Developed in response to growing threats posed by Industry 4.0 and the development of Smart Factories. This course has been developed for manufacturing engineers/managers to help protect manufacturing systems and machines against cyber threats.

Overview

  • Start dateOctober
  • DurationFull-time MSc - one year, Full-time PgCert - one year, Full-time PgDip - one year
  • DeliveryTaught modules 40%, Group project 20%, Individual project 40%
  • QualificationMSc, PgDip, PgCert
  • Study typeFull-time / Part-time
  • CampusCranfield campus

Who is it for?

This course develops the expertise of graduates interested in pursuing careers tackling cybersecurity challenges and technologies in manufacturing.

Why this course?

Developed with academics and industry from manufacturing and the defence and security sector to address the current career demand in Internet of Things (IoT), Big Data, Cloud Computing and Cybersecurity it combines Cranfield's long standing expertise for delivering high-quality Masters programmes in the manufacturing, and security and defence sectors.

This course addresses the main challenges in smart manufacturing, such as to:

  • Identify cyber threats in manufacturing systems from cloud
  • Protect manufacturing systems from cyber attacks
  • Improve incident response and disaster recovery in manufacturing systems
  • Assess the cost of cybersecurity solutions for manufacturing systems.

Students benefit from our wide-range of equipment, analysis tools and specialist software packages.

Informed by Industry

In partnership with the MTA (The Manufacturing Technologies Association).

Cranfield courses receive strong support from our industrial partners. There is a strong emphasis on applying knowledge in the industrial environment and all teaching is in the context of industrial application. This course provides industrially relevant projects and transferable skills for developing graduates entering into this cutting-edge market.

Course details

Eight one-week assessed modules, a group project and an individual project. Students are also supported in their learning and personal development through exposure to; industry seminars, group poster session, group discussions, group presentations, video demonstrations, case studies, laboratory experiments, coursework and project work.

Course delivery

Taught modules 40%, Group project 20%, Individual project 40%

Group project

The group project experience is highly valued by both students and prospective employers and is usually in collaboration with industry. It provides students with the opportunity to take responsibility for a consultancy-type project, finding solutions to real-life challenges in manufacturing informatics. As a result of external engagement Cranfield students enjoy a higher degree of success when it comes to securing employment. Prospective employers value the student experience where team working to find solutions to industrially based problems are concerned.

Individual project

The individual thesis project, usually in collaboration with industry, offers students the opportunity to develop their research capability, depth of understanding and ability to provide solutions to real problems in manufacturing production systems.

Modules

Keeping our courses up-to-date and current requires constant innovation and change. The modules we offer reflect the needs of business and industry and the research interests of our staff and, as a result, may change or be withdrawn due to research developments, legislation changes or for a variety of other reasons. Changes may also be designed to improve the student learning experience or to respond to feedback from students, external examiners, accreditation bodies and industrial advisory panels.

To give you a taster, we have listed the compulsory and elective (where applicable) modules which are currently affiliated with this course. All modules are indicative only, and may be subject to change for your year of entry.


Course modules

Compulsory modules
All the modules in the following list need to be taken as part of this course

Manufacturing Systems Engineering

Module Leader
  • Professor Harris Makatsoris
Aim

    To develop students’ understanding of manufacturing systems engineering in order to analyse and (re)design manufacturing systems that maximise value to customers while minimising waste.

Syllabus
    • Design of layouts.
    • Human centred factory design.
    • Group Technology & Cellular manufacturing.
    • Different approaches to factory layout such as process and product layouts.
    • Reasons for choice of cellular manufacturing and benefits.
    • Manufacturing Systems modelling using discrete-event simulation.
    • Analysis of manufacturing systems using simulation.
Intended learning outcomes On successful completion of this module a student should be able to:

1. Differentiate the applicability of different layout types applicable in manufacturing businesses.
2. Assess how production layout and system design influences productivity
3. Appraise the effectiveness of cellular configurations .
4. Design a graphical simulation model using an industry leading discrete-event simulation tool.
5. Contrast discrete-event simulation to other modelling techniques especially in addressing emerging manufacturing paradigms.
6. Devise an experimental procedure and interpret the consequential results of the simulation model.

Operations Management

Module Leader
  • John Patsavellas
Aim

    To introduce core factors of managing operations.


Syllabus
    • An introduction to manufacturing and service activities
    • Capacity, demand and load; identifying key capacity determinant; order-size mix problem; coping with changes in demand
    • Standard times, and how to calculate them; process analysis and supporting tools; process simplification
    • What quality is; standards and frameworks; quality tools; quality in the supply chain
    • Scheduling rules; scheduling and nested set-ups
    • Roles of inventory; dependent and independent demand; Economic Order Quantity; uncertain demand; inventory management systems and measures
    • Information systems – at operational, managerial, and strategic levels; bills of material; MRP, MPRll and ERP systems
    • Ohno’s 7 wastes; Just-in-Time systems (including the Toyota Production System, and Kanbans)
    • Class discussion of cases, exercises, and videos to support this syllabus
Intended learning outcomes On successful completion of this module a student should be able to:

1. Apply the ‘Framework for the Management of Operations’ to all operations, from pure service to pure manufacturing.
2. Identify the key capacity determinant in an operation, and carry out an analysis to develop the most appropriate approach in response to changes in demand.
3. Select and apply appropriate approaches and tools to determine standards and improve processes.
4. Determine the information needed to support businesses, in particular manufacturing operations.
5. Analyse problems rigorously to develop options, and select an appropriate option taking into consideration relevant factors such as risk, opportunities, cost, flexibility, and time to implement.
6. Select appropriate Just-in-Time (JIT) tools to improve operations.
7. Develop appropriate quality systems for the whole of their supply chain – from supplier, through operations to customers – and ensure these systems are sustained and a culture of continuous improvement prevails.

Cyber Thinking and Practice in Manufacturing

Module Leader
  • Jeremy Hilton
  • Lorraine Dodd
Aim
    To provide the necessary skills and knowledge that enable professionals working in cyber manufacturing contexts to adapt to continual change. (It focuses on investigative methods, systems thinking and anticipating futures with a view to problem solving in a real-world context. Security is one of the many complexities that need to be considered when comprehending the cyber domain.)

Syllabus
    Adapting to change in complex cyber environments
    • representing and navigating complexity
    • soft systems methodology
    • organisational dynamics and change
    • monitoring and adapting
    • anticipating future requirements
    • dealing with disruptive and novel technologies, events and emergent changes


Intended learning outcomes On successful completion of this module a student should be able to:

Knowledge
1. critically evaluate a range of approaches to understanding complex manufacturing environments
2. critically assess approaches to innovation in competitive problem spaces in manufacturing.
3. appraise the techniques that can be used to design investigation, problem formulation and structuring, and interpretation of data skills.
4. design methods to investigate security problems in the cyber manufacturing contexts.
5. conduct techniques for anticipating futures such as horizon scanning and scenario based planning of technology and threats to manufacturing environment.
6. analyse and scope a complex problem-space with a view to action and improvement for manufacturing.



Cybersecurity of Machine Tool Systems

Aim
    To enable students to Think critically about technology, provide solutions and gain best practices of cyber security issues relating to the machine tool systems.
Syllabus
    • Mobile security
    • Industrial network security
    • Cyber incident response and disaster recovery
    • Machine tool systems and monitoring
    • Safety and security of machine tool systems
    • Security of cyber physical systems
    • Cyber-attacks and measures in cyber-physical systems
    • Cyber risks in industry control systems & operations
    • Detect and prevent system intrusion
    • Costing security solutions
Intended learning outcomes

On successful completion of this module a student should be able to:

  1. Evaluate and master a toolset of the research, methods, practices technologies and recent advancements in security of machine tools systems.
  2. Create strategies for managing security threats along with vulnerabilities of cyber physical system and industrial controller.
  3. Analyse mobile security.
  4. Categorise incident response, intrusion and security breaches.
  5. Assess the cost of security solutions for machine tool systems.

Data Analytics for Cyberattack Detection

Aim
    To provide working knowledge of using different data mining techniques to identify cyber threats to a manufacturing system.
Syllabus
    • Concepts of data mining technology and performance matrix
    • Supervised learning techniques: Decision Trees, Neural Networks, Support Vector Machines (SVM)
    • Unsupervised/Semi-supervised learning: K-means clustering, K-nearest neighbour clustering, 1 class SVM
    • Metaheuristic and evolutionary algorithms
    • Feature engineering for cyber-attack detection
    • Spam detection and malware detection,
    • Intrusion detection and prevention systems
    • Graph-based cyber security modelling and attack graphs
    • Data mining technique toolboxes and functions in MatLab
Intended learning outcomes

On successful completion of this module a student should be able to:

  1. Appraise data mining techniques for cognitive cyber-security.
  2. Appraise machine learning techniques for cyberattack detections, including supervised learning and unsupervised learning, with appropriate feature extraction.
  3. Create and assess graph-based attack models for industry systems
  4. Appraise models using relevant toolboxes and functions in MatLab.
  5. Evaluate the performance of the developed models.

Hardware-Level Cyber Security

Aim

    To provide necessary skills and knowledge related to technologies underpinning hardware security, and to give a view that building security is started from hardware design. Students are enabled to understand the means of hardware attacks and vulnerabilities in electronics, ways to detect and mitigate.

Syllabus
    • Basics of electronics and electrical systems
    • Design, simulation, verification, and implementation of fellow
    • Life cycle test services
    • The vulnerability of electronics and physical attacks
    • Design intellectual property protection
    • Counterfeit electronics
    • Hardware Trojan detection and countermeasure
    • Physical Unclonable Functions (PUF) and electronic fingerprint
    • Side channel attack, countermeasure and analysis
    • Cybersecurity of embedded web server
    • Cryptography
    • Attack resilient hardware platforms
    • Hardware-based security services
    • Penetration test on hardware

Intended learning outcomes

On successful completion of this module students should be able to:

  1. Appraise the fundamental concepts of process variation, testability, electronic health management and security lifecycle test
  2. Evaluate vulnerability in electronics, type of threats, and attack classifications, and security lifecycle test
  3. Create strategies for attack detection, mitigation and countermeasure
  4. Design intellectual property protection
  5. Build a secure and attack-resilient hardware.

Industrial Cybersecurity Challenges and Strategies

Aim

    To provide an overview of modern manufacturing environment and associated engineering challenges in cybersecurity.


Syllabus
    • Introduction to Industrial Cybersecurity
    • Manufacturing and manufacturing systems
    • Manufacturing paradigms
    • Industry 4.0
    • Cybersecurity regulations, standards and compliance
    • Digital engineering for virtual product development
      • CAD (Computer-Aided Design) /CAM (Computer Aided Manufacturing) /CAE (Computer Aided Engineering) /CAPP(Computer Aided Process Planning)
      • PDM (Product Data Management)
      • PLM (Product Lifecycle Modelling)
    • Enterprise integration
      • ERP (Enterprise Resource Planning)
      • SCM (Supply Chain Management)
    • Factory automation: SCADA (Supervisory Control and Data Acquisition), Embedded systems, CPS ( Cyber-Physical System)
    • Typical cyberattacks and cybersecurity challenges
    • Cyber security of manufacturing systems
Intended learning outcomes

On successful completion of this module a student should be able to:

  1. Differentiate various manufacturing systems and paradigms.
  2. Appraise the roles and functions of information technologies for digital engineering including modelling methods and algorithms for PDM and PLM.
  3. Appraise the roles and functions of information technologies for enterprise integration including modelling methods and algorithms for ERP and SCM.
  4. Appraise the architecture of factory automation, including SCADA, embedded systems, and CPS.
  5. Assess the limitations and constraints of modern manufacturing systems, and the mitigation of cyber threats to manufacturing.

IoT Security and Systems

Aim

    To provide working knowledge on IoT technologies, including hardware and software options, and their innovation potential, and enable students to analyse alternative security options.



Syllabus
    • Introduction to IoT
    • The key concepts of Internet of Things and its enabling technologies
    • Key applications, protocols and architectures
    • IoT Physical Devices
    • IoT Connectivity and Industrial Internet
    • IoT Human-machine Interfaces
    • IoT System Design & Architectures
    • IoT System Management
    • IoT Business Models & Data Ownership
    • IoT Data Management and Analytics (Edge/Cloud data and Big Data)
    • IoT Enablers – from Design to Implementation
    • IoT Reliability, Privacy, Trust and Ethical issues
    • IoT security and integrity
    • Protecting IoT enabled manufacturing systems.
Intended learning outcomes

On successful completion of this module a student should be able to:

  1. Appraise the key concepts of Internet of Things, and inspect its enabling technologies.
  2. Evaluate use cases of theoretical concepts, including security implications.
  3. Assess recent and evolving developments, protocols and technologies for IoT enabled systems.
  4. Compose architecture designs synthesising technology components to address application requirements with security provisions.
  5. Evaluate Internet of Things – enabled systems regarding security risks, vulnerabilities, threats, risk metrics, and solutions.

Secure Cloud based Manufacturing

Aim

    To provide fundamental knowledge on cloud based manufacturing, security challenges and risks associated with different cloud deployment models along with technologies necessary to protect manufacturing systems.



Syllabus
    • Cloud Concepts and Technologies:
      • Virtualisation, Load balance, Scalability & Elastically, Deployment, Monitoring
      • Service defined networking, Network function virtualization
      • Cloud Trust Model

         

    • Cloud based Manufacturing
      • Architecture
      • Service and platforms: IaaS, PaaS, and SaaS

         

    • Secure Management of Cloud Infrastructure:
      • Virtual layer self-managed services,
      • application layer self-managed service, and
      • Security best practices for automated Cloud infrastructure management
      • Secure cloud technologies
      • Secure Cloud ERP

         

    • Big Data in cloud
    • Hadoop, MapReduce,NoSQL database
    • Big data storage, retrieval and analysis
    • Importing and exporting data

       

    • Data Protection:
    • Fault detection and isolation in the Cloud
    • Threats analysis and mitigation in the Cloud
    • Data privacy
    • Data protection
    • Secure data management

Intended learning outcomes

On successful completion of this module a student should be able to:

  1. Distinguish different types of cloud services, platforms, and cloud based manufacturing architecture.
  2. Appraise the availability of key technologies for cloud based manufacturing.
  3. Evaluate cybersecurity in cloud based manufacturing, and data protection.
  4. Demonstrate applications of big data storage, retrieval and analysis software.
  5. Create solutions to import and export big data sets from the cloud services.

 


Cranfield is very famous in industry which is what attracted me to this University, as well as the course content itself which covers most of the advanced knowledge in IOT, in Cyber-Security and in manufacturing. The aspect of the course which I find most enjoyable is the group project where we have the chance to implement our knowledge and put it into practice. It’s a wonderful course which I would definitely recommend.

I decided to study at Cranfield University because of the wide variety of advanced skills that it offers as well as the high academic standard and expertise of the academic staff on the course (according to the information contained on their background and activities on the University's website). I would definitely recommend the course to anyone who is looking to be a security expert in the manufacturing field.

 

 


Accreditation

Accreditation is being sought for the MSc in Cyber Secure Manufacting from the Insitution of Mechanical Engineers (IMechE), Instituion of Engineering & Technology (IET) and the Royal Aeronautical Society (RAes) on behalf of the Engineering Council as meeting the requirements for Further Learning for registration as a Chartered Engineer.  Candidates must hold a CEng accredited BEng/BSc (Hons) undergraduate first degree to comply with full CEng registration requirements.

Your career

Cranfield's applied approach and close links with industry mean 93% of our graduates find jobs relevant to their degree or go on to further study within six months of graduation. Our careers team support you while you are studying and following graduation with workshops, careers fairs, vacancy information and one-to-one support. 

On successful completion of this course graduates should have a diversity of job opportunities, mainly in the following job markets:

  • Manufacturing informatics
  • Manufacturing engineering
  • Cybersecurity
  • IoT (Internet of Things)
  • Cloud computing
  • Big data analysis.

How to apply

Online application form. UK students are normally expected to attend an interview and financial support is best discussed at this time. Overseas and EU students may be interviewed by telephone.