MaLAware: Automating the Comprehension of Malicious Software Behaviours using Large Language Models (LLMs)

• URL: http://arxiv.org/abs/2504.01145v1
• Date: Tue, 01 Apr 2025 19:27:17 GMT
• Title: MaLAware: Automating the Comprehension of Malicious Software Behaviours using Large Language Models (LLMs)
• Authors: Bikash Saha, Nanda Rani, Sandeep Kumar Shukla,
• Abstract summary: MaLAware is a tool that translates raw malware data into human-readable descriptions.<n>MaLAware processes Cuckoo Sandbox-generated reports to correlate malignant activities and generate concise summaries.<n>The evaluation uses the human-written malware behaviour description dataset as ground truth.
• Score: 3.410195565199523
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Current malware (malicious software) analysis tools focus on detection and family classification but fail to provide clear and actionable narrative insights into the malignant activity of the malware. Therefore, there is a need for a tool that translates raw malware data into human-readable descriptions. Developing such a tool accelerates incident response, reduces malware analysts' cognitive load, and enables individuals having limited technical expertise to understand malicious software behaviour. With this objective, we present MaLAware, which automatically summarizes the full spectrum of malicious activity of malware executables. MaLAware processes Cuckoo Sandbox-generated reports using large language models (LLMs) to correlate malignant activities and generate concise summaries explaining malware behaviour. We evaluate the tool's performance on five open-source LLMs. The evaluation uses the human-written malware behaviour description dataset as ground truth. The model's performance is measured using 11 extensive performance metrics, which boosts the confidence of MaLAware's effectiveness. The current version of the tool, i.e., MaLAware, supports Qwen2.5-7B, Llama2-7B, Llama3.1-8B, Mistral-7B, and Falcon-7B, along with the quantization feature for resource-constrained environments. MaLAware lays a foundation for future research in malware behavior explanation, and its extensive evaluation demonstrates LLMs' ability to narrate malware behavior in an actionable and comprehensive manner.

Related papers

• On Benchmarking Code LLMs for Android Malware Analysis [13.932151152280689]
  Large Language Models (LLMs) have demonstrated strong capabilities in various code intelligence tasks, but their effectiveness for Android malware analysis remains underexplored.<n>This paper presents Cama, a benchmarking framework designed to systematically evaluate the effectiveness of Code LLMs in Android malware analysis tasks.
  arXiv  Detail & Related papers  (2025-04-01T12:05:49Z)
• Unveiling Malware Patterns: A Self-analysis Perspective [15.517313565392852]
  VisUnpack is a static analysis-based data visualization framework for bolstering attack prevention and aiding recovery post-attack.<n>Our method includes unpacking packed malware programs, calculating local similarity descriptors based on basic blocks, enhancing correlations between descriptors, and refining them by minimizing noises.<n>Our comprehensive evaluation of VisUnpack based on a freshly gathered dataset with over 27,106 samples confirms its capability in accurately classifying malware programs with a precision of 99.7%.
  arXiv  Detail & Related papers  (2025-01-10T16:04:13Z)
• Exploring Large Language Models for Semantic Analysis and Categorization of Android Malware [0.0]
  msp is designed to augment malware analysis for Android through a hierarchical-tiered summarization chain and strategic prompt engineering.<n>msp can achieve up to 77% classification accuracy while providing highly robust summaries at functional, class, and package levels.
  arXiv  Detail & Related papers  (2025-01-08T21:22:45Z)
• MASKDROID: Robust Android Malware Detection with Masked Graph Representations [56.09270390096083]
  We propose MASKDROID, a powerful detector with a strong discriminative ability to identify malware. We introduce a masking mechanism into the Graph Neural Network based framework, forcing MASKDROID to recover the whole input graph. This strategy enables the model to understand the malicious semantics and learn more stable representations, enhancing its robustness against adversarial attacks.
  arXiv  Detail & Related papers  (2024-09-29T07:22:47Z)
• Learning to Ask: When LLM Agents Meet Unclear Instruction [55.65312637965779]
  Large language models (LLMs) can leverage external tools for addressing a range of tasks unattainable through language skills alone.<n>We evaluate the performance of LLMs tool-use under imperfect instructions, analyze the error patterns, and build a challenging tool-use benchmark called Noisy ToolBench.<n>We propose a novel framework, Ask-when-Needed (AwN), which prompts LLMs to ask questions to users whenever they encounter obstacles due to unclear instructions.
  arXiv  Detail & Related papers  (2024-08-31T23:06:12Z)
• Catch'em all: Classification of Rare, Prominent, and Novel Malware Families [3.147175286021779]
  Malware remains one of the most dangerous and costly cyber threats. As of last year, researchers reported 1.3 billion known malware specimens. These challenges include detection of novel malware and the ability to perform malware classification in the face of class imbalance.
  arXiv  Detail & Related papers  (2024-03-04T23:46:19Z)
• MalDICT: Benchmark Datasets on Malware Behaviors, Platforms, Exploitation, and Packers [44.700094741798445]
  Existing research on malware classification focuses almost exclusively on two tasks: distinguishing between malicious and benign files and classifying malware by family. We have identified four tasks which are under-represented in prior work: classification by behaviors that malware exhibit, platforms that malware run on, vulnerabilities that malware exploit, and packers that malware are packed with. We are releasing benchmark datasets for each of these four classification tasks, tagged using ClarAVy and comprising nearly 5.5 million malicious files in total.
  arXiv  Detail & Related papers  (2023-10-18T04:36:26Z)
• DRSM: De-Randomized Smoothing on Malware Classifier Providing Certified Robustness [58.23214712926585]
  We develop a certified defense, DRSM (De-Randomized Smoothed MalConv), by redesigning the de-randomized smoothing technique for the domain of malware detection. Specifically, we propose a window ablation scheme to provably limit the impact of adversarial bytes while maximally preserving local structures of the executables. We are the first to offer certified robustness in the realm of static detection of malware executables.
  arXiv  Detail & Related papers  (2023-03-20T17:25:22Z)
• Mate! Are You Really Aware? An Explainability-Guided Testing Framework for Robustness of Malware Detectors [49.34155921877441]
  We propose an explainability-guided and model-agnostic testing framework for robustness of malware detectors. We then use this framework to test several state-of-the-art malware detectors' abilities to detect manipulated malware. Our findings shed light on the limitations of current malware detectors, as well as how they can be improved.
  arXiv  Detail & Related papers  (2021-11-19T08:02:38Z)
• A Novel Malware Detection Mechanism based on Features Extracted from Converted Malware Binary Images [0.22843885788439805]
  We use malware binary images and then extract different features from the same and then employ different ML-classifiers on the dataset thus obtained. We show that this technique is successful in differentiating classes of malware based on the features extracted.
  arXiv  Detail & Related papers  (2021-04-14T06:55:52Z)
• Being Single Has Benefits. Instance Poisoning to Deceive Malware Classifiers [47.828297621738265]
  We show how an attacker can launch a sophisticated and efficient poisoning attack targeting the dataset used to train a malware classifier. As opposed to other poisoning attacks in the malware detection domain, our attack does not focus on malware families but rather on specific malware instances that contain an implanted trigger. We propose a comprehensive detection approach that could serve as a future sophisticated defense against this newly discovered severe threat.
  arXiv  Detail & Related papers  (2020-10-30T15:27:44Z)

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.