CyberCode Academy

CyberCode Academy
  • 0.0 (0)
  • COURSES
  • UPDATED DAILY

Welcome to CyberCode Academy — your audio classroom for Programming and Cybersecurity. 🎧 Each course is divided into a series of short, focused episodes that take you from beginner to advanced level — one lesson at a time. From Python and web development to ethical hacking and digital defense, our content transforms complex concepts into simple, engaging audio learning. Study anywhere, anytime — and level up your skills with CyberCode Academy. 🚀 Learn. Code. Secure. You can listen and download our episodes for free on more than 10 different platforms: https://linktr.ee/cybercode_academy

  1. Course 24 - Machine Learning for Red Team Hackers | Episode 5: The Complete Guide to Deepfake Creation

    23 HR AGO

    Course 24 - Machine Learning for Red Team Hackers | Episode 5: The Complete Guide to Deepfake Creation

    In this lesson, you’ll learn about:What deepfakes are and how neural networks enable face, voice, and style transferThe standard face swap pipeline: extraction → preprocessing → training → predictionWhy conducting a local dry run helps validate datasets before scaling to expensive GPU environmentsThe importance of face alignment, sorting, and dataset cleaning to reduce false positivesHow lightweight models are used for parameter tuning before full-scale trainingThe role of GPU acceleration in deep learning workflowsWhy cloud platforms like Google Cloud are used for large-scale model trainingThe importance of compatible drivers (e.g., NVIDIA drivers) in deep learning setupsHow frameworks such as TensorFlow power neural network trainingHow frame rendering and encoding tools like FFmpeg compile processed frames into videoHow training previews help visualize model convergence from noise to structured outputsEthical & Professional ConsiderationsAlways obtain explicit consent from anyone whose likeness is usedUnderstand laws regarding impersonation, fraud, and non-consensual synthetic mediaConsider watermarking or disclosure when creating synthetic contentBe aware that deepfake techniques are actively studied in media forensics and detection research You can listen and download our episodes for free on more than 10 different platforms: https://linktr.ee/cybercode_academy

    14 min
  2. Course 24 - Machine Learning for Red Team Hackers | Episode 4: Mastering White-Box and Black-Box Attacks

    1 DAY AGO

    Course 24 - Machine Learning for Red Team Hackers | Episode 4: Mastering White-Box and Black-Box Attacks

    In this lesson, you’ll learn about:The difference between white-box and black-box threat models in machine learning securityWhy gradient-based models are vulnerable to carefully crafted input perturbationsThe core intuition behind the Fast Gradient Sign Method (FGSM) as a sensitivity-analysis techniqueHow adversarial perturbations exploit a model’s local linearity and gradient structureThe purpose of adversarial ML frameworks like Foolbox in controlled research environmentsHow pretrained architectures such as ResNet are evaluated for robustnessWhy datasets like MNIST are commonly used for benchmarking security experimentsThe security risks of exposing prediction APIs in black-box servicesWhy production ML systems must assume adversarial interactionDefensive Takeaways for ML Engineers Rather than attacking models in the wild, security teams use adversarial research to:Measure model robustness before deploymentImplement adversarial training to improve resilienceApply input preprocessing defenses and anomaly detectionLimit prediction confidence exposure in public APIsMonitor query patterns to detect probing behaviorUse ensemble methods and hybrid ML + rule-based detection systemsWhy This Matters: Adversarial machine learning highlights that high accuracy ≠ high security. Models that perform well on clean data may fail under minimal, human-imperceptible perturbations. Robustness must be treated as a first-class engineering requirement, especially in:Autonomous systemsBiometric authenticationMalware detectionFinancial fraud systems You can listen and download our episodes for free on more than 10 different platforms: https://linktr.ee/cybercode_academy

    16 min
  3. Course 24 - Machine Learning for Red Team Hackers | Episode 3: Evading Machine Learning Malware Classifiers

    2 DAYS AGO

    Course 24 - Machine Learning for Red Team Hackers | Episode 3: Evading Machine Learning Malware Classifiers

    In this lesson, you’ll learn about:What adversarial machine learning is and why ML-based malware classifiers are vulnerable to manipulationThe difference between feature-engineered models like Ember and end-to-end neural approaches like MalConvWhy handling real malware (e.g., Jigsaw ransomware) requires a properly isolated virtual machine labHow libraries such as LIEF and pefile are used to safely parse and analyze Portable Executable (PE) structuresThe concept of model decision boundaries and detection thresholdsWhy “benign signal injection” works conceptually (model blind spots and over-reliance on superficial features)The security risk of overlay data and section manipulation in static analysis pipelinesThe difference between gradient boosting models and deep neural networks in robustness and feature sensitivityHow adversarial examples reveal weaknesses in ML-based security productsDefensive strategies for improving robustness against evasion attemptsDefensive Takeaways for Security Teams Instead of bypassing detection, professionals use these insights to:Strengthen feature engineering to reduce manipulation opportunitiesNormalize or strip non-executable overlay data before classificationIncorporate adversarial training to improve model resilienceCombine static and dynamic analysis to detect functionality, not just file structureMonitor for abnormal file padding and suspicious section anomaliesImplement ensemble detection strategies rather than relying on a single model You can listen and download our episodes for free on more than 10 different platforms: https://linktr.ee/cybercode_academy

    16 min
  4. Course 24 - Machine Learning for Red Team Hackers | Episode 2: Building and Implementing Evolutionary Testing Tools

    3 DAYS AGO

    Course 24 - Machine Learning for Red Team Hackers | Episode 2: Building and Implementing Evolutionary Testing Tools

    In this lesson, you’ll learn about:What fuzzing is and why it’s a powerful technique for discovering software vulnerabilitiesThe difference between basic randomized fuzzing and more advanced, coverage-guided approachesHow code coverage helps measure which parts of a program are exercised during testingWhy naive random input generation is inefficient for complex formats like PDFsThe concept of mutation-based fuzzing, including byte-level modifications such as insertion, deletion, swapping, and randomizationHow evolutionary fuzzing applies principles from genetic algorithms to improve input effectivenessThe role of a fitness function in selecting high-value test casesHow recombination and mutation evolve a population of inputs to reach deeper code pathsHow professional tools like American Fuzzy Lop instrument compiled programs to detect unique crashes and segmentation faultsWhy fuzzing is critical for secure software development and proactive vulnerability discovery You can listen and download our episodes for free on more than 10 different platforms: https://linktr.ee/cybercode_academy

    17 min
  5. Course 24 - Machine Learning for Red Team Hackers | Episode 1: Building an Automated CAPTCHA-Breaking Bot

    4 DAYS AGO

    Course 24 - Machine Learning for Red Team Hackers | Episode 1: Building an Automated CAPTCHA-Breaking Bot

    In this lesson, you’ll learn about:How CAPTCHA systems (like Really Simple CAPTCHA for WordPress) are designed to prevent automated abuseThe role of reconnaissance in identifying security mechanisms on web applications (for defensive testing with permission)How OpenCV is used in computer vision for:Grayscale conversionImage thresholdingNoise reduction and morphological operations (e.g., dilation)Contour detection and character segmentationThe fundamentals of building a Convolutional Neural Network (CNN) using frameworks like KerasWhy preprocessing (normalization, resizing, padding) is critical for image-based ML accuracyHow browser automation tools such as Selenium function in legitimate contexts (e.g., QA testing, regression testing, accessibility testing)Why CAPTCHA systems can be vulnerable to ML advances—and how modern defenses evolve in responseDefensive & Ethical Takeaway Instead of bypassing CAPTCHAs, security professionals use this knowledge to:Strengthen bot mitigation strategiesImplement more resilient human verification systemsDetect automated abuse patternsTransition toward modern solutions like behavioral analysis and risk-based authentication You can listen and download our episodes for free on more than 10 different platforms: https://linktr.ee/cybercode_academy

    17 min
  6. Course 23 - WiFi Hacking with Raspberry Pi | Episode 7: Building a Digital Ghost on Raspberry Pi

    5 DAYS AGO

    Course 23 - WiFi Hacking with Raspberry Pi | Episode 7: Building a Digital Ghost on Raspberry Pi

    In this lesson, you’ll learn about: What digital identity is and how IP and MAC addresses are used to track usersWhy masking an IP address is essential for protecting location and online activityHow the Tor network provides anonymity by routing traffic through multiple global nodesThe role of ProxyChains in forcing applications to operate through anonymizing networksWhat a MAC address represents and how it can be used for device-level identificationWhy MAC address randomization helps prevent physical and network-based trackingThe limitations and risks of anonymity tools when used incorrectlyHow combining multiple techniques creates a layered anonymity strategyEthical and defensive use cases for anonymity in privacy protection and security research You can listen and download our episodes for free on more than 10 different platforms: https://linktr.ee/cybercode_academy

    12 min
  7. Course 23 - WiFi Hacking with Raspberry Pi | Episode 6: Fix These 5 Router Security Loopholes

    6 DAYS AGO

    Course 23 - WiFi Hacking with Raspberry Pi | Episode 6: Fix These 5 Router Security Loopholes

    In this lesson, you’ll learn about: Why default router settings are a major security risk and commonly targeted by attackersHow changing the administrative IP address reduces exposure to automated attacksThe importance of replacing default usernames and passwords with strong, unique credentialsWhy disabling WPS is critical to preventing brute-force and PIN-based attacksHow enabling modern encryption standards strengthens wireless network protectionThe role of built-in router firewalls in safeguarding connected devicesHow securing local and remote management settings closes common attack vectorsPractical steps to harden a home network against unauthorized access and exploitation You can listen and download our episodes for free on more than 10 different platforms: https://linktr.ee/cybercode_academy

    18 min
  8. Course 23 - WiFi Hacking with Raspberry Pi | Episode 5: Cracking Wi-Fi Passwords With Raspberry Pi

    13 FEB

    Course 23 - WiFi Hacking with Raspberry Pi | Episode 5: Cracking Wi-Fi Passwords With Raspberry Pi

    In this lesson, you’ll learn about: Using a Raspberry Pi as a mobile platform for wireless penetration testingLeveraging Wifite, an automated tool for auditing Wi‑Fi networksExploiting WPS vulnerabilities through the Pixie Dust attack to quickly recover router credentialsPerforming dictionary attacks on WPA/WPA2 networks by capturing handshake packets and testing against common password listsUnderstanding the security implications of handshake interception and why strong, unique passwords are criticalRecognizing the importance of disabling outdated protocols like WPS to protect networks from automated attacksApplying these methods in a controlled, ethical penetration testing environment to evaluate wireless security defenses You can listen and download our episodes for free on more than 10 different platforms: https://linktr.ee/cybercode_academy

    13 min
See All (154)

About

Welcome to CyberCode Academy — your audio classroom for Programming and Cybersecurity. 🎧 Each course is divided into a series of short, focused episodes that take you from beginner to advanced level — one lesson at a time. From Python and web development to ethical hacking and digital defense, our content transforms complex concepts into simple, engaging audio learning. Study anywhere, anytime — and level up your skills with CyberCode Academy. 🚀 Learn. Code. Secure. You can listen and download our episodes for free on more than 10 different platforms: https://linktr.ee/cybercode_academy

Information

  • Creator
    CyberCode Academy
  • Years Active
    2025 - 2026
  • Episodes
    154
  • Rating
    Clean
  • Copyright
    © Copyright CyberCode Academy
  • Show Website
    CyberCode Academy