arXiv subjects

Zecheng He

Publications and source records attributed to Zecheng He.

26 records · Page 2Linked to original sources

arXiv2021

Smartphone Impostor Detection with Behavioral Data Privacy and Minimalist Hardware Support

Impostors are attackers who take over a smartphone and gain access to the legitimate user's confidential and private information. This paper proposes a defense-in-depth mechanism to detect impostors quickly with simple Deep Learning algorithms, which can achieve better detection accuracy than the best prior work which used Machine Learning algorithms requiring computation of multiple features. Different from previous work, we then consider protecting the privacy of a user's behavioral (sensor) data by not exposing it outside the smartphone. For this scenario, we propose a Recurrent Neural Network (RNN) based Deep Learning algorithm that uses only the legitimate user's sensor data to learn his/her normal behavior. We propose to use Prediction Error Distribution (PED) to enhance the detection accuracy. We also show how a minimalist hardware module, dubbed SID for Smartphone Impostor Detector, can be designed and integrated into smartphones for self-contained impostor detection. Experimental results show that SID can support real-time impostor detection, at a very low hardware cost and energy consumption, compared to other RNN accelerators.

cs.CR↗

arXiv2021

ActionBert: Leveraging User Actions for Semantic Understanding of User Interfaces

As mobile devices are becoming ubiquitous, regularly interacting with a variety of user interfaces (UIs) is a common aspect of daily life for many people. To improve the accessibility of these devices and to enable their usage in a variety of settings, building models that can assist users and accomplish tasks through the UI is vitally important. However, there are several challenges to achieve this. First, UI components of similar appearance can have different functionalities, making understanding their function more important than just analyzing their appearance. Second, domain-specific features like Document Object Model (DOM) in web pages and View Hierarchy (VH) in mobile applications provide important signals about the semantics of UI elements, but these features are not in a natural language format. Third, owing to a large diversity in UIs and absence of standard DOM or VH representations, building a UI understanding model with high coverage requires large amounts of training data. Inspired by the success of pre-training based approaches in NLP for tackling a variety of problems in a data-efficient way, we introduce a new pre-trained UI representation model called ActionBert. Our methodology is designed to leverage visual, linguistic and domain-specific features in user interaction traces to pre-train generic feature representations of UIs and their components. Our key intuition is that user actions, e.g., a sequence of clicks on different UI components, reveals important information about their functionality. We evaluate the proposed model on a wide variety of downstream tasks, ranging from icon classification to UI component retrieval based on its natural language description. Experiments show that the proposed ActionBert model outperforms multi-modal baselines across all downstream tasks by up to 15.5%.

cs.CL↗

arXiv2020

New Models for Understanding and Reasoning about Speculative Execution Attacks

Spectre and Meltdown attacks and their variants exploit hardware performance optimization features to cause security breaches. Secret information is accessed and leaked through covert or side channels. New attack variants keep appearing and we do not have a systematic way to capture the critical characteristics of these attacks and evaluate why they succeed or fail. In this paper, we provide a new attack-graph model for reasoning about speculative execution attacks. We model attacks as ordered dependency graphs, and prove that a race condition between two nodes can occur if there is a missing dependency edge between them. We define a new concept, "security dependency", between a resource access and its prior authorization operation. We show that a missing security dependency is equivalent to a race condition between authorization and access, which is a root cause of speculative execution attacks. We show detailed examples of how our attack graph models the Spectre and Meltdown attacks, and is generalizable to all the attack variants published so far. This attack model is also very useful for identifying new attacks and for generalizing defense strategies. We identify several defense strategies with different performance-security tradeoffs. We show that the defenses proposed so far all fit under one of our defense strategies. We also explain how attack graphs can be constructed and point to this as promising future work for tool designers.

cs.CR↗

arXiv2020

Miss the Point: Targeted Adversarial Attack on Multiple Landmark Detection

Recent methods in multiple landmark detection based on deep convolutional neural networks (CNNs) reach high accuracy and improve traditional clinical workflow. However, the vulnerability of CNNs to adversarial-example attacks can be easily exploited to break classification and segmentation tasks. This paper is the first to study how fragile a CNN-based model on multiple landmark detection to adversarial perturbations. Specifically, we propose a novel Adaptive Targeted Iterative FGSM (ATI-FGSM) attack against the state-of-the-art models in multiple landmark detection. The attacker can use ATI-FGSM to precisely control the model predictions of arbitrarily selected landmarks, while keeping other stationary landmarks still, by adding imperceptible perturbations to the original image. A comprehensive evaluation on a public dataset for cephalometric landmark detection demonstrates that the adversarial examples generated by ATI-FGSM break the CNN-based network more effectively and efficiently, compared with the original Iterative FGSM attack. Our work reveals serious threats to patients' health. Furthermore, we discuss the limitations of our method and provide potential defense directions, by investigating the coupling effect of nearby landmarks, i.e., a major source of divergence in our experiments. Our source code is available at https://github.com/qsyao/attack_landmark_detection.

cs.CV↗

arXiv2020

Smartphone Impostor Detection with Built-in Sensors and Deep Learning

In this paper, we show that sensor-based impostor detection with deep learning can achieve excellent impostor detection accuracy at lower hardware cost compared to past work on sensor-based user authentication (the inverse problem) which used more conventional machine learning algorithms. While these methods use other smartphone users' sensor data to build the (user, non-user) classification models, we go further to show that using only the legitimate user's sensor data can still achieve very good accuracy while preserving the privacy of the user's sensor data (behavioral biometrics). For this use case, a key contribution is showing that the detection accuracy of a Recurrent Neural Network (RNN) deep learning model can be significantly improved by comparing prediction error distributions. This requires generating and comparing empirical probability distributions, which we show in an efficient hardware design. Another novel contribution is in the design of SID (Smartphone impostor Detection), a minimalist hardware accelerator that can be integrated into future smartphones for efficient impostor detection for different scenarios. Our SID module can implement many common Machine Learning and Deep Learning algorithms. SID is also scalable in parallelism and performance and easy to program. We show an FPGA prototype of SID, which can provide more than enough performance for real-time impostor detection, with very low hardware complexity and power consumption (one to two orders of magnitude less than related performance-oriented FPGA accelerators). We also show that the FPGA implementation of SID consumes 64.41X less energy than an implementation using the CPU with a GPU.

cs.CR↗

arXiv2019

Power-Grid Controller Anomaly Detection with Enhanced Temporal Deep Learning

Controllers of security-critical cyber-physical systems, like the power grid, are a very important class of computer systems. Attacks against the control code of a power-grid system, especially zero-day attacks, can be catastrophic. Earlier detection of the anomalies can prevent further damage. However, detecting zero-day attacks is extremely challenging because they have no known code and have unknown behavior. Furthermore, if data collected from the controller is transferred to a server through networks for analysis and detection of anomalous behavior, this creates a very large attack surface and also delays detection. In order to address this problem, we propose Reconstruction Error Distribution (RED) of Hardware Performance Counters (HPCs), and a data-driven defense system based on it. Specifically, we first train a temporal deep learning model, using only normal HPC readings from legitimate processes that run daily in these power-grid systems, to model the normal behavior of the power-grid controller. Then, we run this model using real-time data from commonly available HPCs. We use the proposed RED to enhance the temporal deep learning detection of anomalous behavior, by estimating distribution deviations from the normal behavior with an effective statistical test. Experimental results on a real power-grid controller show that we can detect anomalous behavior with high accuracy (>99.9%), nearly zero false positives and short (<360ms) latency.

cs.CR↗

arXiv2018

VerIDeep: Verifying Integrity of Deep Neural Networks through Sensitive-Sample Fingerprinting

Deep learning has become popular, and numerous cloud-based services are provided to help customers develop and deploy deep learning applications. Meanwhile, various attack techniques have also been discovered to stealthily compromise the model's integrity. When a cloud customer deploys a deep learning model in the cloud and serves it to end-users, it is important for him to be able to verify that the deployed model has not been tampered with, and the model's integrity is protected. We propose a new low-cost and self-served methodology for customers to verify that the model deployed in the cloud is intact, while having only black-box access (e.g., via APIs) to the deployed model. Customers can detect arbitrary changes to their deep learning models. Specifically, we define \texttt{Sensitive-Sample} fingerprints, which are a small set of transformed inputs that make the model outputs sensitive to the model's parameters. Even small weight changes can be clearly reflected in the model outputs, and observed by the customer. Our experiments on different types of model integrity attacks show that we can detect model integrity breaches with high accuracy ($>$99\%) and low overhead ($<$10 black-box model accesses).

cs.CR↗

arXiv2018

Privacy-preserving Machine Learning through Data Obfuscation

As machine learning becomes a practice and commodity, numerous cloud-based services and frameworks are provided to help customers develop and deploy machine learning applications. While it is prevalent to outsource model training and serving tasks in the cloud, it is important to protect the privacy of sensitive samples in the training dataset and prevent information leakage to untrusted third parties. Past work have shown that a malicious machine learning service provider or end user can easily extract critical information about the training samples, from the model parameters or even just model outputs. In this paper, we propose a novel and generic methodology to preserve the privacy of training data in machine learning applications. Specifically we introduce an obfuscate function and apply it to the training data before feeding them to the model training task. This function adds random noise to existing samples, or augments the dataset with new samples. By doing so sensitive information about the properties of individual samples, or statistical properties of a group of samples, is hidden. Meanwhile the model trained from the obfuscated dataset can still achieve high accuracy. With this approach, the customers can safely disclose the data or models to third-party providers or end users without the need to worry about data privacy. Our experiments show that this approach can effective defeat four existing types of machine learning privacy attacks at negligible accuracy cost.

cs.CR↗