arXiv subjects

Andrew Wheeler

Publications and source records attributed to Andrew Wheeler.

3 recordsLinked to original sources

arXiv2026

Malformer: A Multi-Modal Malware Detector Using Transformers

Traditional malware detection systems that rely on a single representation of malware often fail to identify novel threats. These representations of malware binaries, also known as modalities, do not provide the models with sufficient information to discriminate among all samples. Additionally, individual representations introduce new failure modes, with some modality extraction being dependent upon the success of disassembling. Past works have integrated either additional modalities or more discriminative representations for classification. In this work, we present Malformer, a quadrimodal malware detection model that incorporates text, image, graph, and audio representations of Windows executables. We demonstrate that multimodal transformer fusion can enhance the performance of Windows malware detectors over that of unimodal and bimodal detectors. Malformer employs a combination of two RoBERTa encoders paired with a modified Vision Transformer for image data, WavLM for audio data, and an adaptive loss-weighting scheme to fuse modality-specific representations. Evaluated on a dataset of 201,549 binary samples, Malformer achieved 98.3% accuracy and an F1 score of 0.9833, outperforming both unimodal baselines and bimodal detectors by 4.6-17.6 percentage points. Malformer demonstrates that multimodal fusion provides a promising foundation for countering the growing scale of malware threats, equipping defenders with generalized and resilient detection capabilities.

cs.CR↗

arXiv2026

Explainability-Guided Adversarial Attacks on Transformer-Based Malware Detectors Using Control Flow Graphs

Transformer-based malware detection systems operating on graph modalities such as control flow graphs (CFGs) achieve strong performance by modeling structural relationships in program behavior. However, their robustness to adversarial evasion attacks remains underexplored. This paper examines the vulnerability of a RoBERTa-based malware detector that linearizes CFGs into sequences of function calls, a design choice that enables transformer modeling but may introduce token-level sensitivities and ordering artifacts exploitable by adversaries. By evaluating evasion strategies within this graph-to-sequence framework, we provide insight into the practical robustness of transformer-based malware detectors beyond aggregate detection accuracy. This paper proposes a white-box adversarial evasion attack that leverages explainability mechanisms to identify and perturb most influential graph components. Using token- and word-level attributions derived from integrated gradients, the attack iteratively replaces positively attributed function calls with synthetic external imports, producing adversarial CFG representations without altering overall program structure. Experimental evaluation on small- and large-scale Windows Portable Executable (PE) datasets demonstrates that the proposed method can reliably induce misclassification, even against models trained to high accuracy. Our results highlight that explainability tools, while valuable for interpretability, can also expose critical attack surfaces in transformer-based malware detectors.

cs.CR↗

arXiv2024

Reducing the Barriers to Entry for Foundation Model Training

The world has recently witnessed an unprecedented acceleration in demands for Machine Learning and Artificial Intelligence applications. This spike in demand has imposed tremendous strain on the underlying technology stack in supply chain, GPU-accelerated hardware, software, datacenter power density, and energy consumption. If left on the current technological trajectory, future demands show insurmountable spending trends, further limiting market players, stifling innovation, and widening the technology gap. To address these challenges, we propose a fundamental change in the AI training infrastructure throughout the technology ecosystem. The changes require advancements in supercomputing and novel AI training approaches, from high-end software to low-level hardware, microprocessor, and chip design, while advancing the energy efficiency required by a sustainable infrastructure. This paper presents the analytical framework that quantitatively highlights the challenges and points to the opportunities to reduce the barriers to entry for training large language models.

cs.ET↗