ࡱ>  0qbjbj00 %ZgZgh+..$$$8\$ 0~|  e)*To*,///////$2@5b/*%)@e)**/ /+/+/+/*8  /+/*/+/+/+/ ~#?d)+2+/w//0 0+/5[-5+/5+/L**+/*****//y.*** 0****5*********.X :  Programme Details 1. Programme titleCybersecurity2. Programme codeCOMT933. QAA FHEQ levelF7 (Masters)4. FacultyEngineering5. DepartmentComputer Science6. Other departments providing credit bearing modules for the programmeNone7. Accrediting Professional or Statutory BodyWe will seek suitable endorsements from both the British Computer Society (BCS) and the National Computer Security Centre (NCSC).8. Date of production/revisionMay 2020 AwardsType of awardDuration9. Final awardMaster of Science (MSc)12 months10. Intermediate awards Postgraduate Diploma (PGDip)9 - 12 monthsPostgraduate Certificate (PGCert)5 - 12 months Programme Codes 11. JACS code(s) Select between one and three codes from the  HYPERLINK "https://www.hesa.ac.uk/support/documentation/jacs/jacs3-principal" \h HESA website. I120 Networks and CommunicationsI400 Artificial Intelligence12. HECoS code(s) Select between one and three codes from the  HYPERLINK "https://www.hesa.ac.uk/innovation/hecos" \h HECoS vocabulary.100376 computer and information security 100365 computer networks 100359 artificial intelligence  Programme Delivery 13. Mode of study Full-time 14. Mode of delivery Blended learning but with a strong emphasis on remote delivery and remote collaboration tools to take into account Covid-19 requirements. 15. Background to the programme and subject area Computer Science is the fundamental discipline of the information and communication age. Cybersecurity now permeates every aspect of life, ranging from business and medicine to science, engineering and the humanities, requiring skilled personnel to harness and exploit the growing power of computing devices, while at the same time protecting the security of the ever-increasing data flows generated on a day-to-day basis. The MSc in Cybersecurity is suited to graduates in computer science or related areas (as well as graduates with significant computer software or system experience) who wish to acquire industrially relevant skills in cybersecurity, supported by subsidiary development of skills in aspects of machine learning, while studying in a research-led teaching environment. The programme is designed to provide specialist skills and knowledge for students with a desire to work in, or further develop their abilities in industries that rely or will rely on cybersecurity, and will do so with some background in foundational machine learning technologies. The learning and teaching methods ensure a mix of working styles; students will work both independently and as part of a team, developing both their confidence and capability in making authoritative decisions. The nature of the programme encourages the development of a network of peers who share enthusiasm for the field, and who bring an understanding of the wider context and applicability of the subject in the wider world. The Departments Industrial Advisory Board plays an important role in advising the Department on its teaching provision, with particular emphasis on the suitability of its degree programmes as training and development for careers in computer science and software engineering. We anticipate that some of MSc graduates from this programme may also wish to pursue PhDs in cybersecurity and so the programme forms part of our overall strategy for research development. See the Department of Computer Science website:  HYPERLINK "http://www.shef.ac.uk/dcs" \h http://www.shef.ac.uk/dcs for more information. 16. Programme aims The MSc in Cybersecurity aims:A1To develop knowledge and understanding of fundamental and leading-edge topics in cybersecurity, enhanced by development of knowledge and understanding in important areas of machine learning and supporting technologies.A2To develop deeper expertise in a selected area of cybersecurity and provide experience of addressing a challenging research problem in that area.A3To provide immediately employable graduates with an industrially-relevant mix of knowledge, understanding, theoretical and practical problem solving skills, experience of team-working, and an appreciation of professional context (ethical, legal, social etc).A4To provide research-led training, thus providing a solid foundation for graduates to pursue a research degree or an industrial career in research and development, including an appreciation of professional context (ethical, legal, social etc).A5To immerse students in an academic environment that rewards innovation, fosters a sense of community and encourages students to direct their own learning. 17. Programme learning outcomes Knowledge and understanding On successful completion of the programme, students will be able to demonstrate knowledge and understanding of:Links to Aim(s)K1several advanced, research-led cybersecurity subject areas. Specifically, students will be able to demonstrate knowledge and understanding of: fundamental security properties and supporting technologies, malware and other threats, secure software development, commercially and industrially relevant architectures for security, and how security risks are managed.A1, A2, A3, A4K2fundamental machine learning technologies and algorithms that can support the solution of cybersecurity problems. Specifically, knowledge and understanding of: supervised learning and unsupervised learning algorithms, their advantages and disadvantages, and what resources/software libraries/computational infrastructures are available to support their use.A1, A3, A4 (A2, depending on project)K3a challenging cybersecurity topic (to a deep level). This will extend K1 by targeting a particular topic and develop significant knowledge and understanding of it. This knowledge and understanding is largely gained by the Independent study module (project). The specific topic must be in the area of cybersecurity but need not be restricted to topics taught directly in the programme.A1, A2, A3, A4, A5K4tools that can be used to support cybersecurity related software and system design and analysis tasks, together with other cybersecurity related tasks. These will include a variety of software and system design, analysis, verification, together with specialist forensic and network security analysis tools.A1, A2, A3, A4K5the ethical, legal, social and professional context in which cybersecurity researchers and practitioners work.A3, A4K6challenges and developments in practical cybersecurity.A3, A4, A5Skills and other attributes On successful completion of the programme, students will be able to:S1Identify and access a range of appropriate information resources relevant to addressing identified cybersecurity problems.A1, A2, A3, A4S2analyse available information and draw appropriate conclusions while upholding legal, professional and ethical standards.A1, A2, A3, A4S3synthesise theories, principles or designs and implementations pertinent to cybersecurity problem-solving and be able to evaluate and justify choices made when solving those problems. The justification will address contextual issues as well as technical ones.A2, A3, A4S4use relevant existing specialist demonstration, design, analysis and model building tools to solve cybersecurity problems.A1, A2, A3, A4S5develop computer software appropriately to solve identified cybersecurity problems. This requires the student to conceive, design, implement and test/verify such software with an appropriate level of rigour.A1, A2, A3, A4S6communicate cybersecurity concepts effectively in writing to both specialist and non-specialist audiences.A3, A4, A5S7communicate cybersecurity concepts effectively in an oral manner.A3, A4 A5S8work effectively in a team to complete set tasks, demonstrating personal responsibility, interpersonal communication skills, and planning to meet deadlines.A3, A4, A5S9demonstrate initiative, self-motivation, and self-organisation and be able to research important topics to an advanced level, fostered through the completion of an individual projectA2, A3, A4,A5 18. Learning and teaching methods Learning is student-centred, that is, the Department fosters an environment with many opportunities for individual and group learning, but the responsibility for learning rests with the student, who must be personally organised and self-motivated to make the most of the programme. Students are assigned to a personal tutor; they meet regularly to discuss progress and learning issues. Academic and technical advice may be sought from lecturers, teaching assistants and supporting staff (initially, via email). Teaching is offered through induction procedures, formal lectures, seminars, computer laboratories, problem-solving classes and project supervision. Induction procedures in which students are provided with an introduction pack and participate in tutorial sessions. Contents of the pack include the MSc Student Handbook, and a departmental map enabling students to familiarise themselves with the layout of the department and the main computing facilities. During intro week, students participate in orientation activities introducing them to the resources available via the departmental web site and local intranet. Learning outcomes K1, K2, and K4 and S1 are supported through this. Lectures are 50-minute formal presentations to a large class of students by a lecturer, who is responsible for the delivery of the module concerned. In many cases pre-recorded materials will be available and lecture delivery will be predominantly on-line. The purpose of a lecture is to motivate interest in a subject, to convey the core concepts and information content succinctly and to point students towards further sources of information. Lectures are interactive and students are encouraged to ask questions at suitable points. Students are expected to take notes during lectures, adding detail to published course materials. The learning outcomes K1, K2, and K5 together with S1 and S2 are supported through this mode. On-line materials. We aim to adopt a flipped classroom approach where appropriate, particularly for parts of the cybersecurity modules. As for lectures, the purpose is to motivate interest in a subject, to convey the core concepts and information content succinctly and to point students towards further sources of information. We will mix video, audio and slide content. Materials will generally be made available in small chunks, supported by on-line quizzes and problem sheets. Where a flipped classroom approach is adopted we will use problem classes, lab classes and seminars to support further understanding. The academic responsible for the module will also maintain a Q&A blog (with a general assumption that all discourse is public). Software tools relevant to the programme are also on-line. This supports K1, K2 and K4 in particular and also S1, S2 and S4 and S5. S6 and S7 are also supported via presentational packages and S8 via major software working packages (for meeting scheduling etc.) We envisage significant provision of links to externally freely available resources. Seminars are longer 90- to 110-minute informal presentations to a class of students by a lecturer, researcher, industrial partner or student, describing an area of their current research or business. There is typically more opportunity to structure the session internally with questions, problem solving and other kinds of interactive or shared learning experience, in which the students may also participate in the teaching. The learning outcomes K1, K2, and K4, K5, K6 (K5, K6 by an industrial seminar series) together with S1, S2, S3 and S7 (by examples of effective communication) are promoted through this mode. Computer laboratories are 50-minute or 110- minute sessions, supervised by teaching assistants (and sometimes attended by the responsible lecturer) in which students work at a computer, to learn and practise a specific practical skill such as computer programming or writing ML solutions (e.g.using ML packages). The learning outcomes K1, K2 and K4 and S1-S5 (all parts of general problem solving, including programming solutions) and S7 (students may give mini-presentations in labs too) are promoted through this mode. Problem-solving classes are 50-minute or 110- minute sessions, sessions conducted by a lecturer with a class of students, in which exercises are completed interactively and solutions are provided within the period.The purpose of such a class is to help students engage practically with material presented in lectures and start to apply this knowledge. The learning outcomes K1, K2, K5 and S1-S5 and S7 (some student mini-presentations on solutions may be given) are supported through this mode. Project supervision is a regular meeting held with an individual or group project supervisor, who may also be the students personal tutor. During the 20-50 minute session, students report on their progress to the supervisor, who highlights further areas of investigation, helps with technical problems, advises about the content and structure of technical reports and generally encourages the students to organise their time effectively. The learning outcomes K3, K5 and S1-S7 and S9 are supported through this mode (see projects below). But particular advice on written and oral communications (S6, S7) are supported. The transition from teaching to self-motivated learning is encouraged through specialist teaching materials such as lecture handouts or copies of lecture slides, which are typically supplied via the Department of Computer Science website. Set course texts and more general background materials are available through the University libraries, at bookshops and also via the Internet. Students are responsible for obtaining textbooks and printing any material downloaded over the Internet. Active learning is fostered and promoted through engagement in practical work, such as exercises, assignments and projects. Additionally, students are expected to undertake private study. Exercises are short tasks, either writing computer programs or working out solutions to other kinds of set problem, which are typically reviewed at the end of the session. Learning outcomes K1-K4 and S1-S3 may be supported this way. Assignments are offered over several weeks, typically involving the design and implementation of a software system to perform a given task, or the researching of a body of information leading to the writing of a discursive essay on a given topic. Learning outcomes K1, K2, K4 and K5, K6 and S1-S8 may be supported by this. (Note: some assignments are team assignments.) Projects are undertaken individually after the taught modules have been completed. Projects typically solve a larger problem, possibly for an industrial client. The project must have a research dimension. They will require and require good organisational and good presentation skills. The learning outcomes K3, K5 (others may be supported to some extent) and S1-S7 and S9 are promoted through this mode. Private study makes up more than half of the time allocated to each module. Students are expected to read around the topics of each module and follow directed reading from recommended course texts and online sources. Private study will include further investigations prior to exercises or projects and consolidates the lecture notes. This supports all K1-K6 LOs, S1-S7 and S9. It is possible that students may privately research how to work in teams, e.g. when reflecting on team working as part of an assignment, and so S8 may also be supported here. 19. Assessment and feedback methods Modules may be assessed by examination, by an individual project or team assignments, or by some combination of examination and a practical assignment. Learning outcomes K1-K5 and S2-S4 may be assessed by examination or coursework. Learning outcomes S4-S7 are assessed by individual and group project work. Examinations are (currently) typically 2-hour question papers. Examinations test the knowledge learning outcomes K1, K2, K4 and K5. They also provide evidence of practical skills S2 and S3. Assignments are pieces of assessed coursework, which students complete individually or in groups as directed. Assignments both develop and assess all skills S1-S9 (there are individual and team assignments) and support K1, K2, K4, K5 and K6. Note: due to Covid-19 we are likely to move away from formal examinations to more open assignments, including some short duration assignments (e.g. 24 hours to complete). A project is completed during the summer. Students select a topic, research the background literature, prepare a survey/analysis report at the interim assessment stage, and apply this knowledge in a practical, problem-solving project which is expected to contain some degree of original contribution. The final assessment stage is by dissertation and presentation (including a software demonstration, if appropriate), assessed independently by two examiners. A viva voce examination may be held to form a common view in cases of insufficient evidence or divergent opinions. The learning outcomes K3, K5 and S1-S7 and S9 are promoted through this mode. Others may be supported depending on the particular project. 20. Programme structure and student development The programme is offered over 12 months, starting in mid-September each year, and finishing the following September. The teaching year is divided into two semesters of 15 weeks, plus a 12-week project period during the summer. The first 12 weeks of each semester are devoted to teaching, with the remaining 3 weeks devoted to examinations. The programme is fully modular, delivered in multiples of 15 credits. Masters students study for 180 credits in a year (120 = 8 x 15 credits of taught modules and a 60-credit dissertation). The programme comprises five 15-credit core cybersecurity modules, a Research Methods and Professional Issues module, and two 15-credit machine learning modules as indicated below. Cybersecurity: COM6014 Fundamental Security Properties and Mechanisms (promoting K1, K3, K4) COM6015 Development of Secure Software (promoting K1, K3) COM6016 Cyber Threat Hunting and Digital Forensics (promoting K1, K2, K4) COM6017 Security of Control and Embedded Systems (promoting K1, K2, K4) COM6XXX Wider Aspects of Security (promoting K1, K2, K5, K6) General professional (but also acting as project preparation) COM6005 Research Methods and Professional Issues (promoting K3, K5) Fundamental Supporting Machine Learning Technologies: COM6509 Machine Learning and Adaptive Intelligence (promoting K2, K3); and COM6102 Scalable Machine Learning (promoting K2, K3) and a 60-credit project-based module: COM6013: Cybersecurity Dissertation Project (supporting K1, K2, K3, K4, K5, S1-S7, S9). All modules support S1-S5. S6 is developed by all modules, but particularly by the independent study module (i.e. project) and by team project assignments (e.g. in COM6017). S7 is developed via project presentation and also podcast development. Team skills (S8) are developed specifically via the Security of Control and Embedded Systems module (whose assessment is in part group based) , the new Wider Aspects of Security module, but also in part by other modules (e.g. Fundamental Security Properties and Mechanisms may involve lab work in pairs or larger groups). Students who obtain 180 credits in total are awarded the degree of MSc. Exit awards of PG Diploma and PG Certificate can also be awarded to students who have obtained the appropriate number of credits as stated in the University regulations. Students entering the programme will typically have a Computer Science background (or relevant industrial computing experience) and are expected to have A-level mathematics, but they are likely to be new to cybersecurity and data analytics. The modules ensure that all students have a common academic grounding, and emphasize fundamental principles, concepts and techniques. Student choice is served through the provision of a wide variety of possible project topics designed to enable students to design a programme in accordance with their developing interests and career aspirations. In addition, students choose one of two data analytic strands. Students development over the course of their study is measured through assessment of performance in each module, and across the programme as a whole, and monitored via regular meetings with a personal tutor who also offers pastoral care. Masters students are required to complete an individual research-based dissertation of 15,000 20,000 words. This enables students to apply appropriate research techniques to a real problem in cybersecurity, and to engage at an in-depth level with an area of the subject which is of particular interest to them. Students may develop their own dissertation topics, in consultation with staff, or select from a list of possible topics generated by academic staff and industrial clients. PG Diploma and PG Certificate students do not complete a dissertation project. PG Certificate students will have successfully completed fewer modules than MSc or PG Diploma students, and are therefore expected to have an appreciation of some learning outcomes (K1-K3) rather than a thorough understanding of them. The project enables candidates to achieve LOs to greater depth than afforded by the taught components. Specifically, K3 and K5 and S1-S7, S9 are promoted via the project. Other LOs may be developed depending on the particular project taken.Detailed information about the structure of programmes, regulations concerning assessment and progression and descriptions of individual modules are published in the University Calendar available online at  HYPERLINK "http://www.sheffield.ac.uk/calendar/" \h http://www.sheffield.ac.uk/calendar/.21. Criteria for admission to the programme Educated to degree level with 2.1 The student should possess either a degree in computer science or related topic OR have significant computing related experience. An ability to program is expected. An A level in mathematics (or equivalent) is required. 22. Reference points The learning outcomes have been developed to reflect the following points of reference: Subject Benchmark Statements  HYPERLINK "https://www.qaa.ac.uk/quality-code/subject-benchmark-statements" \h https://www.qaa.ac.uk/quality-code/subject-benchmark-statements Framework for Higher Education Qualifications (2014)  HYPERLINK "https://www.qaa.ac.uk/docs/qaa/quality-code/qualifications-frameworks.pdf" \h https://www.qaa.ac.uk/docs/qaa/quality-code/qualifications-frameworks.pdf University Strategic Plan  HYPERLINK "http://www.sheffield.ac.uk/strategicplan" \h http://www.sheffield.ac.uk/strategicplan Learning and Teaching Strategy (2016-21)  HYPERLINK "/polopoly_fs/1.661828!/file/FinalStrategy.pdf" \h /polopoly_fs/1.661828!/file/FinalStrategy.pdf The UK Engineering Council Standards and Routes to Registration (SARTOR) document, 3rd Edition, 1997; The draft UK Standards for Professional Engineering Competence (UK-SPEC), 2003;  HYPERLINK "http://www.bcs.org/upload/pdf/hea-guidelinesfull-2015.pdf" \h Accreditation requirements of the British Computer Society (BCS); The workload fits comfortably within the guidelines laid down by the University, of 10 hours per credit awarded, and is monitored by external examiners. 23. Additional information Relevant accreditation for this MSc programme will be sought from the British Computer Society (BCS) and from the National Computer Security Centre (NCSC). We fully expect this programme to gain BCS accreditation and it has been developed with this in mind. (This must be retrospective after a successful run.) All our MSc programmes seek accreditation by the BCS. It is our standard accreditation route. The proposed programme shifts emphasis of our current MSc Cybersecurity & AI programme from a 50-50 Cybersecurity-AI split to a 75-25 Cybersecurity-AI split. This will allow us to satisfy the requirements of the NCSCs MSc Cybersecurity accreditation criteria. We can apply for provisional NCSC MSc Cybersecurity accreditation for this programme and intend to do so. For home students in particular NCSC accreditation is likely to be very important. The NCSC is the national body for cybersecurity in the UK. They are part of GCHQ and exert significant influence in the whole of the cyber domain in the UK, issuing white papers and guidance, running research Centres of Excellence and running a taught programme accreditation scheme in support of the national security strategy. Cyber skills are perceived by the UK Government as critical. Absence of NCSC accreditation doesn't seem to affect overseas numbers but those in the UK may appreciate the benefits of having a certified MSc degree. Long-term, employment by HMG may require such accreditation. Also, running accredited teaching programs means we can be a candidate for being a Centre of Teaching Excellence when these are launched. (This is under consideration.) We occasionally get applicants asking whether we are certified by the NCSC. If further scholarships for cybersecurity MScs are promoted downstream then we need to offer accredited programmes to have a chance of getting them. There is a call out now for such accreditation. The programme (structure and draft syllabus) has been informed by accreditation criteria, and hence we are confident that the programme will be accredited in due course. We also anticipate strong external contributions (which is favourably considered by the accrediting authorities). The Department of Computer Science is housed in the modern, purpose-built Regent Court building and has its own dedicated computing facilities. The Department is internationally recognized for its teaching and research, and has particular established research strengths in the fields of natural language processing, speech technology, machine learning, robotics, visual computing and software verification and testing. It has recently (2017) established a Security of Advanced Systems Research Group, which now has five dedicated security academic staff, together with a teaching fellow to support cybersecurity teaching.  This specification represents a concise statement about the main features of the programme and should be considered alongside other sources of information provided by the teaching department(s) and the University. 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