Reverse Engineering & Malware Analysis Mastery

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Reverse Engineering & Malware Analysis Mastery
Published 9/2026
Created by Bayt Al Hikmah
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All Levels | Genre: eLearning | Language: English | Duration: 114 Lectures ( 25h 29m ) | Size: 1.4 GB

What you'll learn
Read x86-64 assembly straight from compiler output - registers, stack frames, calling conventions, control flow, and optimization artifacts
Dissect ELF and PE binary formats by hand: headers, sections, program headers, relocations, the PLT/GOT, and stripped-symbol recovery.
Navigate Ghidra and radare2 like a professional analyst - decompiler cross-checking, function renaming taxonomy, and CLI-driven triage at scale.
Validate binary behavior safely using GDB, strace, ltrace, filesystem diffing, and isolated network sinkholes - with zero unsafe live-malware execution.
Recognize defensive malware behavior patterns - persistence, injection concepts, packing/entropy signals, and anti-analysis checks.
Engineer tested YARA rules and Sigma detections, complete with false-positive test matrices and ATT&CK technique mapping.
Automate triage pipelines in Python - batch Ghidra headless analysis, JSON Schema-validated evidence, SQLite indexing, and CI-gated detection-as-code.
Operate a production-style sandbox architecture: disposable workers, resource limits, observability (metrics, structured logs, trace IDs), and Kubernetes/GitOps
Govern evidence like a regulated analyst - GDPR-aware classification, DORA resilience packs, NIS2 incident-reporting inputs, and guardrailed local AI assistance
Deliver a full capstone: a sovereign, offline-capable analysis platform with signed evidence bundles, STIX-style intelligence, and executive report
Requirements
Knowledge: Basic comfort with a terminal (running commands, navigating folders). No prior assembly, reverse engineering, or malware analysis experience required - Module 1-2 build the mechanical foundation from zero. Any prior programming exposure (Python especially) helps in Modules 9-12 but isn't mandatory; scripts are explained line by line. No security certifications or prior SOC/IR experience needed. Software (all free/open-source): A computer running Linux (native or via VM) - the course's official environment. Windows/macOS users can follow along using the provided VM/container images. Git, Docker or Podman, Python 3.10+. Ghidra, radare2/Cutter, GDB, binutils (readelf, objdump, nm), YARA, and the coursectl lab harness (provided open-source scaffold). Optional: Kubernetes (Kind) and kubectl for the Module 10 orchestration labs - a lightweight local cluster is all that's needed. Hardware: 40GB+ free disk space, virtualization enabled in firmware (VT-x/AMD-V) for isolated sandbox labs. No physical malware samples, no real customer data, and no internet-exposed execution required - every lab uses instructor-provided benign fixtures and a network-isolated runtime by design.
Description
This course contains the use of artificial intelligence.
We only charge a fee solely for the time invested in building this comprehensive curriculum.
The "I Ran Strings and Called It a Day" Problem
There's a specific kind of false confidence in security work: someone opens a binary in a disassembler, scrolls around for ten minutes, spots a suspicious-looking string, and writes "malicious" in a ticket. It feels like analysis. It isn't. Real incident response, threat research, and detection engineering require something much more rigorous - the ability to read assembly-level behavior, validate it dynamically without guessing, and turn that evidence into a detection rule that will actually hold up against a false-positive test matrix and an auditor's questions.
That gap between "I poked around in a tool" and "I can prove, with reproducible evidence, exactly what this binary does and why my detection fires on it" is what this course is built to close. This is not a course about writing malware. It is a defensive, evidence-first curriculum: every lab uses benign fixtures, controlled samples, and network-isolated sandboxes, and every technique is taught in service of analysis, detection, and governance - never offensive tooling.
The Solution: 100 Labs, One Reproducible Lab Contract
Every single lab in this course follows the same Zero-Failure harness: a pre-flight check, an automatic backup, a scripted rollback, a verification step, and an evidence export - all run through a provided open-source scaffold called coursectl. That means you can never truly break your environment, and every finding you produce is automatically hashed, timestamped, and stored as defensible evidence from Lab 1 onward. By Lab 5 you'll have written your first mini reverse-engineering report. By Lab 100, that same discipline scales into a full production-style analysis platform.
What's Inside
Modules 1-2 build your safe workstation and the mechanical foundation - hex, endianness, registers, the stack, the instruction pointer, flags, and memory addressing - so assembly stops being mysterious symbols and starts being a physical model you can picture.
Module 3 is where you learn to actuallyread x86-64 assembly: calling conventions, prologues/epilogues, loops, switch/jump tables, structs, recursion, and - critically - how optimization changes what you see, so you're never fooled by a compiler's shorthand.
Module 4 takes you inside ELF and PE binary formats by hand: headers, program headers, dynamic linking, the PLT/GOT, and symbol stripping, so you can orient yourself inany unknown binary in minutes.
Module 5 puts professional open-source tooling in your hands - Ghidra project workflows, decompiler cross-validation, radare2 CLI triage, algorithm recovery, and compiler fingerprinting - building the exact static-analysis checklist a working analyst uses on an unknown sample.
Module 6 moves to safe dynamic analysis: GDB breakpoints and watchpoints, strace/ltrace, filesystem diffing, and isolated network sinkhole captures - every technique validated against a snapshot-based VM recovery drill so nothing you do is irreversible.
Modules 7-8 cover defensive malware behavior recognition (persistence, injection concepts, packing/entropy, anti-analysis checks - all via safe, annotated fixtures) and full detection engineering: YARA rules with false-positive test matrices, Sigma detections, IOC extraction, STIX 2.1 bundles, and ATT&CK-mapped, version-controlled detection-as-code.
Modules 9-10 industrialize everything: Python triage automation, batch Ghidra headless analysis, JSON Schema-validated evidence, SQLite indexing, CI gates for rules and reports, queue-based pipelines, and a full sandbox architecture with disposable workers, resource limits, Prometheus-style metrics, and Kubernetes/GitOps deployment blueprints.
Module 11 is the part almost no RE course covers honestly: GDPR-aware evidence classification, DORA resilience packs, NIS2 incident-reporting inputs, a third-party tooling risk register, and - critically - governed local AI-assisted analysis with human-validation guardrails against hallucination and prompt injection from untrusted binary strings.
The Climax: Lab 100 - The Sovereign Malware Analysis Platform
Lab 100 isn't a final quiz - it's a full capstone build, scored against a 100-point rubric spanning safe lab operation, static and dynamic analysis quality, detection engineering, automation reproducibility, evidence governance, ATT&CK mapping, operational reliability, and communication. You will process a controlled case package end-to-end: static analysis with Ghidra and radare2, dynamic validation in an isolated runtime, tested YARA and Sigma detections, a privacy-reviewed STIX-style intelligence bundle, and three final reports written for three different audiences - executive, SOC, and technical. When you finish, you won't have watched someone else analyze a sample. You'll have built, tested, and defended your own reproducible, offline-capable analysis platform.
Why Enroll Now
The market gap this course closes is real and growing: most RE courses teach isolated tool walkthroughs, while senior malware analyst, threat researcher, and security research engineer roles increasingly expect thefull pipeline - assembly literacy through governed, automated detection engineering. Every tool here is free and open-source (Ghidra, radare2, YARA, Sigma, GDB), so what you build is yours to keep, extend, and put directly into your portfolio - no vendor lock-in, no license expiring the day you change jobs.
Who this course is for
The Aspiring Malware Analyst / SOC-to-Threat-Research Transition You've triaged alerts or read vendor reports, but the moment someone hands you a raw binary and asks "what does this actually do," you're stuck clicking around a disassembler with no plan. This course builds the exact hard-skill cluster job postings for malware analyst and threat researcher roles ask for - assembly literacy, Ghidra/radare2 fluency, and detection engineering - in a structured, evidence-driven order.
The Software/Backend Engineer Curious About the Machine Beneath the Code You write software fluently but assembly, binary formats, and "what actually happens when this compiles" feel like a black box you've always wanted to open. This course treats reverse engineering as applied systems knowledge - you'll go from hex and registers to reading real compiled control flow, using skills that make you a sharper engineer even outside security.
The Senior Security Engineer Building a Defensible, Auditable Analysis Practice You already know some RE tools, but your current workflow is ad hoc - no reproducibility, no evidence trail, no way to prove a detection actually works before it ships. This course is built around a Zero-Failure lab contract (pre-flight, backup, rollback, verified evidence export) and closes with a full sovereign, offline-capable analysis platform - governance, observability, and compliance mapping included - so your findings hold up to an auditor, not just a demo.
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