Scalable Learning Environments for Teaching Cybersecurity Hands-on

• URL: http://arxiv.org/abs/2110.10004v2
• Date: Wed, 5 Jan 2022 08:55:36 GMT
• Title: Scalable Learning Environments for Teaching Cybersecurity Hands-on
• Authors: Jan Vykopal, Pavel \v{C}eleda, Pavel Seda, Valdemar \v{S}v\'abensk\'y, and Daniel Tovar\v{n}\'ak
• Abstract summary: This paper describes a technical innovation for scalable teaching of cybersecurity hands-on classes using interactive learning environments. We present our research effort and practical experience in designing and using learning environments that scale up hands-on cybersecurity classes.
• Score: 0.4893345190925178
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This Innovative Practice full paper describes a technical innovation for scalable teaching of cybersecurity hands-on classes using interactive learning environments. Hands-on experience significantly improves the practical skills of learners. However, the preparation and delivery of hands-on classes usually do not scale. Teaching even small groups of students requires a substantial effort to prepare the class environment and practical assignments. Further issues are associated with teaching large classes, providing feedback, and analyzing learning gains. We present our research effort and practical experience in designing and using learning environments that scale up hands-on cybersecurity classes. The environments support virtual networks with full-fledged operating systems and devices that emulate real-world systems. (...) Using the presented environments KYPO Cyber Range Platform and Cyber Sandbox Creator, we delivered the classes on-site or remotely for various target groups of learners (K-12, university students, and professional learners). The learners value the realistic nature of the environments that enable exercising theoretical concepts and tools. The instructors value time-efficiency when preparing and deploying the hands-on activities. Engineering and computing educators can freely use our software, which we have released under an open-source license. We also provide detailed documentation and exemplary hands-on training to help other educators adopt our teaching innovations and enable sharing of reusable components within the community.

Related papers

• An Innovative Solution: AI-Based Digital Screen-Integrated Tables for Educational Settings [0.0]
  Digital screen-integrated tables are designed specifically for educational settings. Tables feature integrated digital screens controlled by a central processing unit (CPU) The invention facilitates the collection of student performance data during classroom activities and assessments.
  arXiv  Detail & Related papers  (2024-10-08T08:00:17Z)
• Robot Fine-Tuning Made Easy: Pre-Training Rewards and Policies for Autonomous Real-World Reinforcement Learning [58.3994826169858]
  We introduce RoboFuME, a reset-free fine-tuning system for robotic reinforcement learning. Our insights are to utilize offline reinforcement learning techniques to ensure efficient online fine-tuning of a pre-trained policy. Our method can incorporate data from an existing robot dataset and improve on a target task within as little as 3 hours of autonomous real-world experience.
  arXiv  Detail & Related papers  (2023-10-23T17:50:08Z)
• Human-oriented Representation Learning for Robotic Manipulation [64.59499047836637]
  Humans inherently possess generalizable visual representations that empower them to efficiently explore and interact with the environments in manipulation tasks. We formalize this idea through the lens of human-oriented multi-task fine-tuning on top of pre-trained visual encoders. Our Task Fusion Decoder consistently improves the representation of three state-of-the-art visual encoders for downstream manipulation policy-learning.
  arXiv  Detail & Related papers  (2023-10-04T17:59:38Z)
• Smart Environment for Adaptive Learning of Cybersecurity Skills [0.5735035463793008]
  We designed a unique and novel smart environment for adaptive training of cybersecurity skills. The environment collects a variety of student data to assign a suitable learning path through the training. We evaluated the learning environment using two different adaptive trainings attended by 114 students of various proficiency.
  arXiv  Detail & Related papers  (2023-07-11T14:20:29Z)
• Dexterous Manipulation from Images: Autonomous Real-World RL via Substep Guidance [71.36749876465618]
  We describe a system for vision-based dexterous manipulation that provides a "programming-free" approach for users to define new tasks. Our system includes a framework for users to define a final task and intermediate sub-tasks with image examples. experimental results with a four-finger robotic hand learning multi-stage object manipulation tasks directly in the real world.
  arXiv  Detail & Related papers  (2022-12-19T22:50:40Z)
• DMCNet: Diversified Model Combination Network for Understanding Engagement from Video Screengrabs [0.4397520291340695]
  Engagement plays a major role in developing intelligent educational interfaces. Non-deep learning models are based on the combination of popular algorithms such as Histogram of Oriented Gradient (HOG), Support Vector Machine (SVM), Scale Invariant Feature Transform (SIFT) and Speeded Up Robust Features (SURF) The deep learning methods include Densely Connected Convolutional Networks (DenseNet-121), Residual Network (ResNet-18) and MobileNetV1.
  arXiv  Detail & Related papers  (2022-04-13T15:24:38Z)
• Flashlight: Enabling Innovation in Tools for Machine Learning [50.63188263773778]
  We introduce Flashlight, an open-source library built to spur innovation in machine learning tools and systems. We see Flashlight as a tool enabling research that can benefit widely used libraries downstream and bring machine learning and systems researchers closer together.
  arXiv  Detail & Related papers  (2022-01-29T01:03:29Z)
• The Other Side of Black Screen: Rethinking Interaction in Synchronous Remote Learning for Collaborative Programming [0.0]
  Collaborative learning environments are crucial for learning experiential hands-on skills such as critical thinking and problem solving. In this case study, we present observations of in-person and online versions of 2 programming courses offered before and during the COVID-19 pandemic. We find that the current online video-conferencing platforms cannot foster collaborative learning among peers.
  arXiv  Detail & Related papers  (2021-11-11T01:52:12Z)
• Teaching Creativity Using a Realistic Multi-User Operation: Packet Tracer [0.0]
  Multi-user capabilities in Packet Tracer provide an incentive for immersive realistic learning to increase the efficiency of teaching and learning in the networking class. The multi-user functionality that uses the one-to-many remote relay server concept allows lecturers to remotely control and test many students at one time.
  arXiv  Detail & Related papers  (2020-12-27T01:56:26Z)
• The Ingredients of Real-World Robotic Reinforcement Learning [71.92831985295163]
  We discuss the elements that are needed for a robotic learning system that can continually and autonomously improve with data collected in the real world. We propose a particular instantiation of such a system, using dexterous manipulation as our case study. We demonstrate that our complete system can learn without any human intervention, acquiring a variety of vision-based skills with a real-world three-fingered hand.
  arXiv  Detail & Related papers  (2020-04-27T03:36:10Z)
• Never Stop Learning: The Effectiveness of Fine-Tuning in Robotic Reinforcement Learning [109.77163932886413]
  We show how to adapt vision-based robotic manipulation policies to new variations by fine-tuning via off-policy reinforcement learning. This adaptation uses less than 0.2% of the data necessary to learn the task from scratch. We find that our approach of adapting pre-trained policies leads to substantial performance gains over the course of fine-tuning.
  arXiv  Detail & Related papers  (2020-04-21T17:57:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.