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AI-based Fingerprinting over Stream, Cache and RF Signals

Li, Haipeng
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1692282362172007

Abstract Details

2023, PhD, University of Cincinnati, Engineering and Applied Science: Computer Science and Engineering.
Fingerprinting is a technique that identifies websites, software and devices by leveraging a group of information from users. An attacker can acquire users' secrets by only analyzing side-channel features from a system, such as network packet size and direction, power usage or CPU usage. In traditional fingerprinting attacks, a large amount of human effort is required as an attacker has to manually extract effective features for attacking purpose. This kind of attacking approach is easy to be defended as a defender can invalidate the attack by modifying the target features that are used in the attack. However, for AI-based fingerprinting, handcrafted feature is not necessary anymore. An attacker can train a machine learning classifier over raw data directly and achieve an impressive classification results. In this proposal, I propose to design effective and efficient defenses against deep neural network based fingerprinting attack. Firstly, I propose to improve the efficiency of existing defense against neural network based stream fingerprinting. Many defense algorithms have been proposed to defeat stream fingerprinting. However, most of those existing defense algorithms need extremely high bandwidth overhead in order to make the defense effective. In this dissertation, I leverage feature selection methods to analyze the feature space in stream fingerprinting. Instead of treating network packets equally when adding noises, we distinguish important packets using feature selection algorithms and add more noise to those important packets. Secondly, I propose to design an efficient defense against CPU cache based website fingerprinting. Recently, a new attack was proposed to monitor the cache occupancy of the Last Level Cache on a user’s CPU. Although a defense was proposed, it is not effective when an attacker adapts the classifier with defended data. In this dissertation, I investigate the behavior of cache occupancy channel and reveal the reason why current defense algorithm fails to protect user's privacy. Based on our observation, I propose a new defense algorithm that can defeat the cache occupancy attack even an attacker can retrain the classifier with defended dataset. Thirdly, I propose to improve the robustness of radio fingerprinting. Existing deep neural network based radio frequency (RF) fingerprinting suffers from performance reduction in a cross-day scenario where the training the test dataset are collected from different days. I investigate how pre-processing raw I/Q data can impact the performance of radio fingerprinting. Moreover, I propose to leverage transfer learning techniques to improve the robustness of deep neural network based radio fingerprinting in a cross-day scenario. At last, I investigate how to improve the portability of deep neural networks in side-channel attacks. In general, a large neural network with millions of trainable parameters is preferred in side-channel attacks. However, it can be extremely resource-consuming to train or test such large networks. In this task, I propose a novel structured pruning algorithm to reduce the size of a pre-defined CNN model. Our method can customize the pruning rate for a layer based on statistical information of weights and can generate a pruned model in one-shot.
Boyang Wang, Ph.D. (Committee Chair)
Nirnimesh Ghose, Ph.D. (Committee Member)
Tingting Yu, Ph.D. (Committee Member)
Nan Niu, Ph.D. (Committee Member)
Wen-Ben Jone, Ph.D. (Committee Member)
143 p.
Computer Engineering
Artificial Intelligence; Side-channel Attacks; Fingerprinting; Deep Neural Networks

Recommended Citations

Citations

  • Li, H. (2023). AI-based Fingerprinting over Stream, Cache and RF Signals [Doctoral dissertation, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1692282362172007

    APA Style (7th edition)

  • Li, Haipeng. AI-based Fingerprinting over Stream, Cache and RF Signals. 2023. University of Cincinnati, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1692282362172007.

    MLA Style (8th edition)

  • Li, Haipeng. "AI-based Fingerprinting over Stream, Cache and RF Signals." Doctoral dissertation, University of Cincinnati, 2023. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1692282362172007

    Chicago Manual of Style (17th edition)

ucin1692282362172007
91
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This open access ETD is published by University of Cincinnati and OhioLINK.