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author:

Chen, Xiang (Chen, Xiang.) [1] | Liu, Hongyan (Liu, Hongyan.) [2] | Huang, Qun (Huang, Qun.) [3] | Zhang, Dong (Zhang, Dong.) [4] (Scholars:张栋) | Zhou, Haifeng (Zhou, Haifeng.) [5] | Wu, Chunming (Wu, Chunming.) [6] | Liu, Xuan (Liu, Xuan.) [7] | Khan, Muhammad Khurram (Khan, Muhammad Khurram.) [8]

Indexed by:

EI Scopus SCIE

Abstract:

Due to limited memory usage and provably high accuracy, sketches running on programmable switches have been commonly used by the literature for network measurement. However, their vulnerabilities are still largely unknown and neglected, which is highly concerning given the increasing popularity of network measurement. In this paper, we identify the Stalker attacks, where attackers aim to degrade the accuracy of sketches running on programmable switches. More precisely, attackers tamper with some sketch operations during sketch deployment atop programmable switches. At runtime, the tampered sketch will record highly inaccurate flow data, which degrades measurement accuracy. We implement Stalker attacks on Tofino switches. The results indicate that Stalker attacks significantly drop the accuracy of network management applications, e.g., reducing the F1 score of heavy hitter detection to zero. However, our analysis indicates that none of existing methods can detect Stalker attacks since they can hardly verify the correctness of sketch operations. Finally, we analyze potential defense mechanisms and identify challenges to enable further research in this context.

Keyword:

attacks Programmable switches sketches

Community:

  • [ 1 ] [Chen, Xiang]Zhejiang Univ City Coll, Coll Comp Sci & Technol, Hangzhou 310058, Peoples R China
  • [ 2 ] [Liu, Hongyan]Zhejiang Univ City Coll, Coll Comp Sci & Technol, Hangzhou 310058, Peoples R China
  • [ 3 ] [Wu, Chunming]Zhejiang Univ City Coll, Coll Comp Sci & Technol, Hangzhou 310058, Peoples R China
  • [ 4 ] [Huang, Qun]Peking Univ, Dept Comp Sci & Technol, Beijing 100871, Peoples R China
  • [ 5 ] [Zhang, Dong]Fuzhou Univ, Coll Comp Sci & Big Data, Fuzhou 350002, Peoples R China
  • [ 6 ] [Zhang, Dong]Fuzhou Univ, Zhicheng Coll, Fuzhou 350002, Peoples R China
  • [ 7 ] [Zhou, Haifeng]Zhejiang Univ, Coll Control Sci & Engn, Hangzhou 310027, Peoples R China
  • [ 8 ] [Liu, Xuan]Yangzhou Univ, Coll Informat Engn, Coll Artificial Intelligence, Yangzhou 225009, Peoples R China
  • [ 9 ] [Liu, Xuan]Southeast Univ, Sch Comp Sci & Engn, Nanjing 211189, Peoples R China
  • [ 10 ] [Khan, Muhammad Khurram]King Saud Univ, Ctr Excellence Informat Assurance CoEIA, Riyadh 11421, Saudi Arabia

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Source :

IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY

ISSN: 1556-6013

Year: 2023

Volume: 18

Page: 5832-5847

6 . 3

JCR@2023

6 . 3 0 0

JCR@2023

JCR Journal Grade:1

CAS Journal Grade:1

Cited Count:

WoS CC Cited Count: 1

SCOPUS Cited Count: 1

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 0

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