1️⃣ Mobile DevSecOps
Integration of security checks (static code analysis, runtime security, vulnerability scanning) within mobile CI/CD pipelines. GitHub Dependabot: Automatically scans and updates mobile dependencies to address security vulnerabilities proactively. SonarQube/Snyk: Provides automated static analysis tailored specifically for Android and iOS apps, flagging security vulnerabilities early in development. CI/CD Security Integration: Students set up GitHub Actions pipelines integrating SonarQube and OWASP Mobile Security Testing Guide scans to detect insecure code patterns and configurations early, reinforcing the culture of security throughout development. 2️⃣ API Security for Mobile Apps
Mobile apps heavily rely on APIs, making them targets for common exploits such as unauthorized access, data interception, and man-in-the-middle attacks. Using AWS API Gateway or Azure API Management with OAuth and JWT tokens to secure API endpoints. Students develop secure API backends with OAuth authentication and JWT tokens, integrate them with mobile apps, and simulate attacks like token interception, understanding proper mitigation (e.g., using SSL pinning or secure key management). 3️⃣ Secure Data Storage & On-Device AI Privacy
Mobile devices store sensitive personal data, making secure storage crucial. As on-device AI becomes commonplace, privacy-preserving techniques (on-device encryption, federated learning) gain importance. Signal App: Demonstrates secure data storage, encryption standards, and privacy-preserving messaging. Apple Core ML & Google’s ML Kit: Run sensitive AI operations locally to protect privacy. Implement secure encrypted storage (Android's Keystore, iOS Keychain) in an app to store sensitive data, ensuring compliance with regulations like GDPR. Experiment with federated learning by running TensorFlow Federated (TFF) to train a model across multiple simulated mobile clients, reinforcing the principle of data minimization and privacy. 4️⃣ User Authentication and Biometrics
Mobile apps often integrate biometric authentication (fingerprint, Face ID) which require specialized security considerations around biometric data handling. Banking apps (Scotiabank, RBC, TD) utilize biometric authentication for secure yet convenient logins. Develop a mobile application utilizing biometric APIs (Android BiometricPrompt, Apple's Face ID/Touch ID APIs), teaching students how to securely authenticate users and manage biometric data securely. 🛠️ Ideas for Lab Infrastructure for Cybersecurity:
GitHub Actions, SonarQube, OWASP Dependency Check AWS API Gateway, Firebase Authentication, Google ML Kit, TensorFlow Federated Android Studio, Xcode, Visual Studio Code Android/iOS devices for testing (multiple generations) Secure test environments (isolated test network for security experiments). VM Ware sand boxes. 📚 Potential Course Structure:
Semester-wise progression (Cybersecurity integrated):
Semester 1: Introduction to secure mobile app programming and secure coding best practices. Labs on basic user authentication and secure data storage. Semester 2: DevOps with security-focused CI/CD pipeline development, mobile vulnerability scanning, and secure app distribution. Semester 3: AI and IoT security: secure data integration, privacy-preserving ML, and secure sensor data transmission. Semester 4: Advanced DevSecOps (AI-driven security scanning), ethical AI deployment, capstone projects integrating comprehensive security practices. The goal is for students to understand that cybersecurity is first a cultural mindset. Social engineering accounts for 90% of attack vectors. You can attack the people or attack the technology, and attacking people (social engineering) is always faster and cheaper.
Fold it in as an integral part of mobile app development from the outset, aligning with both current industry requirements and future job market expectations.
