- 期刊
Ransomware Detection and Prevention through Strategically Hidden Decoy File
摘要
Today's antivirus software has various methods to detect new and unknown malware, offering a very high detection rate and protection ability for virus-type malware. However, this detection rate is significantly reduced or even provides no protection capability for ransomware good at hiding, which is a highly severe threat to the computer files stored by users. Current antivirus software uses machine or deep learning mechanisms to effectively improve the detection rate of new and unknown malware. However, the news still reports ransomware incidents from enterprises or government units. This study implements a honeypot technique, a secret pot mechanism, where decoys are placed in the computer to detect ransomware. The detection program monitors the decoy files used at any time. Once the file is damaged by ransomware, the protection mechanism is triggered immediately, forcing the computer to shut down, preventing the ransomware from encrypting and destroying the files, which can protect the user's files and minimize losses.
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