arXiv subjects

Adrian Shuai Li

Publications and source records attributed to Adrian Shuai Li.

10 recordsLinked to original sources

arXiv2026

PhantomCall: Evading ML Malware Detectors via Function Call Graph Perturbation

Prior adversarial attacks on Windows PE malware detectors target raw bytes, PE headers, or intra-function control-flow graphs, leaving the function call graph (FCG) unexplored as an attack surface. Yet the FCG structure is an important feature in graph-based malware detectors. We present Phan- tomCall, a black-box attack that perturbs the FCG of Windows PE malware by injecting fully executable dummy functions at targeted call sites, adding new nodes and edges to both the CFG and FCG while preserving program semantics. We pair this structural perturbation with classifier-guided search and tunable injection parameters, effective across three archi- tecturally distinct classifiers. Evaluated on a 2025-collected Windows malware corpus against MalConv (raw-byte CNN), MalGraph (graph-based GNN), and SAFE+GNN (pure FCG GNN trained from scratch on a 2024 corpus) at two FPR thresholds, the best PhantomCall variant achieves 85-100% attack success rate across all configurations, exceeding prior state-of-the-art by up to 14.78 percentage points on MalGraph and 95.5 percentage points on SAFE+GNN, and generating evasive variants up to 2.9x faster on average across all targets. For MalConv and MalGraph, the majority of evasions require only a single call site modification, and 86-97% of evaluated evasive variants preserve the original malicious behavior in sandbox-based semantic testing across all configurations.

cs.CR↗

arXiv2026

Can Drift-Adaptive Malware Detectors Be Made Robust? Attacks and Defenses Under White-Box and Black-Box Threats

Concept drift and adversarial evasion are two major challenges for deploying machine learning-based malware detectors. While both have been studied separately, their combination, the adversarial robustness of drift-adaptive detectors, remains unexplored. We address this problem with AdvDA, a recent malware detector that uses adversarial domain adaptation to align a labeled source domain with a target domain with limited labels. The distribution shift between domains poses a unique challenge: robustness learned on the source may not transfer to the target, and existing defenses assume a fixed distribution. To address this, we propose a universal robustification framework that fine-tunes a pretrained AdvDA model on adversarially transformed inputs, agnostic to the attack type and choice of transformations. We instantiate it with five defense variants spanning two threat models: white-box PGD attacks in the feature space and black-box MalGuise attacks that modify malware binaries via functionality-preserving control-flow mutations. Across nine defense configurations, five monthly adaptation windows on Windows malware, and three false-positive-rate operating points, we find the undefended AdvDA completely vulnerable to PGD (100% attack success) and moderately to MalGuise (13%). Our framework reduces these rates to as low as 3.2% and 5.1%, respectively, but the optimal strategy differs: source adversarial training is essential for PGD defenses yet counterproductive for MalGuise defenses, where target-only training suffices. Furthermore, robustness does not transfer across these two threat models. We provide deployment recommendations that balance robustness, detection accuracy, and computational cost.

cs.CR↗

arXiv2025

LFreeDA: Label-Free Drift Adaptation for Windows Malware Detection

Machine learning (ML)-based malware detectors degrade over time as concept drift introduces new and evolving families unseen during training. Retraining is limited by the cost and time of manual labeling or sandbox analysis. Existing approaches mitigate this via drift detection and selective labeling, but fully label-free adaptation remains largely unexplored. Recent self-training methods use a previously trained model to generate pseudo-labels for unlabeled data and then train a new model on these labels. The unlabeled data are used only for inference and do not participate in training the earlier model. We argue that these unlabeled samples still carry valuable information that can be leveraged when incorporated appropriately into training. This paper introduces LFreeDA, an end-to-end framework that adapts malware classifiers to drift without manual labeling or drift detection. LFreeDA first performs unsupervised domain adaptation on malware images, jointly training on labeled and unlabeled samples to infer pseudo-labels and prune noisy ones. It then adapts a classifier on CFG representations using the labeled and selected pseudo-labeled data, leveraging the scalability of images for pseudo-labeling and the richer semantics of CFGs for final adaptation. Evaluations on the real-world MB-24+ dataset show that LFreeDA improves accuracy by up to 12.6% and F1 by 11.1% over no-adaptation lower bounds, and is only 4% and 3.4% below fully supervised upper bounds in accuracy and F1, respectively. It also matches the performance of state-of-the-art methods provided with ground truth labels for 300 target samples. Additional results on two controlled-drift benchmarks further confirm that LFreeDA maintains malware detection performance as malware evolves without human labeling.

cs.CR↗

arXiv2025

LLMalMorph: On The Feasibility of Generating Variant Malware using Large-Language-Models

Large Language Models (LLMs) have transformed software development and automated code generation. Motivated by these advancements, this paper explores the feasibility of LLMs in modifying malware source code to generate variants. We introduce LLMalMorph, a semi-automated framework that leverages semantical and syntactical code comprehension by LLMs to generate new malware variants. LLMalMorph extracts function-level information from the malware source code and employs custom-engineered prompts coupled with strategically defined code transformations to guide the LLM in generating variants without resource-intensive fine-tuning. To evaluate LLMalMorph, we collected 10 diverse Windows malware samples of varying types, complexity and functionality and generated 618 variants. Our experiments demonstrate that LLMalMorph variants can effectively evade antivirus engines, achieving typical detection rate reductions of 10-15% across multiple complex samples. Furthermore, without explicitly targeting learning-based detectors, LLMalMorph attained attack success rates of up to 91% against a Machine Learning (ML) based malware detector. We also discuss the limitations of current LLM capabilities in generating malware variants from source code and assess where this emerging technology stands in the broader context of malware variant generation.

cs.CR↗

arXiv2025

Maximizing Information in Domain-Invariant Representation Improves Transfer Learning

We propose MaxDIRep, a domain adaptation method that improves the decomposition of data representations into domain-independent and domain-dependent components. Existing methods, such as Domain-Separation Networks (DSN), use a weak orthogonality constraint between these components, which can lead to label-relevant features being partially encoded in the domain-dependent representation (DDRep) rather than the domain-independent representation (DIRep). As a result, information crucial for target-domain classification may be missing from the DIRep. MaxDIRep addresses this issue by applying a Kullback-Leibler (KL) divergence constraint to minimize the information content of the DDRep, thereby encouraging the DIRep to retain features that are both domain-invariant and predictive of target labels. Through geometric analysis and an ablation study on synthetic datasets, we show why DSN's weaker constraint can lead to suboptimal adaptation. Experiments on standard image benchmarks and a network intrusion detection task demonstrate that MaxDIRep achieves strong performance, works with pretrained models, and generalizes to non-image classification tasks.

cs.CV↗

arXiv2024

Revisiting Concept Drift in Windows Malware Detection: Adaptation to Real Drifted Malware with Minimal Samples

In applying deep learning for malware classification, it is crucial to account for the prevalence of malware evolution, which can cause trained classifiers to fail on drifted malware. Existing solutions to address concept drift use active learning. They select new samples for analysts to label and then retrain the classifier with the new labels. Our key finding is that the current retraining techniques do not achieve optimal results. These techniques overlook that updating the model with scarce drifted samples requires learning features that remain consistent across pre-drift and post-drift data. The model should thus be able to disregard specific features that, while beneficial for the classification of pre-drift data, are absent in post-drift data, thereby preventing prediction degradation. In this paper, we propose a new technique for detecting and classifying drifted malware that learns drift-invariant features in malware control flow graphs by leveraging graph neural networks with adversarial domain adaptation. We compare it with existing model retraining methods in active learning-based malware detection systems and other domain adaptation techniques from the vision domain. Our approach significantly improves drifted malware detection on publicly available benchmarks and real-world malware databases reported daily by security companies in 2024. We also tested our approach in predicting multiple malware families drifted over time. A thorough evaluation shows that our approach outperforms the state-of-the-art approaches.

cs.CR↗

arXiv2024

Adversarial Domain Adaptation for Metal Cutting Sound Detection: Leveraging Abundant Lab Data for Scarce Industry Data

Cutting state monitoring in the milling process is crucial for improving manufacturing efficiency and tool life. Cutting sound detection using machine learning (ML) models, inspired by experienced machinists, can be employed as a cost-effective and non-intrusive monitoring method in a complex manufacturing environment. However, labeling industry data for training is costly and time-consuming. Moreover, industry data is often scarce. In this study, we propose a novel adversarial domain adaptation (DA) approach to leverage abundant lab data to learn from scarce industry data, both labeled, for training a cutting-sound detection model. Rather than adapting the features from separate domains directly, we project them first into two separate latent spaces that jointly work as the feature space for learning domain-independent representations. We also analyze two different mechanisms for adversarial learning where the discriminator works as an adversary and a critic in separate settings, enabling our model to learn expressive domain-invariant and domain-ingrained features, respectively. We collected cutting sound data from multiple sensors in different locations, prepared datasets from lab and industry domain, and evaluated our learning models on them. Experiments showed that our models outperformed the multi-layer perceptron based vanilla domain adaptation models in labeling tasks on the curated datasets, achieving near 92%, 82% and 85% accuracy respectively for three different sensors installed in industry settings.

cs.LG↗

arXiv2024

Transfer Learning for Security: Challenges and Future Directions

Many machine learning and data mining algorithms rely on the assumption that the training and testing data share the same feature space and distribution. However, this assumption may not always hold. For instance, there are situations where we need to classify data in one domain, but we only have sufficient training data available from a different domain. The latter data may follow a distinct distribution. In such cases, successfully transferring knowledge across domains can significantly improve learning performance and reduce the need for extensive data labeling efforts. Transfer learning (TL) has thus emerged as a promising framework to tackle this challenge, particularly in security-related tasks. This paper aims to review the current advancements in utilizing TL techniques for security. The paper includes a discussion of the existing research gaps in applying TL in the security domain, as well as exploring potential future research directions and issues that arise in the context of TL-assisted security solutions.

cs.CR↗

arXiv2023

Building Manufacturing Deep Learning Models with Minimal and Imbalanced Training Data Using Domain Adaptation and Data Augmentation

Deep learning (DL) techniques are highly effective for defect detection from images. Training DL classification models, however, requires vast amounts of labeled data which is often expensive to collect. In many cases, not only the available training data is limited but may also imbalanced. In this paper, we propose a novel domain adaptation (DA) approach to address the problem of labeled training data scarcity for a target learning task by transferring knowledge gained from an existing source dataset used for a similar learning task. Our approach works for scenarios where the source dataset and the dataset available for the target learning task have same or different feature spaces. We combine our DA approach with an autoencoder-based data augmentation approach to address the problem of imbalanced target datasets. We evaluate our combined approach using image data for wafer defect prediction. The experiments show its superior performance against other algorithms when the number of labeled samples in the target dataset is significantly small and the target dataset is imbalanced.

cs.CV↗

arXiv2022

A Capability-based Distributed Authorization System to Enforce Context-aware Permission Sequences

Controlled sharing is fundamental to distributed systems. We consider a capability-based distributed authorization system where a client receives capabilities (access tokens) from an authorization server to access the resources of resource servers. Capability-based authorization systems have been widely used on the Web, in mobile applications and other distributed systems. A common requirement of such systems is that the user uses tokens of multiple servers in a particular order. A related requirement is the token may be used if certain environmental conditions hold. We introduce a secure capability-based system that supports "permission sequence" and "context". This allows a finite sequence of permissions to be enforced, each with their own specific context. We prove the safety property of this system for these conditions and integrate the system into OAuth 2.0 with proof-of-possession tokens. We evaluate our implementation and compare it with plain OAuth with respect to the average time for obtaining an authorization token and acquiring access to the resource.

cs.CR↗