🎓 B.Sc. Software Engineering student at Afeka College (Academic Reserve – Atuda)

💡 I build clean, practical, and maintainable software for real-world problems

🔍 Continuously learning new technologies and hands-on software development

Languages

Frameworks & Tools

• Low-Level, Binary Exploitation & Reverse Engineering: Linux ELF Binaries, Memory Safety & Corruption (Buffer Overflows), Dynamic Debugging (GDB + GEF), Disassembly (Ghidra), Exploit Automation (pwntools, C, Python).

• Networking & Security Observability: OSI & TCP/IP (L2–L7), Deep Packet Inspection (DPI), Raw Sockets, Traffic Analysis & Intrusion Detection (DoS/Port Scans), Infrastructure-as-Code (Grafana, Loki, Promtail).

• Cryptography & Vault Architecture: Symmetric Encryption (AES-128-CBC / Fernet, HMAC-SHA256), Key Derivation (PBKDF2), Dynamic Threat Mitigation, Password Hashing & Salting.

• Backend Systems & Concurrency: Multi-threaded Architecture (Producer-Consumer Pipelines), Thread Synchronization (threading.Lock), C-FFI / Native Extensions (ctypes), Event-Driven Architecture (watchdog).

• DevOps & Infrastructure: CI/CD (GitHub Actions), Containerization (Docker), Infrastructure Automation & Scripting (Bash), PyPI Packaging, Automated Testing (pytest).

High-Performance Network Intrusion Detection System with Dual-Layer DPI & Observability Stack

• Motivation: Building an enterprise-grade NIDS to perform real-time L3-L7 network monitoring, multi-pattern payload scanning, active defense, and dynamic threat mitigation.
• The Challenge: Overcoming Python's FFI overhead and GIL bottlenecks during deep packet inspection (DPI) under heavy traffic while ensuring Memory Safety and preventing buffer over-reads on raw binary traffic — including diagnosing and fixing a false-positive signature match against encrypted traffic during live testing.
• Key Learning: Implemented a dual-layer live DPI pipeline — an Aho-Corasick automaton for keyword signatures alongside a Native C DPI Engine (ctypes) using bounds-checked memory operations (memchr/memcmp) for injection-pattern signatures, measured at an 8x throughput speedup in isolated benchmarking (~2.5M pkts/sec) with zero crash risk on invalid network frames. Mastered Producer-Consumer queues, thread safety (threading.Lock), background GC routines, and Dashboards-as-Code.
• Tech Stack: Python, Native C Extension (GCC/ctypes), Scapy, Multithreading, Docker Compose, Grafana, Loki, Promtail, JSON Structured Logging, IaC.

Personal security research repository containing binary exploitation exploits, writeups, and reverse engineering analysis

• Motivation: Deepening practical skills in low-level vulnerabilities, memory safety, binary analysis, and reverse engineering techniques.
• The Challenge: Analyzing unknown compiled ELF binaries, identifying memory corruption flaws (e.g., Buffer Overflows, format string bugs), and crafting reliable exploits to redirect execution flow.
• Key Learning: Advanced dynamic analysis with GDB + GEF, static analysis using Ghidra, shellcode execution, and automating exploit scripts via pwntools.
• Tech Stack & Tools: C, Python, Pwntools, GDB (GEF), Ghidra, Linux (WSL2/Ubuntu), Bash.

Secure, modular CLI/TUI password manager published on PyPI with automated CI/CD

• Motivation: Moving away from third-party managers to build a production-ready personal vault, implement secure cryptography from scratch, and master end-to-end software packaging.
• The Challenge: Correctly implementing Fernet symmetric encryption (AES-128-CBC & HMAC-SHA256), PBKDF2 key derivation, an interactive arrow-key TUI (questionary/rich), and a fully automated PyPI release pipeline via GitHub Actions.
• Key Learning: Masterclass in Cybersecurity fundamentals, software modularity, robust exception flows, and end-to-end DevOps automation from local testing to global package distribution.
• Tech Stack: Python, Cryptography, Pyperclip, Rich, Questionary, Pytest, GitHub Actions (CI/CD), PyPI.

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