Cyber Security and SIEM Training
Introduction to Cyber Security
Gain an understanding of the fundamentals of cyber security, including key concepts, principles, and the importance of protecting information assets.
Network Security
Explore network security concepts and practices. Learn about network threats, firewalls, intrusion detection and prevention systems, and securing network communications.
Security Policies and Procedures
Study the development and implementation of security policies and procedures. Understand how to create policies to safeguard information and ensure compliance with regulatory requirements.
Risk Management
Learn about risk management techniques in cyber security. Explore methods for identifying, assessing, and mitigating security risks to protect organizational assets.
Security Architecture and Design
Understand the principles of security architecture and design. Study how to design secure systems, implement security controls, and evaluate the effectiveness of security measures.
Incident Response and Management
Learn how to respond to and manage security incidents. Study incident response strategies, including detection, analysis, containment, eradication, and recovery.
Security Information and Event Management (SIEM)
Explore SIEM systems and their role in cyber security. Learn how to deploy, configure, and manage SIEM solutions to collect, analyze, and respond to security events and incidents.
Threat Intelligence and Analysis
Study threat intelligence and analysis techniques. Learn how to gather and analyze threat data to identify and respond to emerging cyber threats.
Compliance and Legal Issues
Understand compliance and legal issues related to cyber security. Explore regulatory frameworks, standards, and laws that impact information security practices.
Case Studies and Practical Exercises
Engage in case studies and practical exercises to apply cyber security concepts. Work on real-world scenarios to develop hands-on skills in security analysis and incident response.
Emerging Trends and Future Directions
Explore emerging trends and future directions in cyber security. Learn about new technologies, evolving threats, and how they impact security practices and strategies.
CUDA Programming syllabus
Introduction to GPU Computing and CUDA
- Overview of GPU Architecture and CUDA Programming Model
- Evolution and Benefits of GPU Computing
- CUDA Programming Paradigm and Basic Concepts
CUDA Programming Basics
- Setting Up CUDA Development Environment (CUDA Toolkit, IDE)
- Writing and Compiling CUDA Programs
- Understanding CUDA Threads, Blocks, and Grids
Memory Hierarchy in CUDA
- Overview of CUDA Memory Model (Global, Shared, Constant, and Local Memory)
- Memory Allocation and Management in CUDA
- Optimization Techniques for Memory Access Patterns
CUDA Thread Coordination
- Synchronization and Communication Between CUDA Threads
- Thread Divergence and Warp Execution Model
- Utilizing Thread Synchronization Primitives (e.g., Barriers, Locks)
CUDA Kernel Optimization
- Techniques for Optimizing CUDA Kernels (Memory Coalescing, Loop Unrolling)
- Performance Considerations and Profiling Tools (nvprof)
- Hands-on Exercises in Optimizing CUDA Code
Advanced CUDA Memory Management
- Unified Memory and Managed Memory in CUDA
- Asynchronous Memory Operations and Data Transfers
- Best Practices for Memory Usage in CUDA Applications
CUDA Libraries and Utilities
- Overview of CUDA-Accelerated Libraries (cuBLAS, cuFFT, cuDNN)
- Integrating CUDA Libraries into Applications
- Using CUDA Thrust for High-Level GPU Programming
Multi-GPU Programming with CUDA
- Scalable Parallelism with Multiple GPUs
- CUDA Multi-GPU Programming Techniques (MPI, CUDA-aware MPI)
CUDA Applications and Case Studies
- Real-World Applications of CUDA in Various Domains (e.g., Scientific Computing, Deep Learning)
- Case Studies of CUDA-Accelerated Projects and Success Stories
CUDA and Deep Learning
- Overview of CUDA Support in Deep Learning Frameworks (e.g., TensorFlow, PyTorch)
- Accelerating Neural Network Training and Inference with CUDA
- Implementing Custom Layers and Optimizations in CUDA
CUDA and Image Processing
- GPU-Accelerated Image Processing Techniques with CUDA
- Implementing Filters, Transformations, and Feature Extraction Using CUDA
- Case Studies in CUDA-Powered Image Processing Applications
CUDA and Parallel Algorithms
- Parallel Algorithm Design and Implementation in CUDA
- Implementing Parallel Sorting, Reduction, and Other Algorithms
- Analyzing Performance and Scalability of Parallel Algorithms
CUDA and Real-Time Systems
- CUDA Applications in Real-Time and Embedded Systems
- Challenges and Considerations for Real-Time CUDA Programming
- Case Studies in Real-Time CUDA Applications
CUDA and High Performance Computing (HPC)
- CUDA in High-Performance Computing (HPC) Clusters
- Optimizing CUDA Applications for Large-Scale Distributed Computing
- Managing Data Locality and Communication Overhead in CUDA HPC
Future Trends in CUDA and GPU Computing
- Emerging Technologies and Advancements in CUDA
- GPU Architectures and Trends in Parallel Computing
- Exploring CUDA for AI, IoT, and Other Emerging Fields
Capstone Project (if applicable)
- Design and Implementation of a CUDA-Accelerated Application
- Project-Based Learning with Mentorship and Feedback
Training
Basic Level Training
Duration : 1 Month
Advanced Level Training
Duration : 1 Month
Project Level Training
Duration : 1 Month
Total Training Period
Duration : 3 Months
Course Mode :
Available Online / Offline
Course Fees :
Please contact the office for details