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Keywords = I/O Request Packet (IRP)

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14 pages, 1691 KiB  
Article
Temporal Data Correlation Providing Enhanced Dynamic Crypto-Ransomware Pre-Encryption Boundary Delineation
by Abdullah Alqahtani and Frederick T. Sheldon
Sensors 2023, 23(9), 4355; https://doi.org/10.3390/s23094355 - 28 Apr 2023
Cited by 8 | Viewed by 2159
Abstract
Ransomware is a type of malware that employs encryption to target user files, rendering them inaccessible without a decryption key. To combat ransomware, researchers have developed early detection models that seek to identify threats before encryption takes place, often by monitoring the initial [...] Read more.
Ransomware is a type of malware that employs encryption to target user files, rendering them inaccessible without a decryption key. To combat ransomware, researchers have developed early detection models that seek to identify threats before encryption takes place, often by monitoring the initial calls to cryptographic APIs. However, because encryption is a standard computational activity involved in processes, such as packing, unpacking, and polymorphism, the presence of cryptographic APIs does not necessarily indicate an imminent ransomware attack. Hence, relying solely on cryptographic APIs is insufficient for accurately determining a ransomware pre-encryption boundary. To this end, this paper is devoted to addressing this issue by proposing a Temporal Data Correlation method that associates cryptographic APIs with the I/O Request Packets (IRPs) based on the timestamp for pre-encryption boundary delineation. The process extracts the various features from the pre-encryption dataset for use in early detection model training. Several machine and deep learning classifiers are used to evaluate the accuracy of the proposed solution. Preliminary results show that this newly proposed approach can achieve higher detection accuracy compared to those reported elsewhere. Full article
(This article belongs to the Special Issue Privacy and Security in Wireless Networks and the Emerging Internet of Things)
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