28 lines
2.1 KiB
Text
28 lines
2.1 KiB
Text
Main Points for the Introduction
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Background and Motivation:
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The increasing sophistication of malware and attack techniques, such as rootkits and masquerading processes, poses significant challenges to traditional detection mechanisms.
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Hidden or masqueraded processes can evade standard tools by leveraging techniques like kernel-level manipulation, API hooking, or process injection.
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Limitations of Current Approaches:
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Signature-based and behavior-based detection methods are often circumvented by polymorphic or fileless malware.
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Existing tools may struggle to differentiate between legitimate and malicious processes, especially when attackers mimic trusted processes.
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Emerging Focus on Side-Channel Analysis:
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Side-channel data, such as power consumption, has emerged as a promising non-invasive means of system monitoring.
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Power consumption patterns inherently reflect the activity of running processes, including their computational and memory usage characteristics.
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Research Gap:
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While side-channel data has been explored for other applications, its potential for detecting hidden or masqueraded processes remains underexplored.
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A reliable method to associate anomalous power consumption patterns with malicious process activity could significantly enhance detection capabilities.
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Proposed Contribution:
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Introduction of a novel method leveraging power consumption patterns to detect hidden or masqueraded processes.
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Description of how the method identifies deviations from expected power usage profiles using advanced statistical or machine learning techniques.
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Significance of the Work:
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The proposed method offers a complementary tool to traditional detection systems, enhancing system security.
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Its ability to utilize hardware-level data reduces reliance on potentially compromised software-based mechanisms.
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Structure of the Article:
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Overview of the proposed method, followed by an in-depth explanation of the methodology, experimental setup, results, and discussion on implications and limitations.
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