Winter Semester 2024-2025

Content

• Introduction to Operating Systems (OS) – Role, purpose and design issues
• Processes and Threads – OS structures, process control, abstractions, kernel/user modes and operations, context switching, interrupts
• Inter-Process Communication – Message passing IPC, RPC, layers, interfaces, hierarchies
• Coordination: Deadlocks – Process coordination, critical sections, deadlock characterization, deadlock detection and recovery, deadlock avoidance
• Scheduling/Resource Management – Task ordering, preemptive and non-preemptive scheduling, schedulers and policies, OS implementations
• Concurrency: Races, Mutual Exclusions – Critical sections, races, spin locks, synchronization
• Programming Abstractions: Semaphores – Semaphores, Monitors
• Memory Management – Storage structures, management/replacements approaches, virtual memory, paging, caching, segmentation
• I/O – Device management, drivers, segmentation, interrupt handling, DMA
• File systems – File systems requirements, design and implementation, file structures, directories, naming, partitions, virtual file systems
• Fault Tolerance/Resilience – Fault types, fault handling approaches, reliable message delivery, OS reliability and availability, security issues
• Embedded/RT OS – Memory/disk/performance management, recovery, fault-tolerances, real-time aspects
• Distributed OS – Distributed computation and communication abstractions, synchronization, coordination, consistency
• Virtual Machines – Purpose and types of virtualization, virtual file systems, Hypervisors

Literature

_{Operating Systems: Three Easy Pieces (v0.92); Remzi H. Arpaci-Dusseau & Andrea C. Arpaci-Dusseau; Arpaci-Dusseau Books}

_{Modern Operating Systems, 4th Edition; Andrew S. Tanenbaum & Herbert Bos; Pearson; ISBN-13: 978-0133591620}

_{Operating System Concepts, 9th Edition; Abraham Silberschatz, Peter B. Galvin, Greg Gagne; John Wiley & Sons; ISBN-13: 978-1118093757}

Please check Moodle for more information:

https://moodle.informatik.tu-darmstadt.de/course/view.php?id=1676

Content

In this practical course, the students deal with different security aspects of artificial intelligence (AI) and systems. The project tasks comprise the following areas:

• Design and implementation of selected software attacks (ethical hacking)
• Design and implementation of secure user apps
• Modifications of the Android Middleware and Kernel to build security architectures
• System programming in general
• Applications of Machine Learning for Security
• Security and Privacy of Deep Neural Networks

Please check Moodle for full information:

https://moodle.informatik.tu-darmstadt.de/course/view.php?id=1678

Content

In today's rapidly evolving technological landscape, artificial intelligence, specifically deep learning, becomes able to solve increasingly complex tasks. Their increasing capabilities make them useful for real-world problems and deployed even for critical tasks such as making hiring decisions, medical diagnostics, or safety-critical operations. However, deep learning algorithms face new challenges, constraints, and threats when deployed outside well-controlled test environments to solve real-world problems.

In this seminar, students will summarize and critically analyze the recent literature on the assigned topic in the form of a report. Additionally, each student will present his work in front of the group at the end of the semester.
Possible topics include:

• Fair and non-biased deep learning
• Explainable and trustworthy decisions
• Privacy and the right to be forgotten for deep neural networks
• Robust and trustworthy deep learning for security-critical applications
• Distributed learning schemes
• Deep neural networks in environments with runtime constraints
• Detecting AI-generated content
• Responsible deep fake generation

Please check Moodle for full information:

https://moodle.informatik.tu-darmstadt.de/course/view.php?id=1677