Level Up with RealAEs: Leveraging Domain Constraints in Feature Space to
Strengthen Robustness of Android Malware Detection
- URL: http://arxiv.org/abs/2205.15128v3
- Date: Sun, 11 Jun 2023 06:37:55 GMT
- Title: Level Up with RealAEs: Leveraging Domain Constraints in Feature Space to
Strengthen Robustness of Android Malware Detection
- Authors: Hamid Bostani, Zhengyu Zhao, Zhuoran Liu, Veelasha Moonsamy
- Abstract summary: A vulnerability to adversarial examples remains one major obstacle for Machine Learning (ML)-based Android malware detection.
We propose to generate RealAEs in the feature space, leading to a simpler and more efficient solution.
Our approach is driven by a novel interpretation of Android domain constraints in the feature space.
- Score: 6.721598112028829
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: The vulnerability to adversarial examples remains one major obstacle for
Machine Learning (ML)-based Android malware detection. Realistic attacks in the
Android malware domain create Realizable Adversarial Examples (RealAEs), i.e.,
AEs that satisfy the domain constraints of Android malware. Recent studies have
shown that using such RealAEs in Adversarial Training (AT) is more effective in
defending against realistic attacks than using unrealizable AEs (unRealAEs).
This is because RealAEs allow defenders to explore certain pockets in the
feature space that are vulnerable to realistic attacks. However, existing
defenses commonly generate RealAEs in the problem space, which is known to be
time-consuming and impractical for AT. In this paper, we propose to generate
RealAEs in the feature space, leading to a simpler and more efficient solution.
Our approach is driven by a novel interpretation of Android domain constraints
in the feature space. More concretely, our defense first learns feature-space
domain constraints by extracting meaningful feature dependencies from data and
then applies them to generating feature-space RealAEs during AT. Extensive
experiments on DREBIN, a well-known Android malware detector, demonstrate that
our new defense outperforms not only unRealAE-based AT but also the
state-of-the-art defense that relies on non-uniform perturbations. We further
validate the ability of our learned feature-space domain constraints in
representing Android malware properties by showing that our feature-space
domain constraints can help distinguish RealAEs from unRealAEs.
Related papers
- Attention Tracker: Detecting Prompt Injection Attacks in LLMs [62.247841717696765]
Large Language Models (LLMs) have revolutionized various domains but remain vulnerable to prompt injection attacks.
We introduce the concept of the distraction effect, where specific attention heads shift focus from the original instruction to the injected instruction.
We propose Attention Tracker, a training-free detection method that tracks attention patterns on instruction to detect prompt injection attacks.
arXiv Detail & Related papers (2024-11-01T04:05:59Z) - Effective and Efficient Adversarial Detection for Vision-Language Models via A Single Vector [97.92369017531038]
We build a new laRge-scale Adervsarial images dataset with Diverse hArmful Responses (RADAR)
We then develop a novel iN-time Embedding-based AdveRSarial Image DEtection (NEARSIDE) method, which exploits a single vector that distilled from the hidden states of Visual Language Models (VLMs) to achieve the detection of adversarial images against benign ones in the input.
arXiv Detail & Related papers (2024-10-30T10:33:10Z) - Improving Adversarial Robustness in Android Malware Detection by Reducing the Impact of Spurious Correlations [3.7937308360299116]
Machine learning (ML) has demonstrated significant advancements in Android malware detection (AMD)
However, the resilience of ML against realistic evasion attacks remains a major obstacle for AMD.
In this study, we propose a domain adaptation technique to improve the generalizability of AMD by aligning the distribution of malware samples and AEs.
arXiv Detail & Related papers (2024-08-27T17:01:12Z) - Decompose to Adapt: Cross-domain Object Detection via Feature
Disentanglement [79.2994130944482]
We design a Domain Disentanglement Faster-RCNN (DDF) to eliminate the source-specific information in the features for detection task learning.
Our DDF method facilitates the feature disentanglement at the global and local stages, with a Global Triplet Disentanglement (GTD) module and an Instance Similarity Disentanglement (ISD) module.
By outperforming state-of-the-art methods on four benchmark UDA object detection tasks, our DDF method is demonstrated to be effective with wide applicability.
arXiv Detail & Related papers (2022-01-06T05:43:01Z) - Discriminator-Free Generative Adversarial Attack [87.71852388383242]
Agenerative-based adversarial attacks can get rid of this limitation.
ASymmetric Saliency-based Auto-Encoder (SSAE) generates the perturbations.
The adversarial examples generated by SSAE not only make thewidely-used models collapse, but also achieves good visual quality.
arXiv Detail & Related papers (2021-07-20T01:55:21Z) - MixDefense: A Defense-in-Depth Framework for Adversarial Example
Detection Based on Statistical and Semantic Analysis [14.313178290347293]
We propose a multilayer defense-in-depth framework for AE detection, namely MixDefense.
We leverage the noise' features extracted from the inputs to discover the statistical difference between natural images and tampered ones for AE detection.
We show that the proposed MixDefense solution outperforms the existing AE detection techniques by a considerable margin.
arXiv Detail & Related papers (2021-04-20T15:57:07Z) - Universal Adversarial Perturbations for Malware [15.748648955898528]
Universal Adversarial Perturbations (UAPs) identify noisy patterns that generalize across the input space.
We explore the challenges and strengths of UAPs in the context of malware classification.
We propose adversarial training-based mitigations using knowledge derived from the problem-space transformations.
arXiv Detail & Related papers (2021-02-12T20:06:10Z) - Attribute-Guided Adversarial Training for Robustness to Natural
Perturbations [64.35805267250682]
We propose an adversarial training approach which learns to generate new samples so as to maximize exposure of the classifier to the attributes-space.
Our approach enables deep neural networks to be robust against a wide range of naturally occurring perturbations.
arXiv Detail & Related papers (2020-12-03T10:17:30Z) - SLAP: Improving Physical Adversarial Examples with Short-Lived
Adversarial Perturbations [19.14079118174123]
Short-Lived Adrial Perturbations (SLAP) is a novel technique that allows adversaries to realize physically robust real-world AE by using a light projector.
SLAP allows the adversary greater control over the attack compared to adversarial patches.
We study the feasibility of SLAP in the self-driving scenario, targeting both object detector and traffic sign recognition tasks.
arXiv Detail & Related papers (2020-07-08T14:11:21Z) - Adversarial vs behavioural-based defensive AI with joint, continual and
active learning: automated evaluation of robustness to deception, poisoning
and concept drift [62.997667081978825]
Recent advancements in Artificial Intelligence (AI) have brought new capabilities to behavioural analysis (UEBA) for cyber-security.
In this paper, we present a solution to effectively mitigate this attack by improving the detection process and efficiently leveraging human expertise.
arXiv Detail & Related papers (2020-01-13T13:54:36Z) - Intriguing Properties of Adversarial ML Attacks in the Problem Space [Extended Version] [18.3238686304247]
We propose a general formalization for adversarial ML evasion attacks in the problem-space.
We propose a novel problem-space attack on Android malware that overcomes past limitations in terms of semantics and artifacts.
Our results demonstrate that "adversarial-malware as a service" is a realistic threat.
arXiv Detail & Related papers (2019-11-05T23:39:55Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.