ARTIFICIAL INTELLIGENCE FOR CYBERSECURITY

SHE Level 4
SCQF Credit Points 20.00
ECTS Credit Points 10.00
Module Code MHH126582
Module Leader Hadi Larijani
School School of Computing, Engineering and Built Environment
Subject Cyber Security and Networks
Trimester
  • A (September start)

Summary of Content

The module is designed to equip the student with modelling skills that will develop the ability to analyse and review complex networking systems and understand the theoretical underpinning of simulation and gain competency in the use of a simulation package. Today's organizations spend billions of dollars globally on cybersecurity. Artificial intelligence has emerged as a great solution for building smarter and safer security systems that allow you to predict and detect suspicious network activity, such as phishing or unauthorized intrusions. This cybersecurity module presents and demonstrates popular and successful AI approaches and models that you can adapt to detect potential attacks and protect your corporate systems. You'll learn about the role of machine learning and neural networks, as well as deep learning in cybersecurity, and you'll also learn how you can infuse AI capabilities into building smart defensive mechanisms. As you advance, you'll be able to apply these strategies across a variety of applications, including spam filters, network intrusion detection, botnet detection, and secure authentication. By the end of this module, you'll be ready to develop intelligent systems that can detect unusual and suspicious patterns and attacks, thereby developing strong network security defences using AI.

Syllabus

Introduction to AI for Cybersecurity Professionals -360b7 Applying AI in cybersecurity b7 The evolution from expert systems to data mining and AI b7 The different forms of automated learning b7 Types of machine learning b7 The characteristics of algorithm training and optimization b7 Beginning with AI via Jupyter Notebooks b7 Introducing AI in the context of cybersecurity b7 Ham or Spam? Detecting Email Cybersecurity Threats with AI b7 Network Anomaly Detection with AI b7 Getting to know Python for AI and cybersecurity b7 Fraud Prevention with Cloud AI Solutions b7 Evaluating Algorithms b7 Case studies in SIEMs and AI - QRadar and Logrhythm

Learning Outcomes

On successful completion of this module, the student should be able to:1 - Identify and predict security threats using artificial intelligence2 - Develop intelligent systems that can detect unusual and suspicious patterns and attacks3 - Critically assess the effectiveness of your AI cybersecurity algorithms and tools#4 - Predict network intrusions and detect anomalies with machine learning5 - Verify the strength of biometric authentication procedures with deep learning6 - Critically Evaluate cybersecurity strategies and learn how you can improve them

Teaching / Learning Strategy

The course will be presented as a programme of blended lectures supported by tutorials and associated practical work. Students are directed to read appropriate texts and articles to consolidate their knowledge of the topics covered. Materials will be made available for students who are taking the programme through Distance or Flexible learning and to improve accessibility in accordance with the University's Strategy for Learning 2015-2020. Learning and teaching will take place through a variety of mechanisms, including lectures and practical sessions, research into current developments and issues, and case studies. This module emphasises an active "hands-on" approach to learning. Case studies will be used formatively in tutorials throughout the module in order to promote the application of knowledge to specific problems and to facilitate discussion. Suitable software has been identified i.e .SAS, Matlab, and blended learning labs can be developed.

Indicative Reading

Books and articles: AI in Cybersecurity, Editors Leslie F. Sikos, Springer 2018 Hands-On Artificial Intelligence for Cybersecurity: Implement smart AI systems for preventing cyber -attacks and detecting threats and network anomalies Paperback - 2 Aug 2019 Machine Learning and Security Paperback - 16 Feb 2018 - by Clarence Chio (Author), David Freeman Deep Learning (Adaptive Computation and Machine Learning Series) Hardcover - 3 Jan 2017 by Ian Goodfellow (Author), Yoshua Bengio (Author), Aaron Courville T. T. T. Nguyen and G. Armitage, "A survey of techniques for internet traffic classification using machine learning," IEEE Communications Surveys Tutorials, vol. 10, no. 4, pp. 56-76, Fourth 2008. R. Sommer and V. Paxson, "Outside the closed world: On using machine learning for network intrusion detection," in 2010 IEEE symposium on security and privacy. IEEE, 2010, pp. 305-316. Online sources: Matlab: https://uk.mathworks.com/?s_tid=gn_logo SAS: https://www.sas.com/en_gb/home.html Computer Networks: A Systems Approach, Fourth Edition, L. Peterson and B. Davie, The Morgan Kaufmann Series in Networking, ISBN 1-55860-368-9 Multimedia over IP and Wireless Networks: Compression, Networking, and Systems, M. van der Schaar and P. Chou, Elsevier, ISBN: 978-0-12-088480-3 The Practical OPNET Users Guide for Computer Network Simulation. Sethi, A.S. & Hnatyshin, V.Y., CRS Press, 2012. Multimedia Networking: From Theory to Practice. Hwang, J.N. Cambridge University Press, 2009.

Transferrable Skills

By the end of this module students will have gained competence in the following key areas: D1 Specialist knowledge and application D2 Critical thinking and problem solving D4 Communication skills, written, oral and listening D7 Computer literacy D8 Self-confidence, self-discipline & self-reliance (independent working) D9 Awareness of strengths and weaknesses D15 Ability to prioritise tasks and time management D16 Interpersonal skills, team working and leadership

Module Structure

Activity Total Hours
Seminars (FT) 24.00
Assessment (FT) 18.00
Independent Learning (FT) 146.00
Lectures (FT) 12.00

Assessment Methods

Component Duration Weighting Threshold Description
Exam (Dept) 02 2.00 50.00 n/a Unseen online exam.
Exam (Dept) 01 2.00 50.00 n/a Lab-based exam anomaly identification on data set.