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We have developed a machine learning approach that characterizes user tasks through their resource utilization.
We develop decoy process mechanisms that camouflage performance counter data to prevent malware from learning the resource utilization of a user task. We tested ...
We develop decoy process mechanisms that camouflage performance counter data to prevent malware from learning the resource utilization of a user task. We tested ...
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Mar 4, 2024 · Machine learning is crucial in enhancing cybersecurity by enabling organizations to detect and respond to threats more effectively and efficiently.
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A deep learning model can learn complex feature hierarchies and incorporate diverse steps of malware detection pipeline into one solid model that can be trained ...
Nov 3, 2022 · To identify malicious threats or malware, we used a number of machine learning techniques. A high detection ratio indicated that the algorithm ...
Apr 29, 2024 · In this chapter, readers will explore how machine learning has been applied to build malware detection systems designed for the Windows operating system.
This paper proposes a novel Malware Attack Detection in android using deep belief NETwork (MAD-NET) which accurately detects and mitigates the malware attacks.
Deep learning technology ushered in a new era for malware identification, leveraging models such as recurrent neural networks and convolutional neural networks ...
Feb 26, 2024 · Machine learning can help detect malware by analyzing the features and behaviors of different types of files, such as executables, scripts, documents, or ...