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.
Automated malware detection using machine learning and deep ...
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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 ...