ACIT4050 Applied Computer and Network Security Course description

The aim of this course is to build further on the grounding of principles in the earlier security courses, covering both practical and theoretical aspects of cyber security. The course will give an in-depth insight into societal aspects of computer and network security, practical experience in penetration testing, and insights into relevant mechanisms of cyber defense. Real-life cases of security incidents will be discussed and then analyzed in depth by the students.

Language of Instruction: English

The student should have the following outcomes upon completing the course:

Knowledge

Upon successful completion of the course, the student has:

• thorough knowledge of cyber security
• advanced knowledge of mechanisms for cyber defense, and how they are used in practice
• a thorough understanding of societal aspects of cyber security
• a thorough understanding of the relation between security and privacy

Skills

Upon successful completion of the course, the student can:

• describe the main aspects of the relation between security and privacy
• describe central problems related to cyber security governance
• plan and describe the structure of cyber defense for an organization

General com​petence

Upon successful completion of the course, the student:

• understands the role of, and mechanisms that are used in penetration testing
• understands the role of, and mechanisms that are used for cyber defence
• can explain and discuss security challenges related to cyber security to experts and non-experts alike
• can explain and discuss societal aspects of cyber security with experts and non-experts alike.

This course features weekly lectures and workshops to provide both theoretical content and hands-on experience. Students work individually or in groups to complete assignments. The students supplement the lectures and workshops with their own reading. Compulsory assignments are given throughout the semester.

Specification for students’ laptop

It is recommended that students come with laptops that can support the lab sessions during lectures.

Laptops should have enough hard disk, memory and CPUs to run at least one Kali Linux and one Ubuntu or Windows virtual machines at the same time.

Minimum laptop specification

PC

4 cores CPU

8GB of RAM

100-120 GB of free hard disk space

MacBook

M1 or 4 cores CPU

8GB of RAM

100-120 GB of free hard disk space

In addition, students are expected to be familiar with Linux operating system. For example, they should be able to install packages and run basic Linux commands.

This course provides a foundation in Artificial Intelligence (AI) and focuses on a hands-on approach to the main Machine Learning (ML) methods used in data science and engineering, with special focus on robotics and control applications such as vision, navigation, task learning, fault diagnostics, condition monitoring.

The course aims to balance a good theoretical foundation with practical applications of ML to a selection of robotics and control related problems. Both Supervised, Unsupervised, and Reinforcement Learning are covered. Some of the main methods and algorithms for Regression, Classification, and Clustering are included. The principles of Artificial Neural Networks (ANN) and Deep Learning (DL) are covered in some detail, and some of the most commonly used NNs are used in problem solving. After covering the fundamentals of Reinforcement Learning (RL), the main RL methods are applied to example robotics and control problem solving. Genetic Algorithms and generative AI are briefly introduced. The course provides a foundation and practical skills for ML-based model development and problem solving that enables further knowledge and skill development. The curriculum shall be regularly updated in accordance with developments in this rapidly evolving field.

The course comprises two parts. The first part is a series of lecture seminars where after the presentation of each topic, the students work on hands-on exercises in class. The second part of the course is a practical project in groups.

Language of Instruction: English

No formal requirements over and above the admission requirements.

A student who has completed this course should have the following learning outcomes defined in terms of knowledge, skills and general competenc.

Knowledge:

The student:

• is familiar with the main principles in AI and has a practical understanding of the development and use of AI and ML
• has an understanding of the current application areas of AI and in particular for solving R&C problems
• has the theoretical and practical skills required to build simple ML models
• is familiar with Supervised-, Unsupervised-, and Reinforcement Learning ML methods.

Skills:

The student:

• can apply a variety of state-of-the-art ML methods in different robotics and control applications such as: machine vision, perception, inverse kinematics, navigation, reasoning, learning, fault detection and diagnostics, process control, condition monitoring, and many more.
• can evaluate the technical quality and practical value of various types of ML, and AI more general, for problem solving in robotics and control.

General competence:

The student:

• has both theoretical and practical understanding of ML methods
• can apply ML, and AI in general, to engineering problems.
• can discuss the relevance, strengths, and limitations of the different ML methods, and can mutually compare them to choose the appropriate method for the problem at hand.
• is able to solve real-world problems using ML and AI.
• can reflect on the practical, social, and ethical implications of AI in our society.

The first part of the course (nine weeks) comprises a series of whole-day lecture seminars. Students are expected to play an active role. Lecture seminars start with a lecture that introduces the topic and are followed by hands-on exercises in class.

The second part of the course (nine weeks) is a Robotics and Control project in groups of 1-3 students.

The course is completed by the students submitting a report and giving a presentation of their work.

The following required coursework must be approved before the student can take the exam:

One individual assignment consisting of 2-3 machine learning exercises using R.