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Mobile Social Networking under Side-Channel Attacks: Practical Security Challenges

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Mobile Social Networking under Side-Channel Attacks : Practical Security Challenges. / Ometov, Aleksandr; Levina, Alla; Borisenko, Pavel; Mostovoy, Roman; Orsino, Antonino; Andreev, Sergey.

In: IEEE Access, Vol. 5, 2017, p. 2591-2601.

Research output: Contribution to journalArticleScientificpeer-review

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Ometov, Aleksandr ; Levina, Alla ; Borisenko, Pavel ; Mostovoy, Roman ; Orsino, Antonino ; Andreev, Sergey. / Mobile Social Networking under Side-Channel Attacks : Practical Security Challenges. In: IEEE Access. 2017 ; Vol. 5. pp. 2591-2601.

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@article{a63568af617241dfa981bf83d1c245fe,
title = "Mobile Social Networking under Side-Channel Attacks: Practical Security Challenges",
abstract = "Mobile social networks (MSNs) are the networks of individuals with similar interests connected to each other through their mobile devices. Recently, MSNs are proliferating fast supported by emerging wireless technologies that allow to achieve more efficient communication and better networking performance across the key parameters, such as lower delay, higher data rate, and better coverage. At the same time, most of the MSN users do not fully recognize the importance of security on their handheld mobile devices. Due to this fact, multiple attacks aimed at capturing personal information and sensitive user data become a growing concern, fueled by the avalanche of new MSN applications and services. Therefore, the goal of this work is to understand whether the contemporary user equipment is susceptible to compromising its sensitive information to the attackers. As an example, various information security algorithms implemented in modern smartphones are thus tested to attempt the extraction of the said private data based on the traces registered with inexpensive contemporary audio cards. Our obtained results indicate that the sampling frequency, which constitutes the strongest limitation of the off-the-shelf side-channel attack equipment, only delivers low-informative traces. However, the success chances to recover sensitive data stored within a mobile device may increase significantly when utilizing more efficient analytical techniques as well as employing more complex attack equipment. Finally, we elaborate on the possible utilization of neural networks to improve the corresponding encrypted data extraction process, while the latter part of this paper outlines solutions and practical recommendations to protect from malicious side-channel attacks and keep the personal user information protected.",
keywords = "Information systems security, Mobile social networks (MSNs), Neural networks, Side-channel attacks, Social networking services",
author = "Aleksandr Ometov and Alla Levina and Pavel Borisenko and Roman Mostovoy and Antonino Orsino and Sergey Andreev",
note = "INT=elt,{"}Orsino, Antonino{"}",
year = "2017",
doi = "10.1109/ACCESS.2017.2665640",
language = "English",
volume = "5",
pages = "2591--2601",
journal = "IEEE Access",
issn = "2169-3536",
publisher = "Institute of Electrical and Electronics Engineers",

}

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TY - JOUR

T1 - Mobile Social Networking under Side-Channel Attacks

T2 - Practical Security Challenges

AU - Ometov, Aleksandr

AU - Levina, Alla

AU - Borisenko, Pavel

AU - Mostovoy, Roman

AU - Orsino, Antonino

AU - Andreev, Sergey

N1 - INT=elt,"Orsino, Antonino"

PY - 2017

Y1 - 2017

N2 - Mobile social networks (MSNs) are the networks of individuals with similar interests connected to each other through their mobile devices. Recently, MSNs are proliferating fast supported by emerging wireless technologies that allow to achieve more efficient communication and better networking performance across the key parameters, such as lower delay, higher data rate, and better coverage. At the same time, most of the MSN users do not fully recognize the importance of security on their handheld mobile devices. Due to this fact, multiple attacks aimed at capturing personal information and sensitive user data become a growing concern, fueled by the avalanche of new MSN applications and services. Therefore, the goal of this work is to understand whether the contemporary user equipment is susceptible to compromising its sensitive information to the attackers. As an example, various information security algorithms implemented in modern smartphones are thus tested to attempt the extraction of the said private data based on the traces registered with inexpensive contemporary audio cards. Our obtained results indicate that the sampling frequency, which constitutes the strongest limitation of the off-the-shelf side-channel attack equipment, only delivers low-informative traces. However, the success chances to recover sensitive data stored within a mobile device may increase significantly when utilizing more efficient analytical techniques as well as employing more complex attack equipment. Finally, we elaborate on the possible utilization of neural networks to improve the corresponding encrypted data extraction process, while the latter part of this paper outlines solutions and practical recommendations to protect from malicious side-channel attacks and keep the personal user information protected.

AB - Mobile social networks (MSNs) are the networks of individuals with similar interests connected to each other through their mobile devices. Recently, MSNs are proliferating fast supported by emerging wireless technologies that allow to achieve more efficient communication and better networking performance across the key parameters, such as lower delay, higher data rate, and better coverage. At the same time, most of the MSN users do not fully recognize the importance of security on their handheld mobile devices. Due to this fact, multiple attacks aimed at capturing personal information and sensitive user data become a growing concern, fueled by the avalanche of new MSN applications and services. Therefore, the goal of this work is to understand whether the contemporary user equipment is susceptible to compromising its sensitive information to the attackers. As an example, various information security algorithms implemented in modern smartphones are thus tested to attempt the extraction of the said private data based on the traces registered with inexpensive contemporary audio cards. Our obtained results indicate that the sampling frequency, which constitutes the strongest limitation of the off-the-shelf side-channel attack equipment, only delivers low-informative traces. However, the success chances to recover sensitive data stored within a mobile device may increase significantly when utilizing more efficient analytical techniques as well as employing more complex attack equipment. Finally, we elaborate on the possible utilization of neural networks to improve the corresponding encrypted data extraction process, while the latter part of this paper outlines solutions and practical recommendations to protect from malicious side-channel attacks and keep the personal user information protected.

KW - Information systems security

KW - Mobile social networks (MSNs)

KW - Neural networks

KW - Side-channel attacks

KW - Social networking services

U2 - 10.1109/ACCESS.2017.2665640

DO - 10.1109/ACCESS.2017.2665640

M3 - Article

VL - 5

SP - 2591

EP - 2601

JO - IEEE Access

JF - IEEE Access

SN - 2169-3536

ER -