Semantic-Aware Contrastive Fine-Tuning: Boosting Multimodal Malware Classification with Discriminative Embeddings

• URL: http://arxiv.org/abs/2504.21028v1
• Date: Fri, 25 Apr 2025 02:41:45 GMT
• Title: Semantic-Aware Contrastive Fine-Tuning: Boosting Multimodal Malware Classification with Discriminative Embeddings
• Authors: Ivan Montoya Sanchez, Shaswata Mitra, Aritran Piplai, Sudip Mittal,
• Abstract summary: Large Language Models (LLMs) offer potential for generating malware descriptions to aid family classification.<n>We propose a contrastive fine-tuning (CFT) method that refines LLM embeddings via targeted selection of hard negative samples.<n>Our approach combines high-similarity negatives to enhance discriminative power and mid-tier negatives to increase embedding diversity.
• Score: 2.1874189959020427
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The rapid evolution of malware variants requires robust classification methods to enhance cybersecurity. While Large Language Models (LLMs) offer potential for generating malware descriptions to aid family classification, their utility is limited by semantic embedding overlaps and misalignment with binary behavioral features. We propose a contrastive fine-tuning (CFT) method that refines LLM embeddings via targeted selection of hard negative samples based on cosine similarity, enabling LLMs to distinguish between closely related malware families. Our approach combines high-similarity negatives to enhance discriminative power and mid-tier negatives to increase embedding diversity, optimizing both precision and generalization. Evaluated on the CIC-AndMal-2020 and BODMAS datasets, our refined embeddings are integrated into a multimodal classifier within a Model-Agnostic Meta-Learning (MAML) framework on a few-shot setting. Experiments demonstrate significant improvements: our method achieves 63.15% classification accuracy with as few as 20 samples on CIC-AndMal-2020, outperforming baselines by 11--21 percentage points and surpassing prior negative sampling strategies. Ablation studies confirm the superiority of similarity-based selection over random sampling, with gains of 10-23%. Additionally, fine-tuned LLMs generate attribute-aware descriptions that generalize to unseen variants, bridging textual and binary feature gaps. This work advances malware classification by enabling nuanced semantic distinctions and provides a scalable framework for adapting LLMs to cybersecurity challenges.

Related papers

• SHA256 at SemEval-2025 Task 4: Selective Amnesia -- Constrained Unlearning for Large Language Models via Knowledge Isolation [12.838593066237452]
  Large language models (LLMs) memorize frequently sensitive information during training, posing risks when deploying publicly accessible models.<n>This paper presents our solution to SemEval-2025 Task 4 on targeted unlearning, which combines causal mediation analysis with layer-specific optimization.
  arXiv  Detail & Related papers  (2025-04-17T15:05:40Z)
• Estimating Commonsense Plausibility through Semantic Shifts [66.06254418551737]
  We propose ComPaSS, a novel discriminative framework that quantifies commonsense plausibility by measuring semantic shifts.<n> Evaluations on two types of fine-grained commonsense plausibility estimation tasks show that ComPaSS consistently outperforms baselines.
  arXiv  Detail & Related papers  (2025-02-19T06:31:06Z)
• Multi-Attribute Steering of Language Models via Targeted Intervention [56.93583799109029]
  Inference-time intervention (ITI) has emerged as a promising method for steering large language model (LLM) behavior in a particular direction.<n>We introduce Multi-Attribute Targeted Steering (MAT-Steer), a novel steering framework designed for selective token-level intervention across multiple attributes.
  arXiv  Detail & Related papers  (2025-02-18T02:27:23Z)
• Targeting Alignment: Extracting Safety Classifiers of Aligned LLMs [4.492376241514766]
  Alignment in large language models (LLMs) is used to enforce guidelines such as safety.<n>Yet, alignment fails in the face of jailbreak attacks that modify inputs to induce unsafe outputs.<n>We present and evaluate a method to assess the robustness of LLM alignment.
  arXiv  Detail & Related papers  (2025-01-27T22:13:05Z)
• Adaptive Pruning for Large Language Models with Structural Importance Awareness [66.2690963378878]
  Large language models (LLMs) have significantly improved language understanding and generation capabilities. LLMs are difficult to deploy on resource-constrained edge devices due to their high computational and storage resource demands. We propose structurally-aware adaptive pruning (SAAP) to significantly reduce the computational and memory costs while maintaining model performance.
  arXiv  Detail & Related papers  (2024-12-19T18:08:04Z)
• Constructing Domain-Specific Evaluation Sets for LLM-as-a-judge [15.980606104936365]
  Large Language Models (LLMs) have revolutionized the landscape of machine learning, yet current benchmarks often fall short in capturing the diverse behavior of these models in real-world applications. Existing frameworks like Alpaca-Eval 2.0 LC citedubois2024lengthcontrolledalpacaevalsimpleway and Arena-Hard v0.1 citeli2024crowdsourced are limited by their focus on general-purpose queries and lack of diversity across domains such as law, medicine, and multilingual contexts. We introduce a novel data pipeline that curates, domain-specific evaluation sets tailored for LLM-as
  arXiv  Detail & Related papers  (2024-08-16T15:41:43Z)
• Task-oriented Embedding Counts: Heuristic Clustering-driven Feature Fine-tuning for Whole Slide Image Classification [1.292108130501585]
  We propose a clustering-driven feature fine-tuning method (HC-FT) to enhance the performance of multiple instance learning. The proposed method is evaluated on both CAMELYON16 and BRACS datasets, achieving an AUC of 97.13% and 85.85%, respectively.
  arXiv  Detail & Related papers  (2024-06-02T08:53:45Z)
• Preference Fine-Tuning of LLMs Should Leverage Suboptimal, On-Policy Data [102.16105233826917]
  Learning from preference labels plays a crucial role in fine-tuning large language models. There are several distinct approaches for preference fine-tuning, including supervised learning, on-policy reinforcement learning (RL), and contrastive learning.
  arXiv  Detail & Related papers  (2024-04-22T17:20:18Z)
• Rethinking Clustering-Based Pseudo-Labeling for Unsupervised Meta-Learning [146.11600461034746]
  Method for unsupervised meta-learning, CACTUs, is a clustering-based approach with pseudo-labeling. This approach is model-agnostic and can be combined with supervised algorithms to learn from unlabeled data. We prove that the core reason for this is lack of a clustering-friendly property in the embedding space.
  arXiv  Detail & Related papers  (2022-09-27T19:04:36Z)
• MCDAL: Maximum Classifier Discrepancy for Active Learning [74.73133545019877]
  Recent state-of-the-art active learning methods have mostly leveraged Generative Adversarial Networks (GAN) for sample acquisition. We propose in this paper a novel active learning framework that we call Maximum Discrepancy for Active Learning (MCDAL) In particular, we utilize two auxiliary classification layers that learn tighter decision boundaries by maximizing the discrepancies among them.
  arXiv  Detail & Related papers  (2021-07-23T06:57:08Z)

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.