In this paper, we have explored the efficacy of deep learning techniques in the early detection of ransomware through the analysis of API call sequences. We ...
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Aug 15, 2024 · This paper systematically compares various subsets of API call features, different ML techniques, and context-window sizes to identify the ...
This paper systematically compares various subsets of API call features, different ML techniques, and context-window sizes to identify the optimal ransomware ...
This paper proposes Zero-Ran Sniff (ZRS), an early zero-day ransomware detection method based on zero-shot learning, which can detect zero-day ransomware ...
This paper proposes an early detection model (CRED) that can determine the pre-encryption boundaries and collect the data related to this phase more accurately.
Identifies three detection methods: API/System call monitoring, I/O tracking, and file system observation—all utilizing ML. Highlights the disparity between ...
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Sep 19, 2022 · This paper presents a Cost-Sensitive Pareto Ensemble strategy, CSPE-R to detect novel Ransomware attacks.
Jul 7, 2024 · This survey investigates the contributions of research into the detection of ransomware malware using machine learning and deep learning ...
The proposed EJMI method has demonstrated a 4% improvement in detection accuracy compared to previous methods, highlighting its effectiveness in identifying and ...
In this paper, we present a feature selection-based framework with adopting different machine learning algorithms including neural network-based architectures ...
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