skip to main content


This content will become publicly available on July 10, 2024

Title: EMShepherd: Detecting Adversarial Samples via Side-channel Leakage
Deep Neural Networks (DNN) are vulnerable to adversarial perturbations — small changes crafted deliberately on the input to mislead the model for wrong predictions. Adversarial attacks have disastrous consequences for deep learning empowered critical applications. Existing defense and detection techniques both require extensive knowledge of the model, testing inputs and even execution details. They are not viable for general deep learning implementations where the model internal is unknown, a common ‘black-box’ scenario for model users. Inspired by the fact that electromagnetic (EM) emanations of a model inference are dependent on both operations and data and may contain footprints of different input classes, we propose a framework, EMShepherd, to capture EM traces of model execution, perform processing on traces and exploit them for adversarial detection. Only benign samples and their EM traces are used to train the adversarial detector: a set of EM classifiers and class-specific unsupervised anomaly detectors. When the victim model system is under attack by an adversarial example, the model execution will be different from executions for the known classes, and the EM trace will be different. We demonstrate that our air-gapped EMShepherd can effectively detect different adversarial attacks on a commonly used FPGA deep learning accelerator for both Fashion MNIST and CIFAR-10 datasets. It achieves a detection rate on most types of adversarial samples, which is comparable to the state-of-the-art ‘white-box’ software-based detectors.  more » « less
Award ID(s):
1916762 1929300 2212010
NSF-PAR ID:
10440768
Author(s) / Creator(s):
; ; ; ;
Date Published:
Journal Name:
ASIA CCS '23: Proceedings of the 2023 ACM Asia Conference on Computer and Communications Security
Page Range / eLocation ID:
300 to 313
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. We present PROVNINJA, a framework designed to generate adversarial attacks that aim to elude provenance-based Machine Learning (ML) security detectors. PROVNINJA is designed to identify and craft adversarial attack vectors that statistically mimic and impersonate system programs. Leveraging the benign execution profile of system processes commonly observed across a multitude of hosts and networks, our research proposes an efficient and effective method to probe evasive alternatives and devise stealthy attack vectors that are difficult to distinguish from benign system behaviors. PROVNINJA's suggestions for evasive attacks, originally derived in the feature space, are then translated into system actions, leading to the realization of actual evasive attack sequences in the problem space. When evaluated against State-of-The-Art (SOTA) detector models using two realistic Advanced Persistent Threat (APT) scenarios and a large collection of fileless malware samples, PROVNINJA could generate and realize evasive attack variants, reducing the detection rates by up to 59%. We also assessed PROVNINJA under varying assumptions on adversaries' knowledge and capabilities. While PROVNINJA primarily considers the black-box model, we also explored two contrasting threat models that consider blind and white-box attack scenarios. 
    more » « less
  2. We present PROVNINJA, a framework designed to generate adversarial attacks that aim to elude provenance-based Machine Learning (ML) security detectors. PROVNINJA is designed to identify and craft adversarial attack vectors that statistically mimic and impersonate system programs. Leveraging the benign execution profile of system processes commonly observed across a multitude of hosts and networks, our research proposes an efficient and effective method to probe evasive alternatives and devise stealthy attack vectors that are difficult to distinguish from benign system behaviors. PROVNINJA's suggestions for evasive attacks, originally derived in the feature space, are then translated into system actions, leading to the realization of actual evasive attack sequences in the problem space. When evaluated against State-of-The-Art (SOTA) detector models using two realistic Advanced Persistent Threat (APT) scenarios and a large collection of fileless malware samples, PROVNINJA could generate and realize evasive attack variants, reducing the detection rates by up to 59%. We also assessed PROVNINJA under varying assumptions on adversaries' knowledge and capabilities. While PROVNINJA primarily considers the black-box model, we also explored two contrasting threat models that consider blind and white-box attack scenarios. 
    more » « less
  3. We present PROVNINJA, a framework designed to generate adversarial attacks that aim to elude provenance-based Machine Learning (ML) security detectors. PROVNINJA is designed to identify and craft adversarial attack vectors that statistically mimic and impersonate system programs. Leveraging the benign execution profile of system processes commonly observed across a multitude of hosts and networks, our research proposes an efficient and effective method to probe evasive alternatives and devise stealthy attack vectors that are difficult to distinguish from benign system behaviors. PROVNINJA's suggestions for evasive attacks, originally derived in the feature space, are then translated into system actions, leading to the realization of actual evasive attack sequences in the problem space. When evaluated against State-of-The-Art (SOTA) detector models using two realistic Advanced Persistent Threat (APT) scenarios and a large collection of fileless malware samples, PROVNINJA could generate and realize evasive attack variants, reducing the detection rates by up to 59%. We also assessed PROVNINJA under varying assumptions on adversaries' knowledge and capabilities. While PROVNINJA primarily considers the black-box model, we also explored two contrasting threat models that consider blind and whitebox attack scenarios. 
    more » « less
  4. Machine learning-based security detection models have become prevalent in modern malware and intrusion detection systems. However, previous studies show that such models are susceptible to adversarial evasion attacks. In this type of attack, inputs (i.e., adversarial examples) are specially crafted by intelligent malicious adversaries, with the aim of being misclassified by existing state-of-the-art models (e.g., deep neural networks). Once the attackers can fool a classifier to think that a malicious input is actually benign, they can render a machine learning-based malware or intrusion detection system ineffective. Objective To help security practitioners and researchers build a more robust model against non-adaptive, white-box and non-targeted adversarial evasion attacks through the idea of ensemble model. Method We propose an approach called Omni, the main idea of which is to explore methods that create an ensemble of “unexpected models”; i.e., models whose control hyperparameters have a large distance to the hyperparameters of an adversary’s target model, with which we then make an optimized weighted ensemble prediction. Results In studies with five types of adversarial evasion attacks (FGSM, BIM, JSMA, DeepFool and Carlini-Wagner) on five security datasets (NSL-KDD, CIC-IDS-2017, CSE-CIC-IDS2018, CICAndMal2017 and the Contagio PDF dataset), we show Omni is a promising approach as a defense strategy against adversarial attacks when compared with other baseline treatments Conclusions When employing ensemble defense against adversarial evasion attacks, we suggest to create ensemble with unexpected models that are distant from the attacker’s expected model (i.e., target model) through methods such as hyperparameter optimization. 
    more » « less
  5. Deep Neural Networks (DNNs) have shown phenomenal success in a wide range of real-world applications. However, a concerning weakness of DNNs is that they are vulnerable to adversarial attacks. Although there exist methods to detect adversarial attacks, they often suffer constraints on specific attack types and provide limited information to downstream systems. We specifically note that existing adversarial detectors are often binary classifiers, which differentiate clean or adversarial examples. However, detection of adversarial examples is much more complicated than such a scenario. Our key insight is that the confidence probability of detecting an input sample as an adversarial example will be more useful for the system to properly take action to resist potential attacks. In this work, we propose an innovative method for fast confidence detection of adversarial attacks based on integrity of sensor pattern noise embedded in input examples. Experimental results show that our proposed method is capable of providing a confidence distribution model of most of popular adversarial attacks. Furthermore, our presented method can provide early attack warning with even the attack types based on different properties of the confidence distribution models. Since fast confidence detection is a computationally heavy task, we propose an FPGA-Based hardware architecture based on a series of optimization techniques, such as incremental multi-level quantization and etc. We realize our proposed method on an FPGA platform and achieve a high efficiency of 29.740 IPS/W with a power consumption of only 0.7626W. 
    more » « less