System Calls for Malware Detection and Classification: Methodologies and Applications
원문 초록
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 communication between user applications and the operating system and their kernels. These calls provide valuable insight into how software or programs behaves, making them an useful tool for spotting suspicious or harmful activity of programs and software. This chapter takes a deep down look at how system calls are used in malware detection and classification, covering techniques like static and dynamic analysis, as well as sandboxing. By combining these methods with advanced techniques like machine learning, statistical analysis, and anomaly detection, researchers can analyze system call patterns to tell the difference between normal and malicious behavior. The chapter also explores how these techniques are applied across different systems, including Windows, Linux, and Android, while also looking at the ways sophisticated malware tries to evade detection.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
EMBER2024 -- A Benchmark Dataset for Holistic Evaluation of Malware Classifiers
Cyber Security Data Science: Machine Learning Methods and their Performance on Imbalanced Datasets
Adapting Novelty towards Generating Antigens for Antivirus systems
Dual Explanations via Subgraph Matching for Malware Detection
Towards High-Resolution 3D Anomaly Detection: A Scalable Dataset and Real-Time Framework for Subtle Industrial Defects
Evaluating Language Models For Threat Detection in IoT Security Logs
토론
이 논문과 연결된 포럼 글이 아직 없습니다.