Deep learning LSTM based ransomware detection

Autor:	Sumith Maniath, A. U. Prem Sankar, Srinath Jan, V G Sujadevi, Prabaharan Poornachandran, Aravind Ashok
Rok vydání:	2017
Předmět:	Application programming interface business.industry Computer science Deep learning Feature extraction computer.file_format Machine learning computer.software_genre Ransomware Malware Artificial intelligence Executable business Hidden Markov model computer Word (computer architecture)
Zdroj:	2017 Recent Developments in Control, Automation & Power Engineering (RDCAPE).
Popis:	There is a growing interest in academia and industry to employ dynamic analysis for automating malwares analysis. In dynamic analysis, Application Programming Interface (API) calls made by the executable is a promising source to identify the behavior of an application. The list of API calls made by a process can be considered as a word sequence. This work aims to detect ransomware behavior by employing Long-Short Term Memory (LSTM) networks for binary sequence classification of API calls. We present an automated approach to extract API calls from the log of modified sandbox environment and detect ransomware behavior. The proposed approach is expected to improve the automated analysis of large volume of malwares samples.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::34eaf20247bf2fcc03ae1e056baabe82 https://doi.org/10.1109/rdcape.2017.8358312 Zobrazit plný text záznamu