update Mastersthesis.pdf and main.tex to enhance clarity and detail in APT detection analysis

Thesis_Docs/Nikkhah_Nasab-Aida-Mastersthesis.tex

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-In today’s interconnected digital landscape, Advanced Persistent Threats (APTs) exploit stealthy beaconing behavior to evade detection, posing significant risks to enterprise networks. This thesis investigates the performance of the BAYWATCH framework in identifying APTs by analyzing periodic communication patterns within extensive network log data.
+In today’s interconnected digital landscape, Advanced Persistent Threats (APTs) exploit stealthy beaconing behavior to evade detection, posing significant risks to enterprise networks. These sophisticated cyber threats can infiltrate systems, remain undetected for extended periods, and exfiltrate sensitive data, making them a formidable challenge for cybersecurity professionals. This thesis investigates the performance of the BAYWATCH framework in identifying APTs by analyzing periodic communication patterns within extensive network log data, aiming to enhance early detection and mitigation strategies.
 
-The study employs a signal analysis pipeline that combines Fast Fourier Transform (FFT) for frequency-domain detection with autocorrelation function (ACF) for time-domain verification. This dual approach ensures robust identification of periodicities, even under noisy conditions. To systematically evaluate resilience, synthetic datasets with programmable jitter (2–150 seconds) and beacon intervals (10–300 seconds) are generated, alongside validation using real-world enterprise network traces. Key innovations include permutation-based FFT thresholding, bandpass filtering, and frequency-lag correlation, collectively improving detection accuracy while minimizing false positives.
+This thesis offers a comprehensive examination of the BAYWATCH framework, an advanced system designed for monitoring, detecting, and analyzing data patterns, applied to both real-world and synthetic datasets. The research represents the theoretical underpinnings of BAYWATCH, outlining its algorithmic architecture, essential components, and the innovative methods it utilizes for real-time anomaly detection and data pattern recognition. Through a systematic evaluation, the study assesses the framework’s performance in controlled experimental settings and its effectiveness in complex, real-world scenarios. 
 
-This work contributes a scalable, efficient solution for early APT detection, validated in both controlled and operational environments. Future directions include real-time streaming analysis, machine learning integration for anomaly detection, and extension to IoT and cloud infrastructures. The thesis advances proactive cybersecurity strategies, offering a practical tool to safeguard large-scale networks against evolving threats.
+The study employs a comprehensive signal analysis pipeline that combines Fast Fourier Transform (FFT) for frequency-domain detection with autocorrelation function (ACF) for time-domain verification. This dual approach ensures robust identification of periodicities, even under noisy conditions. To systematically evaluate the resilience and effectiveness of the BAYWATCH framework, synthetic datasets with programmable jitter (ranging from 2 to 150 seconds) and beacon intervals (spanning 10 to 300 seconds) are generated. These synthetic datasets are complemented by validation using real-world enterprise network traces, providing a thorough assessment of the framework's capabilities in diverse operational environments.
+
+The insights gained fromthis research contribute to a deeper understanding of data monitoring systems and offer practical recommendations for future improvements, thereby advancing the application of intelligent data analysis techniques in both academic research and industry practice.
 
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