Smartphone reconnaissance: Operating system identification

Nicholas Ruffing; Ye Zhu; Rudy Libertini; Yong Guan; Riccardo Bettati

doi:10.1109/CCNC.2016.7444941

Smartphone reconnaissance: Operating system identification

Nicholas Ruffing
, Ye Zhu
, Rudy Libertini
, Yong Guan
, Riccardo Bettati

Cleveland State University
Iowa State University
Texas A&M University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

10 Scopus citations

Abstract

Smartphone reconnaissance, the first step to launch security attacks to a target smartphone, enables an adversary to tailor attacks by exploiting known vulnerabilities of the target system. We investigate OS identification against smartphones that use encrypted traffic. A traffic content agnostic identification algorithm is proposed that is based on the spectral analysis of the encrypted traffic. The identification algorithm is designed for high identification accuracy by removing noise frequency components and for high efficiency in terms of computation complexity. We evaluate the identification algorithm against collected smartphone traffic. The experiment results show that the algorithm can identify the smartphone OS accurately. The identification accuracy can reach 100% with only 30 seconds of smartphone traffic.

Original language	English
Title of host publication	2016 13th IEEE Annual Consumer Communications and Networking Conference, CCNC 2016
Place of Publication	usa
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1086-1091
Number of pages	6
ISBN (Electronic)	9781467392921
DOIs	https://doi.org/10.1109/CCNC.2016.7444941
State	Published - Mar 30 2016
Event	13th IEEE Annual Consumer Communications and Networking Conference, CCNC 2016 - Las Vegas, United States Duration: Jan 6 2016 → Jan 13 2016

Conference

Conference	13th IEEE Annual Consumer Communications and Networking Conference, CCNC 2016
Country/Territory	United States
City	Las Vegas
Period	01/6/16 → 01/13/16

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10.1109/CCNC.2016.7444941

Cite this

@inproceedings{cd1616d3b4cd4e8fa9c047d98a504103,

title = "Smartphone reconnaissance: Operating system identification",

abstract = "Smartphone reconnaissance, the first step to launch security attacks to a target smartphone, enables an adversary to tailor attacks by exploiting known vulnerabilities of the target system. We investigate OS identification against smartphones that use encrypted traffic. A traffic content agnostic identification algorithm is proposed that is based on the spectral analysis of the encrypted traffic. The identification algorithm is designed for high identification accuracy by removing noise frequency components and for high efficiency in terms of computation complexity. We evaluate the identification algorithm against collected smartphone traffic. The experiment results show that the algorithm can identify the smartphone OS accurately. The identification accuracy can reach 100\% with only 30 seconds of smartphone traffic.",

author = "Nicholas Ruffing and Ye Zhu and Rudy Libertini and Yong Guan and Riccardo Bettati",

year = "2016",

month = mar,

day = "30",

doi = "10.1109/CCNC.2016.7444941",

language = "English",

pages = "1086--1091",

booktitle = "2016 13th IEEE Annual Consumer Communications and Networking Conference, CCNC 2016",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

note = "13th IEEE Annual Consumer Communications and Networking Conference, CCNC 2016 ; Conference date: 06-01-2016 Through 13-01-2016",

}

Ruffing, N, Zhu, Y, Libertini, R, Guan, Y & Bettati, R 2016, Smartphone reconnaissance: Operating system identification. in 2016 13th IEEE Annual Consumer Communications and Networking Conference, CCNC 2016., 7444941, Institute of Electrical and Electronics Engineers Inc., usa, pp. 1086-1091, 13th IEEE Annual Consumer Communications and Networking Conference, CCNC 2016, Las Vegas, United States, 01/6/16. https://doi.org/10.1109/CCNC.2016.7444941

Smartphone reconnaissance: Operating system identification. / Ruffing, Nicholas; Zhu, Ye; Libertini, Rudy et al.
2016 13th IEEE Annual Consumer Communications and Networking Conference, CCNC 2016. usa: Institute of Electrical and Electronics Engineers Inc., 2016. p. 1086-1091 7444941.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Smartphone reconnaissance: Operating system identification

AU - Ruffing, Nicholas

AU - Zhu, Ye

AU - Libertini, Rudy

AU - Guan, Yong

AU - Bettati, Riccardo

PY - 2016/3/30

Y1 - 2016/3/30

N2 - Smartphone reconnaissance, the first step to launch security attacks to a target smartphone, enables an adversary to tailor attacks by exploiting known vulnerabilities of the target system. We investigate OS identification against smartphones that use encrypted traffic. A traffic content agnostic identification algorithm is proposed that is based on the spectral analysis of the encrypted traffic. The identification algorithm is designed for high identification accuracy by removing noise frequency components and for high efficiency in terms of computation complexity. We evaluate the identification algorithm against collected smartphone traffic. The experiment results show that the algorithm can identify the smartphone OS accurately. The identification accuracy can reach 100% with only 30 seconds of smartphone traffic.

AB - Smartphone reconnaissance, the first step to launch security attacks to a target smartphone, enables an adversary to tailor attacks by exploiting known vulnerabilities of the target system. We investigate OS identification against smartphones that use encrypted traffic. A traffic content agnostic identification algorithm is proposed that is based on the spectral analysis of the encrypted traffic. The identification algorithm is designed for high identification accuracy by removing noise frequency components and for high efficiency in terms of computation complexity. We evaluate the identification algorithm against collected smartphone traffic. The experiment results show that the algorithm can identify the smartphone OS accurately. The identification accuracy can reach 100% with only 30 seconds of smartphone traffic.

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Smartphone reconnaissance: Operating system identification

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