Unmasking the lurking: Malicious behavior detection for IoT malware with multi-label classification

• Main Authors: FENG, Ruitao, LI, Sen, CHEN, Sen, GE, Mengmeng, LI, Xuewei, LI, Xiaohong
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2024
• Subjects: IoT malware; Malicious behavior detection; Masked Graph Embedding; Multi-label classification; Information Security
• Online Access: https://ink.library.smu.edu.sg/sis_research/8974; https://ink.library.smu.edu.sg/context/sis_research/article/9977/viewcontent/3652032.3657577_pvoa_cc_by_nc_nd.pdf
• Institution: Singapore Management University

Current methods for classifying IoT malware predominantly utilize binary and family classifications. However, these outcomes lack the detailed granularity to describe malicious behavior comprehensively. This limitation poses challenges for security analysts, failing to support further analysis and t...