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.


  • 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.


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 modules and (where applicable) some elective modules affiliated with this programme which ran in the academic year 2018–2019. There is no guarantee that these modules will run for 2019 entry. All modules are subject to change depending on your year of entry.

Course modules

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

Modern Manufacturing and Security Challenges

Module Leader
  • Professor Tetsuo Tomiyama

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

    • Manufacturing and manufacturing systems
    • Manufacturing paradigms
    • 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)
    • Types of cyber threats
    • Engineering aspects of the threats and system failures
    • Engineering and technological solutions against the threats
    Comparative study of the security solutions

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. Setup methods for factory automation, including SCADA, embedded systems, and CPS.
    5. Assess the limitations and constraints of the state-of-the-art digital engineering and factory automation.
    6. Assess the mitigation of cyber threats to manufacturing.

Secure IoT and System Architecture

Module Leader
  • Dr Christos Emmanouilidis

    To provide working knowledge on IoT hardware and software options, and enable students to analyse alternative secure architectures.

    • The key concepts of Internet of Things and its enabling technologies
    • Key applications, protocols and architectures
    • IoT Physical Devices and their Data Types
    • IoT Human-machine Interfaces
    • IoT System Design & Architectures
    • IoT System Management
    • IoT Business Models & Data Ownership
    • IoT Big Data & its Uses
    • IoT Enablers – How to Get to the IoT
    • IoT Reliability, Privacy, Trust and Ethical issues
    • IoT security
    • 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. Design and develop Internet of Things systems and applications.
3. Evaluate use cases of theoretical concepts.
4. Assess recent and evolving developments, protocols and technologies for IoT enabled systems.
5. Apply the cognitive, practical and key transferable skills necessary for IoT enabled applications and services with smart devices and machine-to-machine communications.
6. Create security metrics from the vulnerabilities, threats, risks and solutions for IoT enabled systems.
7. Examine alternative system architectures for secure IoT applications.

Secure Cloud Manufacturing

Module Leader
  • Dr Yifan Zhao
  • Dr Christopher Turner

    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.

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

      • Cloud 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

      • Security challenges related to data provenance:
      - 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. Appraise the availability of key technologies for Cloud Manufacturing.
2. Evaluate cybersecurity challenges in cloud manufacturing.
3. Distinguish different types of cloud services, platforms, and cloud manufacturing architecture.
4. Evaluate applications of big data storage, retrieval and analysis software.
5. Create solutions to import and export big data sets from the cloud services.
6. Manage data protection in Cloud Manufacturing.
7. Analyse and solve cybersecurity and system safety issues in cloud.
8. Propose security solution for cloud ERP (enterprise resource planning).

Data Mining Technology for Cyber Threat Identification

Module Leader
  • Dr Hongmei He
    To provide working knowledge of using different data mining techniques to identify cyber threats to a manufacturing system.

    • Concepts of data mining technology
    • Decision trees
    • Neural networks
    • Support vector machines
    • Clustering Algorithms
    • Metaheuristic and Genetic Algorithms
    • Social Network Analysis and Complex System
    • Spam detection
    • Trust/risk assessment
    • System abnormality analysis and attack detection
    • Multi-modality authentication
    • Data mining technology for threat identification for both legacy and new built systems and machines
    • 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 data-driving cyber-security.
2. Analyse security tolerance in complex systems.
3. Analyse abnormality from data sets produced by manufacturing systems.
4. Propose solutions for system authentication.
5. Develop data mining models for data-driven cybersecurity, particularly risk assessment, threat identification, and trust evaluation of products and services.
6. Implement the developed models with Data Mining Technique Toolboxes and functions in MatLab.
7. Evaluate the performance of the developed models.

Security of Machine Tool Systems

Module Leader
  • Dr Hongmei He
    To enable students to detect and prevent system intrusion, improve defence against targeted attacks and incident response, master modern technologies for security of machine tool systems and cyber-physical systems.

      • Security Landscape
      • Mobile security
      • Embedded system security
      • Detect and Prevent System Intrusion
      • Cyber Incident Response and Disaster Recovery
      • Machine tool systems
      • Cyber physical systems
      • Safety and security of cyber physical systems
      • Cyber-attacks and measures in cyber-physical systems
      • Cyber risks in industry control systems
      • Costing security solutions

Intended learning outcomes On successful completion of this module a student should be able to:
1. Compare the theory, technology and recent advancements in cyber security for cyber-physical systems.
2. Develop technical expertise in security of cyber-physical systems.
3. Analyse embedded systems and mobile security.
4. Create working knowledge on incident response to machine tool systems.
5. Categorise intrusion and security breaches to machine tool systems.
6. Propose security solutions for machine tool systems.
7. Assess the cost of security solutions for machine tool systems.

Cyber Thinking and Practice in Manufacturing

Module Leader
  • Jeremy Hilton
  • Lorraine Dodd
    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.)

    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:

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.

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 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.