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