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Detect & Respond to Mobile AI Threats

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Coursera

Detect & Respond to Mobile AI Threats

Reza Moradinezhad
Starweaver

Instructors: Reza Moradinezhad

Included with Coursera Plus

Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

4 hours to complete
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

4 hours to complete
Flexible schedule
Learn at your own pace

What you'll learn

  • Analyze how AI features like sensors, models, and agents make phones attack surfaces and enable deepfake-based scams.

  • Evaluate technical attack paths—zero-permission inference and multi-layer agent attacks—using real research cases.

  • Design a mobile-focused detection and response plan with simple rules, containment steps, and key resilience controls.

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Recently updated!

December 2025

Assessments

1 assignment

Taught in English

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There are 3 modules in this course

This module sets the mental model for how AI embeds across the phone—keyboards, cameras, sensors, and agents—and why that expands risk. Learners examine deepfake social engineering and zero-permission inference attacks that leak behavior. They connect people/model/GUI/system layers to real incidents. A short intro activity builds intuition before deeper technical work.

What's included

4 videos2 readings1 peer review

This module examines the mechanics of AI-powered mobile exploits, from zero-permission sensor inference to multi-layer AI agent hijacking. Learners study real research cases, explore how deep learning amplifies attacks, and analyze adversarial examples and AI-enabled malware. The focus is on understanding how technical threats operate in practice, preparing learners for hands-on detection in the next module.

What's included

3 videos1 reading1 peer review

This module converts theory into practice by focusing on detection signals, response steps, and resilience controls. Learners design telemetry rules, run an incident response simulation, and propose hardening measures such as allow-lists, verified links, and attestation. The goal is to build practical readiness against mobile AI-driven threats.

What's included

4 videos1 reading1 assignment2 peer reviews

Instructors

Reza Moradinezhad
Coursera
4 Courses3,918 learners

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Coursera

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