The aim of this module is to develop a deep understanding and provide with hands-on implementation experience of network security concepts. A focus has been made on discussing networking fundamentals and implementation in first half of the module. Considerable content in second part of the module has been focused on Artificial Intelligence (AI) and Machine Learning (ML)-based solutions in the cybersecurity domain. Developing exemplar AI models in practical for achieving security measures allow students to professionally design, implement, and analyse AI-based cybersecurity strategies. An outline of the main areas includes:• Introduction to network security and challenges: It focuses on discussing various network threats and attacks that can compromise the network security. Discussion then diverts to network defence strategies, for example, perimeter and defence-in-depth.• Access Control and Authentication: This lecture focuses on introducing and discussing various types of traditional centre of gravity of computer security, the “Access Control”. The list of concepts in this lecture covers trust and identity, attacks, models – access control models, network device access control, AAA, Layer 2, device hardening.• Firewalls: In this part of module, a discussion around different types and existing technologies of firewall is presented. Students get an opportunity to implement and deploy the concepts, such as host-based or network-based firewall, static packet filtering, stateful packet filtering, and multilayer firewall in the lab environment. • Fundamentals of Cryptography and Remote Access VPN: Fundamentals of encryption, decryption, and authentication are touched before diving deeper in developing remote access and Virtual Private Networks (VPNs) for network security, while also covering types (L2, L3 and L4/5) and technologies (IPSec and SSL).• Artificial Intelligence and Machine Learning: Discussion here covers introduction to various learning techniques including supervised, unsupervised and reinforcement learning in terms of various application domains, i.e., classification, regression and clustering.• Introduction to machine learning models: Various AI models are introduced and implemented including neural network, linear regression, k-means clustering, support vector machine, random forest, decision tree, deep neural network.• Data-Driven Cybersecurity: Intrusion Detection Systems (IDS) vs Intrusion Detection and Prevention Systems (IDPS) are introduced in terms of types, alert monitoring and sensor tuning; behavioural analysis, in-line vs out-of-line IDS/IDPS. Practical provides an opportunity to applying AI techniques to real-world intrusion detection problems. • Relevant state-of-the-art research in the domain: Discussions around recent research advances in the fields of network security and cybersecurity.