This Specialization is designed for post-graduate students aiming to master AI applications in cybersecurity. Through three comprehensive courses, you will explore advanced techniques for detecting and mitigating various cyber threats. The curriculum covers essential topics such as AI-driven fraud prevention, malware analysis, and the implications of Generative Adversarial Networks (GANs). You will gain hands-on experience in identifying anomalies in network traffic, implementing reinforcement learning techniques for adaptive security measures, and evaluating AI model performance against real-world challenges. By completing this Specialization, you will develop a deep understanding of how to secure AI systems while addressing the complexities of adversarial attacks. This knowledge will prepare you to tackle emerging cybersecurity challenges, making you a valuable asset in the rapidly evolving field of digital security. With a focus on practical applications and industry-relevant skills, you will be well-equipped for a career in AI-driven cybersecurity.
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Spécialisation AI for Cybersecurity
Master AI Techniques for Cybersecurity Challenges. Develop expertise in advanced AI techniques to detect and prevent cybersecurity threats, ensuring robust protection against evolving digital risks.
Instructeur : Lanier Watkins
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Ce que vous apprendrez
Implement AI-driven techniques for detecting and mitigating advanced malware and network anomalies effectively.
Utilize Generative Adversarial Networks (GANs) to understand and counteract adversarial attacks in AI systems.
Evaluate AI model performance and apply reinforcement learning to enhance adaptive cybersecurity measures.
Vue d'ensemble
Compétences que vous acquerrez
- Malware Protection
- Feature Engineering
- Threat Detection
- Cyber Attacks
- Deep Learning
- Cyber Threat Intelligence
- System Design and Implementation
- Continuous Monitoring
- Cybersecurity
- Email Security
- Authentications
- Intrusion Detection and Prevention
- Threat Modeling
- Network Analysis
- Network Security
- Artificial Intelligence and Machine Learning (AI/ML)
- Machine Learning Software
- Anomaly Detection
Outils que vous découvrirez
Ce qui est inclus
Ajouter à votre profil LinkedIn
Améliorez votre expertise en la matière
- Acquérez des compétences recherchées auprès d’universités et d’experts du secteur
- Maîtrisez un sujet ou un outil avec des projets pratiques
- Développez une compréhension approfondie de concepts clés
- Obtenez un certificat professionnel auprès de Johns Hopkins University
Spécialisation - série de 3 cours
Ce que vous apprendrez
Use AI techniques to detect and mitigate various cyber threats, protecting digital assets and data.
Develop and apply machine learning models to identify, classify, and filter spam and phishing emails.
Implement AI-driven biometric solutions like keystroke dynamics and facial recognition to enhance user authentication security.
Compétences que vous acquerrez
Ce que vous apprendrez
Understand various types of malware and apply foundational analysis techniques to effectively detect and classify them.
Implement advanced machine learning algorithms, including clustering and decision trees, for efficient malware detection.
Explore anomaly detection techniques using botnet data and learn how to analyze network traffic for unusual patterns.
Collaborate and present research findings on current trends in network anomaly detection, enhancing communication and analytical skills.
Compétences que vous acquerrez
Ce que vous apprendrez
Learn to implement AI-based solutions to detect and prevent credit card fraud in cloud environments.
Explore the fundamentals of Generative Adversarial Networks and their applications in generating synthetic data.
Gain hands-on experience with black-box and white-box adversarial attacks to assess and enhance model resilience.
Master techniques in feature engineering and performance evaluation to optimize AI models for cybersecurity applications.
Compétences que vous acquerrez
Obtenez un certificat professionnel
Ajoutez ce titre à votre profil LinkedIn, à votre curriculum vitae ou à votre CV. Partagez-le sur les médias sociaux et dans votre évaluation des performances.
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Foire Aux Questions
The specialization is designed to be completed at your own pace, but on average, it is expected to take approximately 3 months to finish if you dedicate around 5 hours per week. However, as it is self-paced, you have the flexibility to adjust your learning schedule based on your availability and progress.
You are encouraged to take the courses in the recommended sequence to ensure a smoother learning experience, as each course builds on the knowledge and skills developed in the previous ones. However, you are not required to follow a specific order, and you can take the courses in the order that best suits your needs and prior knowledge.
This course is completely online, so there’s no need to show up to a classroom in person. You can access your lectures, readings and assignments anytime and anywhere via the web or your mobile device.
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