Projects
Explore my innovative cybersecurity research projects focusing on advanced neural networks, explainability, and cutting-edge threat detection. My work bridges the gap between machine learning and network security to address emerging challenges in the digital landscape.
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A transformer-based NLP approach that translates network traffic data (PCAP) into natural language for enhanced security analysis. This project enables security analysts to interpret network patterns more intuitively through advanced NMT techniques.
A revolutionary approach to Multi-Recurrent Neural Networks (MRNs) focusing on autonomous memory composition through self-learning link ratios, enhanced pruning techniques, and adaptive activation functions. This research improves forecasting of software vulnerabilities while developing deeper MRN architectures with reduced computational overhead.
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Making temporal Komogorov-Arnold neural networks transparent through advanced explainability techniques. This project provides insights into how these complex models process time-series data for security applications.

Revolutionary baseline analysis system for detecting malicious behavior patterns in system calls. This framework establishes new benchmarks for malware detection through behavioral analysis techniques.