REALTIME MALWARE DETECTION USING IN-MEMORY ANALYSIS

Abstract The increasing sophistication of cyber threats has made traditional malware detection techniques, which rely heavily on signature-based methods and file-system scanning, less effective. Many modern malware variants operate entirely in system memory, leaving little or no trace on disk, thereby evading conventional security solutions. This study focuses on real-time malware detection using in-memory analysis as a proactive approach to identifying malicious activities within a computer system. The research explores techniques for monitoring and analyzing processes, memory patterns, and behavioral indicators directly from system memory to detect anomalies associated with malware execution. By leveraging memory forensics, behavioral analysis, and machine learning techniques, the proposed system aims to identify malicious code, suspicious process injections, and fileless malware in real time. The methodology involves capturing memory snapshots, extracting relevant features, and applying detection algorithms capable of distinguishing between legitimate and malicious activities. The results demonstrate that in-memory analysis significantly improves the detection of advanced persistent threats and memory-resident malware that bypass traditional antivirus mechanisms. This approach provides faster threat identification and response, thereby reducing the risk of system compromise and data loss. The study concludes that real-time in-memory malware detection is an effective and essential strategy for enhancing modern cybersecurity defenses, particularly against evolving and stealthy malware threats.