module specification

The multidisciplinary intersection of biology and embedded digital security, the 'Bio-inspired AI and Security' module offers an immersive exploration of cutting-edge security algorithms inspired by the complexity and adaptability of biological systems. This module is designed to address the multifaceted challenges of cybersecurity through the lens of genetic algorithms, artificial immune systems, swarm intelligence, neural networks, biometric security, and memetic algorithms.

Students will engage with the foundational principles of natural science and engineering to devise solutions for complex cybersecurity problems with content that remains at the forefront of technological advancements. Through a dynamic blend of formal lectures/seminars and hands-on workshops, the curriculum not only imparts theoretical knowledge but also fosters practical skills in designing security solutions that harmonise societal, user, business, and customer needs, integrating considerations of health and safety, diversity, inclusion, and environmental stewardship.

Crucially, the module emphasises evaluating these bio-inspired solutions' environmental and societal impacts, aiming to minimise adverse consequences and promote sustainability. It adopts an inclusive approach to engineering practice, underlining the paramount importance of equality, diversity, and inclusion in fostering innovative solutions and advancing the field of bio-inspired AI and security.

CT5051: Advanced Electronics Systems (or equivalent) completed.

CT5003: Microprocessors and Embedded Systems (or equivalent) completed.

CT5055: AI for Robotics (or equivalent) completed.

Overview of bio-inspired algorithms and their origins in biological systems. (LO1)

Fundamental principles of genetic algorithms, artificial immune systems, and swarm intelligence. (LO1 and LO2)

Application of neural networks, biometric-based security, and memetic algorithms in enhancing digital security. (LO2)

Case studies highlight the use of bio-inspired algorithms to solve complex cybersecurity problems. (LO2)

Practical workshops on developing bio-inspired algorithms for encryption, intrusion detection, and network security. (LO2 and LO3)

Design principles for creating solutions that address societal, user, business, and customer needs. (LO3 and LO4)

Discussion on the ethical implications of using AI in security and evaluating the environmental and societal impacts of technology solutions. (LO3)

Exploration of the latest research and developments in bio-inspired AI and security through the discussion on the future potential of bio-inspired algorithms in addressing new and evolving security threats such as ISO/IEC 27001 Standard. (LO3 and LO4)

In the "Bio-inspired AI and Security" module, students will embark on a comprehensive learning journey, combining weekly lectures, tutorials, and supervised workshops with directed independent learning. This structured approach is designed to cultivate a deep understanding of bio-inspired security algorithms alongside practical investigative skills crucial for navigating the complexities of cybersecurity challenges.

Weekly lectures will lay the foundational knowledge of bio-inspired algorithms, utilising real-world examples and case studies to elucidate the basic principles underpinning this cutting-edge field. These sessions stimulate curiosity and foster a conducive environment for active learning. Tutorials will serve as an interactive platform, encouraging students to engage with the material on a deeper level, clarifying doubts, and reinforcing learning through discussion and problem-solving exercises. Central to the module, these workshops provide hands-on experience with electronic equipment and subsystems within a state-of-the-art laboratory setting. These sessions are pivotal for students to gain practical exposure to applying bio-inspired algorithms in security tasks.

Leveraging the University's Virtual Learning Environment (VLE), simulation tools, and laboratory equipment, students will access various resources to support their learning. This approach includes delivering content, fostering active learning, conducting assessments, and providing timely feedback. Students are encouraged to maintain reflective commentaries on their learning activities and tasks. This reflective practice enhances their understanding and appreciation of the coursework, promoting a deeper engagement with the subject matter. The module emphasises the importance of collaborative and individual work. Students are expected to work both independently and in groups to devise and implement solutions to workshop exercises and coursework. This collaborative effort aims to hone their teamwork skills and individual problem-solving capabilities.

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

LO1. Demonstrate an understanding of natural science and engineering principles and apply this knowledge to develop innovative solutions to complex cybersecurity challenges, incorporating bio-inspired algorithms.

LO2. Understand principles of genetic algorithms, artificial immune systems, swarm intelligence, neural networks, biometric security, and memetic algorithms to design effective security solutions. Critically assess these solutions in terms of their efficiency, reliability, and adaptability to dynamic security threats.

LO3. Evaluate the environmental and societal impact of bio-inspired security solutions, aiming to minimise adverse effects while promoting sustainable and responsible engineering practices.

LO4. Adopt an inclusive approach to problem-solving and design, recognising and addressing the benefits and importance of supporting equality, diversity, and inclusion in engineering. Understand and apply ethical considerations and industry standards in the development of cybersecurity solutions.

CT6066 Bio-inspired AI and Security | London Metropolitan University

Core text:

Sergei Petrenko, "Developing a Cybersecurity Immune System for Industry 4.0," in Developing a Cybersecurity Immune System for Industry 4.0 , River Publishers, 2020, pp.i-xlvi.

Lenau, Torben & Lakhtakia, Akhlesh. (2021). Biologically Inspired Design: A Primer. Synthesis Lectures on Engineering, Science, and Technology. 3. 1-115. 10.2200/S01064ED1V01Y202012EST014.

A Bio-Inspired Smart Security Model for Pervasive Smart Environment. (n.d.). (n.p.): Archers & Elevators Publishing House.

Other Texts:

Nature-Inspired Cyber Security and Resiliency: Fundamentals, Techniques and Applications. (2019). United Kingdom: Institution of Engineering and Technology.

National Defense University. (2010). Bio-Inspired Innovation and National Security. United States

Journals:

Y. Wang, W. Tang, Z. Yu, X. Qian, Z. Li and J. Qu, "Swimming Optimization Method of Soft Bio-Inspired Robotic Fish Based on Improved CPG Model," 2023 IEEE 19th International Conference on Automation Science and Engineering (CASE), Auckland, New Zealand, 2023, pp. 1-6, doi: 10.1109/CASE56687.2023.10260624.

S. J and M. Kowsigan, "Optimal Water Supply Scheduling Mechanisms using Bio-Inspired Algorithms for IoT-based Smart Water Distribution Network," 2022 International Conference on Augmented Intelligence and Sustainable Systems (ICAISS), Trichy, India, 2022, pp. 1082-1087, doi: 10.1109/ICAISS55157.2022.10011121.

M. Cheng, W. Zhu and Q. Ren, "Bio-inspired Control of a Rehabilitation Robot Actuated by Pneumatic Artificial Muscles," 2023 WRC Symposium on Advanced Robotics and Automation (WRC SARA), Beijing, China, 2023, pp. 103-108, doi: 10.1109/WRCSARA60131.2023.10261853.

Ahsan MM, Gupta KD, Nag AK, Poudyal S, Kouzani AZ, Mahmud MP. Applications and evaluations of bio-inspired approaches in cloud security: a review. IEEE Access. 2020;8:180799–814.

Johnson AP, Al-Aqrabi H, Hill R. Bio-inspired approaches to safety and security in iot-enabled cyber-physical systems. Sensors. 2020;20(3):844.

Fan X, Sayers W, Zhang S, Han Z, Ren L, Chizari H. Review and classification of bio-inspired algorithms and their applications. J Bionic Eng. 2020;17(3):611–31.

Mariyanayagam, D., Shukla, P., Virdee, B.S. (2022). Bio-Inspired Framework for Security in IoT Devices. In: Nagar, A.K., Jat, D.S., Marín-Raventós, G., Mishra, D.K. (eds) Intelligent Sustainable Systems. Lecture Notes in Networks and Systems, vol 333. Springer, Singapore.

Rauf U. A taxonomy of bio-inspired cyber security approaches: existing techniques and future directions. Arab J Sci Eng. 2018;43(12):6693–708.

Saleem K, Alabduljabbar GM, Alrowais N, Al-Muhtadi J, Imran M, Rodrigues JJ. Bio-inspired network security for 5G-enabled IoT applications. IEEE Access. 2020;8:229152–60.

Websites:

University Library website- https://student.londonmet.ac.uk/library/

Electronic Databases:

IEEE Xplore / IET Digital Library (IEL) - https://ieeexplore.ieee.org/Xplore/home.jsp

ACDM Digital Library - https://0-dl-acm-org.emu.londonmet.ac.uk/dl.cfm

Wiley Online Library - https://0-www-onlinelibrary-wiley-com.emu.londonmet.ac.uk/