Papers Malware Analysis
“Malware Analysis” 태그가 달린 논문 89편 · 필터 해제
Semantic Preprocessing for LLM-based Malware Analysis
In a context of malware analysis, numerous approaches rely on Artificial Intelligence to handle a large volume of data. However, these techniques focus on data view (images, sequences) and not on an expert's view. Notici…
Language ModelingLanguage ModellingLarge Language ModelMalware Analysis+1EMBER2024 -- A Benchmark Dataset for Holistic Evaluation of Malware Classifiers
A lack of accessible data has historically restricted malware analysis research, and practitioners have relied heavily on datasets provided by industry sources to advance. Existing public datasets are limited by narrow s…
Malware AnalysisMalware ClassificationMalware DetectionSystem Calls for Malware Detection and Classification: Methodologies and Applications
As malware continues to become more complex and harder to detect, Malware Analysis needs to continue to evolve to stay one step ahead. One promising key area approach focuses on using system calls and API Calls, the core…
Anomaly DetectionMalware AnalysisMalware DetectionAdapting Novelty towards Generating Antigens for Antivirus systems
It is well known that anti-malware scanners depend on malware signatures to identify malware. However, even minor modifications to malware code structure results in a change in the malware signature thus enabling the var…
Evolutionary AlgorithmsMalware AnalysisMalware DetectionDecompileBench: A Comprehensive Benchmark for Evaluating Decompilers in Real-World Scenarios
Decompilers are fundamental tools for critical security tasks, from vulnerability discovery to malware analysis, yet their evaluation remains fragmented. Existing approaches primarily focus on syntactic correctness throu…
Malware AnalysisDual Explanations via Subgraph Matching for Malware Detection
Interpretable malware detection is crucial for understanding harmful behaviors and building trust in automated security systems. Traditional explainable methods for Graph Neural Networks (GNNs) often highlight important …
Malware AnalysisMalware DetectionMalware analysis assisted by AI with R2AI
This research studies the quality, speed and cost of malware analysis assisted by artificial intelligence. It focuses on Linux and IoT malware of 2024-2025, and uses r2ai, the AI extension of Radare2's disassembler. Not …
Malware AnalysisLarge Language Model (LLM) for Software Security: Code Analysis, Malware Analysis, Reverse Engineering
Large Language Models (LLMs) have recently emerged as powerful tools in cybersecurity, offering advanced capabilities in malware detection, generation, and real-time monitoring. Numerous studies have explored their appli…
Language ModelingLanguage ModellingLarge Language ModelMalware Analysis+1Identifying Obfuscated Code through Graph-Based Semantic Analysis of Binary Code
Protecting sensitive program content is a critical issue in various situations, ranging from legitimate use cases to unethical contexts. Obfuscation is one of the most used techniques to ensure such protection. Consequen…
Malware AnalysisObfuscation DetectionOn Benchmarking Code LLMs for Android Malware Analysis
Large Language Models (LLMs) have demonstrated strong capabilities in various code intelligence tasks. However, their effectiveness for Android malware analysis remains underexplored. Decompiled Android malware code pres…
BenchmarkingMalware AnalysisCyberLLMInstruct: A New Dataset for Analysing Safety of Fine-Tuned LLMs Using Cyber Security Data
The integration of large language models (LLMs) into cyber security applications presents significant opportunities, such as enhancing threat analysis and malware detection, but can also introduce critical risks and safe…
Adversarial AttackMalware AnalysisMalware DetectionLAMD: Context-driven Android Malware Detection and Classification with LLMs
The rapid growth of mobile applications has escalated Android malware threats. Although there are numerous detection methods, they often struggle with evolving attacks, dataset biases, and limited explainability. Large L…
Android Malware DetectionMalware AnalysisMalware DetectionRecent Advances in Malware Detection: Graph Learning and Explainability
The rapid evolution of malware has necessitated the development of sophisticated detection methods that go beyond traditional signature-based approaches. Graph learning techniques have emerged as powerful tools for model…
Feature EngineeringGraph EmbeddingGraph LearningMalware Analysis+2Exploring Large Language Models for Semantic Analysis and Categorization of Android Malware
Malware analysis is a complex process of examining and evaluating malicious software's functionality, origin, and potential impact. This arduous process typically involves dissecting the software to understand its compon…
Malware AnalysisPrompt EngineeringOn the Lack of Robustness of Binary Function Similarity Systems
Binary function similarity, which often relies on learning-based algorithms to identify what functions in a pool are most similar to a given query function, is a sought-after topic in different communities, including mac…
Malware AnalysisVulnerability DetectionUnmasking the Shadows: Pinpoint the Implementations of Anti-Dynamic Analysis Techniques in Malware Using LLM
Sandboxes and other dynamic analysis processes are prevalent in malware detection systems nowadays to enhance the capability of detecting 0-day malware. Therefore, techniques of anti-dynamic analysis (TADA) are prevalent…
Language ModelingLanguage ModellingLarge Language ModelMalware Analysis+1Is Function Similarity Over-Engineered? Building a Benchmark
Binary analysis is a core component of many critical security tasks, including reverse engineering, malware analysis, and vulnerability detection. Manual analysis is often time-consuming, but identifying commonly-used or…
Malware AnalysisVulnerability DetectionDeep Learning Based XIoT Malware Analysis: A Comprehensive Survey, Taxonomy, and Research Challenges
The Internet of Things (IoT) is one of the fastest-growing computing industries. By the end of 2027, more than 29 billion devices are expected to be connected. These smart devices can communicate with each other with and…
Deep LearningMalware AnalysisMalware DetectionDomain-Specific Retrieval-Augmented Generation Using Vector Stores, Knowledge Graphs, and Tensor Factorization
Large Language Models (LLMs) are pre-trained on large-scale corpora and excel in numerous general natural language processing (NLP) tasks, such as question answering (QA). Despite their advanced language capabilities, wh…
Anomaly DetectionAttributeKnowledge GraphsMalware Analysis+5Magika: AI-Powered Content-Type Detection
The task of content-type detection -- which entails identifying the data encoded in an arbitrary byte sequence -- is critical for operating systems, development, reverse engineering environments, and a variety of securit…
CPUMalware Analysis