Cybersecurity Workforce Program - Healthcare

Program Overview

The combination of all three levels of cybersecurity knowledge: Explorer, Practitioner, and Professional bundles are combined into one with a an included Capstone module at the end that encapsulates everything that has been learned along the program.

What You'll Learn

• Basic Computer Knowledge
• Computer hardware knowledge
• Computer privacy and security laws
• Artificial Intelligence
•  Introduction to Networks and Databases
• Moderate knowledge on cybersecurity threats, attacks, vulnerabilities
• Network traffic
• Blockchain
• Cloud security
• Advanced knowledge on Databases
• Risk Analysis
• Internet of Things
• Cybersecurity Research
• Advanced Cryptography knowledge

Admission Requirements

• 18 years of age or older

Curriculum Details

Sample courses include:

• IT Basics: This module provides students with a basic understanding of the components in an information technology system and their roles in system operation.

• Coding: This module provides an introduction to problem-solving techniques and the computer program development process through coding in a high-level language. Topics include: program structure; data types; variables; operations; expressions; input/output; sequence, selection, and repetition; functions; data structures; and software design techniques. Concepts are reinforced with many programming laboratory-exercises throughout the course.

• Security Principles & Foundations: This module provides an overview of the cybersecurity field and its related concepts. An introduction to cybersecurity terminology, best practices, principles and standards, planning and management of cybersecurity functions and assets are included. This module will provide a foundation for understanding common threats and attacks and the methods and tools to defend and protect against them, an overview of human, organizational, social, and legal issues related to cybersecurity. and concepts which meet national standards in cybersecurity.

• Network Foundations: This course discusses the foundations of computer networks. The course participant will learn about the network architecture including the OSI model and the layering concept. The TCP/IP protocol stack layers will be discussed. Participants will learn about physical layer concepts including communication media, multiplexing, and switching, data link layer concepts and protocols (e.g., MAC protocols for wired LANs such as Ethernet, and ARP), network layer concepts and protocols (e.g., routing and IPv4), transport layer concepts and protocols (e.g., reliable data transfer, TCP, UDP), and application layer concepts such as client-servers. The course also provides an overview of wireless networking technologies (WiFi).

• Artificial Intelligence: This module provides an overview of the fundamental techniques and approaches in artificial intelligence. Topics include the history of AI; knowledge and reasoning; problem solving; learning approaches; practical considerations; and responsible AI. The module uses case studies from cybersecurity and healthcare applications.

• Cryptography: This module provides an overview of the cryptography field and its related applications. The statistical properties of digital data types such as text, image, and sound are introduced. Basic cryptographic substitution and permutation methods are discussed together with simple software tools. This module presents introduction to symmetric key and public key standards such as Data Encryption Standard (DES), Advanced Encryption Standard (AES), RSA Public-key Cryptosystem, and ElGamal Public-key Cryptosystem. Applications such as data encryption, message authentication, and digital signature are included. Concepts are reinforced with many programming laboratory-exercises throughout the course.

• Privacy-Legal Foundations & Ethics: This course examines legal, privacy, and ethical issues of information security in the electronic and internet environment in healthcare.

• Database Management: Provides an overview of fundamental concepts of databases and database management. These concepts include aspects of database design, database management systems, data models, normalization, the standard relational database query, SQL, basic database security and relational database alternatives.

• Cloud Foundations: Students will be exposed to the current practices in cloud computing. Topics may include cloud service models such as Infrastructure as a Service (IaaS), Platform as a Service (PaaS), Software as a Service (SaaS), technologies supporting cloud computing such as virtualization, motivating factors, benefits and challenges of the cloud, cloud storage, performance and systems issues, disaster recovery, federated clouds, data centers, and cloud security. Students will be exposed to a leading public cloud computing platform.

• Blockchain: This course will be an introduction to Blockchain. There will be a modularized approach, to focus development on the skills for each aspect of blockchain. Labs based on practical applications e.g. bitcoins are incorporated.

• Data Mining: This module provides an overview of basic concepts in data mining and main methodologies and approaches used for knowledge discovery from data. Topics include the definition of data mining field; data mining process; data preprocessing; predictive and descriptive data mining; evaluation of data mining models; and privacy and security of data. The?module?uses?case studies?from healthcare.

• Cognitive Computing: This module provides lectures and labs on deep learning and machine learning using combination of tools such as cognitive services APIs, Deep Learning frameworks and libraries. Two major breakthroughs for cognitive computing in healthcare are: 1) consumer/user engagement (e.g.example IBM Watson social network Welltok) and 2) discovery applications, such as drug discovery and analysis of human health. Internet of Medical Things integrates sensors and AI algorithms, which are vulnerable to cyber-attacks. Examples of attacks are: 1) access by malicious actors; 2) loss or corruption of enterprise information and patent data. Topics include: Neural Networks, Convolutional Neural Network (CNN), Long – Short Term Memory (LSTM), Natural Language Processing (NLP) and Deep Reinforcement Learning (Deep RL).

• Information Security: This module is designed to give students a basic competency in the principles of information security and how they relate to computing systems, particularly at the level of the operating system. Key areas of focus include fundamental security design principles, the adversarial model, data security, virtualization, viruses & malware, and operating-system specific information security concepts for both desktop and mobile systems.

• Network Security: The module discusses fundamental concepts and principles of network security. The course covers basic security topics, including networking basics, network traffic signatures, cryptography, wireless networking, wireless security, firewalls, IDPSs, virtual private network, and web security.

• Digital Forensics: This module covers the fundamentals of digital forensics. Topics will include historical issues, key concepts, and tools and techniques of the trade. In addition, reporting methods will be discussed. Various open-source tools will be used in the lab portion of the module.

• Cyber Threat Hunting: This module covers the basics of cyber threat hunting in a secure operations center. Students will learn the mindset of the adversary and methods to identify new and emerging cyber threats in an organizational environment. Topics include log analysis, event correlation, open-source threat intelligence, use of AI in threat identification, honeypots, and threat sharing. Students will practice new skills in a simulated threat environment.

• Cloud Security: This course introduces you to cybersecurity for the cloud. Students will learn and apply classic security techniques to today’s cloud security problems. The course will start with a deceptively simple and secure web service and address the problems arising as we improve it. Students will learn how to analyze recent cloud security vulnerabilities using standard, systematic techniques. In this course, students will build their own web service case studies and construct security solutions for them.

• Database Security: Provides an overview of fundamental concepts of databases security. These concepts include access control and data protection.

• Internet of Things (IoT): This module provides basic knowledge and understanding of fundamental concepts of Internet of Things (IoT). This includes: Overview of IoT that is required from design perspective, details of technical building blocks of IoT , concepts of machine learning and data analytics in IoT, communication and security challenges in IoT common industry use cases.

• Post Quantum Cryptography: This module covers the foundational principles and techniques of quantum technology, including topics such as basic quantum mechanics; quantum information theory and quantum algorithms such as Grover’s search and Shor’s factoring; quantum error-correcting codes; and quantum computing applications such as quantum encryption and quantum key distribution. We will explore and discuss real world scenarios related to the understanding of quantum technologies, the participants will understand the opportunities and challenges presented by quantum technology and will have a hands-on experience with quantum computers and algorithms using IBM Q Experience quantum devices.

• Risk Analysis: This module provides an overview of the fundamental techniques and approaches to identify, asses, and mitigate cybersecurity risk. Topics include identification of cybersecurity risks, assessment and mitigation approaches. This module will provide case studies related to cybersecurity risk analysis in the healthcare industry.

• Robotics Process Automation Analysis: This module provides lectures and labs on the system architecture enabling robotics and common use cases for robotics in healthcare. Topics include: Distinguish the characteristics of operating systems enabling robotics; Distinguish the characteristics of operating systems enabling robotics; Common threats and cyber-attacks to robotic systems; The ethical issues related to the use of robotics and the need for cybersecurity to enable trust in robotic operations.