Online Course – Google Certified Professional Internship in AI for Cybersecurity, Johns Hopkins University

Acquire advanced skills in artificial intelligence techniques to identify and prevent cyber threats, ensuring solid protection against ever-evolving digital risks.

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קורס אונליין - התמחות מקצועית מוסמכת ב-AI for Cybersecurity של Google

Professional Certificate

Beginners

No prior knowledge required

Time to complete the course

7-day free trial

No unnecessary risks

Skills you will acquire in the course

  • Mastering artificial intelligence applications in cybersecurity
  • Advanced techniques for detecting cyber threats
  • Artificial intelligence-driven fraud prevention
  • Malware analysis
  • Understanding the effects of adversarial networks (GANs)
  • Detecting network traffic anomalies
  • Applying Reinforced Learning Techniques
  • Evaluating the performance of artificial intelligence models
  • Developing models in machine learning (ML) and deep learning (DL)
  • Feature engineering for ML and raw data optimization for DL
  • Metamorphic malware counter design
  • Analyzing opcode sequences to classify them as malicious or legitimate software
  • Identifying threats driven by artificial intelligence
  • Applying models to real cybersecurity challenges

What you will learn in the course

Courses for which the course is suitable

  • Cybersecurity expert
  • Malware Analyzer
  • Machine Learning Model Developer
  • Data Engineer
  • Artificial Intelligence Expert for Cybersecurity
  • Threat Analyst
  • Cybersecurity tool developer
  • Deep Learning Engineer
  • IoT Systems Specialist

Internship – 3-part course series

This specialization is designed for undergraduate students who aspire to master artificial intelligence applications in cybersecurity. Over three comprehensive courses, you will learn advanced techniques for detecting and dealing with various cyber threats.

Essential topics in the curriculum:

  • Artificial intelligence-driven fraud prevention
  • Malware analysis
  • Effects of adversarial networks (GANs)

You will gain practical experience in identifying network traffic anomalies, implementing reinforcement learning techniques, and evaluating the performance of artificial intelligence models against real-world challenges.

Upon completion of the training, you will develop a deep understanding of how to secure AI systems while dealing with the complexities of adversarial attacks. This knowledge will prepare you to face new challenges in the field of cybersecurity.

Hands-on Learning Project

In the “Artificial Intelligence for Cybersecurity” specialization, learners will use artificial intelligence techniques to develop practical cybersecurity tools.

Projects include:

  • Developing machine learning (ML) and deep learning (DL) models to detect Internet of Things (IoT) botnet activity.
  • Feature engineering for ML and raw data optimization for DL.
  • Designing a metamorphic malware counter using a hidden Markov model.
  • Analyzing opcode sequences to classify them as malicious or legitimate software.

Learners will build models, test them on previously unseen data, and submit video demonstrations along with their code. This hands-on approach equips learners with the skills to identify AI-driven threats and apply models to real-world cybersecurity challenges.

Details of the courses that make up the specialization

Introduction to AI for Cybersecurity

Course 1 – 9 hours

Use AI techniques to identify and mitigate various cyber threats, while protecting digital assets and data.

  • Developing and implementing machine learning models to identify, classify, and prevent spam and phishing emails.
  • Implementing AI-driven biometric solutions such as keystroke dynamics and facial recognition to improve user identification security.

Skills you will gain

  • AI Applications in Cybersecurity
  • Cyber ​​Threat Risk Management
  • AI-driven user identification
  • Developing practical ML models
  • Spam and phishing email detection

Course 2 – 11 hours

Understand different types of malware and apply basic analysis techniques to effectively identify and classify them.

  • Implementing advanced machine learning algorithms, including clustering and decision trees, to effectively identify malware.
  • Explore anomaly detection techniques using botnet data and learn how to analyze network traffic to identify unusual patterns.
  • Collaboration and presentation of research findings on current trends in network anomaly detection.

Skills you will gain

  • Malware analysis
  • Research presentation skills
  • Machine learning for identification
  • Anomaly detection techniques
  • Performance evaluation

Course 3 – 15 hours

Learn how to implement AI-based solutions to detect and prevent credit card fraud in cloud environments.

  • Exploring the fundamentals of adversarial generation networks and their application in synthetic data generation.
  • Practical experience with aggressive black and white form attacks to assess and improve the robustness of the model.
  • Mastery of feature engineering and performance evaluation techniques to optimize AI models for cybersecurity applications.

Skills you will gain

  • Fraud detection techniques
  • Implementing aggressive attacks
  • Model evaluation and optimization
  • Generative Adversarial Networks (GANs)
  • Reinforcement learning applications

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