / Yuan, Zhenlong; Xu, Jie; Xue, Yibo; Van Der Schaar, Mihaela.
In: IEEE Communications Letters, Vol. 20, No. 4, 7393470, 01.04.2016, p. 704-707.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Bits Learning
T2 - User-Adjustable Privacy Versus Accuracy in Internet Traffic Classification
AU - Yuan, Zhenlong
AU - Xu, Jie
AU - Xue, Yibo
AU - Van Der Schaar, Mihaela
PY - 2016/4/1
Y1 - 2016/4/1
N2 - During the past decade, a great number of machine learning (ML)-based methods have been studied for accurate traffic classification. Flow features such as the discretizations of the first five packet sizes (PS) and flow ports (FP) are considered the best discriminators for per-flow classification. For the first time, this letter proposes to treat the first n-bits of a flow (BitFlow) as features and compares its overall performance with the well-known ACAS (automated construction of application signatures) that takes the first n-bytes of a flow (ByteFlow) as features. The results show that BitFlow achieves not only a higher classification accuracy but also 1-3 orders of magnitude faster speed than ACAS in training and classifying. More importantly, this letter also proposes to treat the first n-bits of each of the first few packet payloads (BitPack) as features, which enables a user-adjustable tradeoff between user privacy protection and classification accuracy maximization. The experiments show that BitPack can significantly outperform BitFlow, PS, and FP.
AB - During the past decade, a great number of machine learning (ML)-based methods have been studied for accurate traffic classification. Flow features such as the discretizations of the first five packet sizes (PS) and flow ports (FP) are considered the best discriminators for per-flow classification. For the first time, this letter proposes to treat the first n-bits of a flow (BitFlow) as features and compares its overall performance with the well-known ACAS (automated construction of application signatures) that takes the first n-bytes of a flow (ByteFlow) as features. The results show that BitFlow achieves not only a higher classification accuracy but also 1-3 orders of magnitude faster speed than ACAS in training and classifying. More importantly, this letter also proposes to treat the first n-bits of each of the first few packet payloads (BitPack) as features, which enables a user-adjustable tradeoff between user privacy protection and classification accuracy maximization. The experiments show that BitPack can significantly outperform BitFlow, PS, and FP.
KW - bits as features
KW - ML
KW - Traffic classification
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UR - http://www.scopus.com/inward/citedby.url?scp=84963959776&partnerID=8YFLogxK
U2 - 10.1109/LCOMM.2016.2521837
DO - 10.1109/LCOMM.2016.2521837
M3 - Article
VL - 20
SP - 704
EP - 707
JO - IEEE Communications Letters
JF - IEEE Communications Letters
SN - 1089-7798
IS - 4
M1 - 7393470
ER -