Serverless computing has gained widespread popularity among developers due to its low cost, fine-grained deployment, and management-free operation. However, when deploying serverless applications in practice, a single function is often insufficient to fulfill complete application requirements. This has led to the emergence of serverless workflows, which orchestrate a series of related serverless functions according to predefined logic. Through our investigation of existing serverless workflow platforms, we identify two major security limitations. First, current serverless workflows cannot guarantee execution integrity—they are unable to detect changes in the function execution order and lack mechanisms to defend against workflow-targeted denial-of-service (DoS) attacks. Second, identity management is typically coarse-grained, often resulting in over-privileged access and lacking support for function-level access control.To address these issues, we propose and implement the LASEFlow, a label-aware security enhancement framework for serverless workflows. A sequential function execution chain is designed based on the Platform Configuration Register (PCR) technique to guarantee the integrity of the execution of workflow in LASEFlow. In addition, a fine-grained function-level access control mechanism is designed to prevent privilege abuse in work-flows. The evaluation demonstrates the effectiveness of LASEFlow against workflow attacks, with an overhead of less than 4% in performance.

Website fingerprinting (WF) attacks can reveal the users' online privacy by the traffic analysis technique, even with the protection of the Tor anonymity network. Recent WF attacks tend to leverage the deep learning (DL) models, which require a large number of traffic samples for training. In this case, it is impractical for low-resource adversaries in reality. Thus, we propose a lightweight WF attack to tackle this challenge, i.e., Deep Quadruplet Fingerprinting (DQF), which only needs one training sample to obtain an accuracy of 87.1%. Regarding the overall design, DQF first combines the metric learning and meta-learning schemes. To improve the generalization ability of the trained model, DQF leverages the quadruplet networks as the architecture and modifies the quadruplet loss function. Besides, by taking the deep local fingerprinting features (DLFFs), DQF avoids losing a lot of discriminative information, which is a problem with previous attacks. To evaluate DQF, we use multiple typical datasets and conduct 11 different experiments. In closed-world settings, the accuracy of DQF can exceed the best baseline attack by 10%. In open-world settings, DQF steadily performs the best even in the most challenging scenario, namely, 1-shot learning, where previous attacks significantly degrade the performance or even fail.

Website fingerprinting (WF) attacks have become a significant threat to users’ privacy, even against Tor, one of the most famous anonymous communication tools. However, some limitations have prevented them from applying to the real world. A severe limitation for traditional deep-learning-based WF attacks is that a large amount of training data is required to gain classification capabilities. In this paper, we considered a more practical setting where the attacker could only obtain a few traffic trace instances for each target website, called the few-shot WF problem. Unlike previous work using transfer learning and demanding additional pre-training data, we leveraged data augmentation to generate additional virtual samples from the training sample neighborhood. It can expand the support for the distribution of training data, thereby solving the data hunger problem of deep-learning-based WF attacks. Specifically, we proposed a new augmentation method called Trace-Form Based Refill (TForm-RF), which is tailored to the intrinsic properties of website traffic trace. From cell sequences, we extract TForms to figuratively represent traffic patterns and refill them randomly to generate meaningful new instances. We evaluate this idea with several reasonable experiments. We illustrated that TForm-RF is much more effective than prior HDA and has a competitive advantage over TF in both closed-world and open-world scenarios.

This work introduces a novel data augmentation method for few-shot website fingerprinting (WF) attack where only a handful of training samples per website are available for deep learning model optimization. Moving beyond earlier WF methods relying on manually-engineered feature representations, more advanced deep learning alternatives demonstrate that learning feature representations automatically from training data is superior. Nonetheless, this advantage is subject to an unrealistic assumption that there exist many training samples per website, which otherwise will disappear. To address this, we introduce a model-agnostic, efficient, and harmonious data augmentation (HDA) method that can improve deep WF attacking methods significantly. HDA involves both intrasample and intersample data transformations that can be used in a harmonious manner to expand a tiny training dataset to an arbitrarily large collection, therefore effectively and explicitly addressing the intrinsic data scarcity problem. We conducted expensive experiments to validate our HDA for boosting state-of-the-art deep learning WF attack models in both closed-world and open-world attacking scenarios, at absence and presence of strong defense. For instance, in the more challenging and realistic evaluation scenario with WTF-PAD-based defense, our HDA method surpasses the previous state-of-the-art results by nearly 3% in classification accuracy in the 20-shot learning case. An earlier version of this work Chen et al. (2021) has been presented as preprint in ArXiv (https://arxiv.org/abs/2101.10063).