DFD: Adversarial Learning-based Approach to Defend Against Website Fingerprinting

Abusnaina, Ahmed; Jang, Rhongho; Khormali, Aminollah; Nyang, DaeHun; Mohaisen, Aziz

doi:10.1109/infocom41043.2020.9155465

Cited by 35 publications

(8 citation statements)

References 28 publications

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“…GLUE added dummy packets between separate traces so that they appeared to the attacker as a long consecutive trace, rendering the attacker unable to find their start or end points. Abusnaina et al [138] proposed a novel defense mechanism using a per-burst injection technique, called Deep Fingerprinting Defender (DFD), against deep learning-based WF attacks, which had two operation modes, one-way and two-way injection. DFD was designed to break the inherent patterns preserved in Tor traffic traces by carefully injecting dummy packets within every burst.…”

Section: B Approachesmentioning

confidence: 99%

A Survey on Deep Learning for Website Fingerprinting Attacks and Defenses

Liu

2023

IEEE Access

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The attacks and defenses on the information of which website pages are visited by users are important research subjects in the field of privacy enhancing technologies, they are termed as website fingerprinting (WF) attacks and defenses. Nowadays, deep learning is an important tool in many research areas, including WF attacks and defenses. In this paper, we offer a comprehensive survey on deep learning for WF attacks and defenses. After a brief introduction, we first summarize deep learning, WF attacks, and WF defenses. For deep learning, we review the common paradigms, architectures, and performance metrics. For WF attacks, we review the approaches, challenges and solutions. The approaches include deep learning, traditional machine learning, and other methods. Challenges and solutions cover multi-tab browsing, concept drift, and the base rate fallacy. For WF defenses, we review the strategies and approaches. Then, we survey deep learning for WF attacks, and deep learning for WF defenses. In deep learning for WF attacks, we survey in detail the deep learning paradigms, architectures of WF attack models, and the performance of several representative WF attack models, and look into the future. In deep learning for WF defenses, we survey the architecture, efficacy and overhead of deep learning models in WF defenses, and look into the future. In the end, we summarize this paper.

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Section: B Approachesmentioning

confidence: 99%

A Survey on Deep Learning for Website Fingerprinting Attacks and Defenses

Liu

2023

IEEE Access

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“…e latest defense method (DFD) [14] injects a dummy burst sequence according to the previous burst sequence in the real-time traffic. When the injection method is the server-side injection method, the attack success rate using CNN as the attack model is reduced from 99.93% to 5% with 85.56% bandwidth overhead.…”

Section: Website Fingerprintingmentioning

confidence: 99%

“…In the meantime, some WF defenses have been proposed such as WTF-PAD [12], walkie-talkie (W-T) [13], and Deep Fingerprinting Defender (DFD) [14]. In addition, researchers have found that deep learning models are vulnerable to adversarial examples [15].…”

Section: Introductionmentioning

confidence: 99%

SAD: Website Fingerprinting Defense Based on Adversarial Examples

Tang

Shen

Guo

et al. 2022

Security and Communication Networks

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Website fingerprinting (WF) attacks can infer website names from encrypted network traffic when the victim is browsing the website. Inherent defenses of anonymous communication systems such as The Onion Router(Tor) cannot compete with current WF attacks. The state-of-the-art attack based on deep learning can gain over 98% accuracy in Tor. Most of the defenses have excellent defensive capabilities, but it will bring a relatively high bandwidth overhead, which will seriously affect the user’s network experience. And some defense methods have less impact on the latest website fingerprinting attacks. Defense-based adversarial examples have excellent defense capabilities and low bandwidth overhead, but they need to get the complete website traffic to generate defense data, which is obviously impractical. In this article, based on adversarial examples, we propose segmented adversary defense (SAD) for deep learning-based WF attacks. In SAD, sequence data are divided into multiple segments to ensure that SAD is feasible in real scenarios. Then, the adversarial examples for each segment of data can be generated by SAD. Finally, dummy packets are inserted after each segment original data. We also found that setting different head rates, that is, end points for the segments, will get better results. Experimentally, our results show that SAD can effectively reduce the accuracy of WF attacks. The technique drops the accuracy of the state-of-the-art attack hardened from 96% to 3% while incurring only 40% bandwidth overhead. Compared with the existing proposed defense named Deep Fingerprinting Defender (DFD), the defense effect of SAD is better under the same bandwidth overhead.

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“…Matthews, Sirinam, and Wright used this framework to implement the "Random Extend Burst" (REB) defense mechanism [25], and Pulls used genetic algorithms to find the locally optimal padding machines Spring and Interspace [28]. Several other defenses [4,5,11] also use padding to disrupt fingerprints, but none have been implemented in the circuit padding framework.…”

Section: Defensesmentioning

confidence: 99%

Padding-only defenses add delay in Tor

Witwer¹,

Holland²,

Hopper³

2022

Preprint

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Website fingerprinting is an attack that uses size and timing characteristics of encrypted downloads to identify targeted websites. Since this can defeat the privacy goals of anonymity networks such as Tor, many algorithms to defend against this attack in Tor have been proposed in the literature. These algorithms typically consist of some combination of the injection of dummy "padding" packets with the delay of actual packets to disrupt timing patterns. For usability reasons, Tor is intended to provide low latency; as such, many authors focus on padding-only defenses in the belief that they are "zero-delay. " We demonstrate through Shadow simulations that by increasing queue lengths, padding-only defenses add delay when deployed network-wide, so they should not be considered "zerodelay. " We further argue that future defenses should also be evaluated using network-wide deployment simulations. CCS CONCEPTS• Security and privacy → Pseudonymity, anonymity and untraceability;

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DFD: Adversarial Learning-based Approach to Defend Against Website Fingerprinting

Cited by 35 publications

References 28 publications

A Survey on Deep Learning for Website Fingerprinting Attacks and Defenses

A Survey on Deep Learning for Website Fingerprinting Attacks and Defenses

SAD: Website Fingerprinting Defense Based on Adversarial Examples

Padding-only defenses add delay in Tor

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