LooCipher Ransomware Detection Using Lightweight Packet Characteristics | Zendy

Te-Min Liu | Zendy; DaYu Kao | Zendy; Yun-Ya Chen | Zendy

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LooCipher Ransomware Detection Using Lightweight Packet Characteristics

Author(s) -

Te-Min Liu,

DaYu Kao,

Yun-Ya Chen

Publication year - 2020

Publication title -

procedia computer science

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.334

H-Index - 76

ISSN - 1877-0509

DOI - 10.1016/j.procs.2020.09.192

Subject(s) - ransomware , computer science , offensive , network packet , payload (computing) , merge (version control) , botnet , deep packet inspection , computer security , network security , cybercrime , malware , computer network , data mining , the internet , operating system , operations research , information retrieval , engineering

Ransomware activities have been rising steadily. The network traffic characteristics in a network packet analysis are available immediately to explore anomalies and find any offensive behaviors. This paper applies a lightweight ICEAP (Identify-Collect-Examine-Analyze-Present) approach for effectively identifying LooCipher ransomware activities instead of establishing complex systems or creating various programs. This proposed approach tracks online behaviors and understands the source/destination entities. With this innovative detection method, analysts can merge the eigenvalues into security mechanisms, uncover network threats by analyzing the full payload, and detect infected ransomware in a minimum effort.

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