This poster presents an ongoing study that takes a diverse and inclusive approach to designing a practical group work assessment for an undergraduate cybersecurity course delivered to third year university cohorts. Students were given the choice to either work individually or as part of a group to complete the assignment in virtual laboratories. The study evaluates how student grouping preferences change when the teaching structure adapts in response to the the Covid-19 pandemic and how grouping preference impacts upon academic performance. Students reflected positively on the assignment and demonstrated a preference for treating group information as private. No disputes regarding group marks or contributions from different group members arose despite the number of groups involved. Students have taken responsibility for their choices and have accepted the outcomes of their teamwork.

This paper presents a new Intrusion Detection System (IDS) using a 3 layer Convolutional Neural Network (CNN), capable of identifying malicious network traffic. We employ a new injective algorithm to encode network traffic without loss of information. We also include a new algorithm to decode, encoded RGB images back into network traffic. We evaluate the proposed IDS in terms of its computational complexity in for example: time, memory and CPU utilisation for the encoding and decoding algorithms, and its accuracy and loss during training and detection. Lastly, we compare the proposed IDS against a significant IDS algorithm that uses a different approach for encoding, decoding and CNN detection.

Service providers have now entered the implementation phase for 5G mobile telecommunication networks. With this, the concept of Multi-access Edge Computing (MEC) will play a crucial role when providing services on-the-go with low latency, high availability and high bandwidth. However, due to the low processing power of MEC nodes, adversaries may target the platform for malevolent purposes. In this paper we focus on building a realistic 5G-MEC testbed to run legitimate traffic and network attacks, and to collect 5G datasets for 5G-MEC. We also apply a Convolutional Neural Network to the dataset created on our testbed and to publicly available datasets. Our datasets and detection rate suggest that the employment of current public datasets for research based on 5G-MEC security, is now inappropriate.

The current trend in developing smart technology for the Internet of Things (IoT) has motivated a lot of research interest in optimizing data transmission or minimizing energy consumption, but with little evidence of proposals for achieving both objectives in a single model. Using the concept of game theory, we develop a new MAC protocol for IEEE 802.15.4 and IoT networks in which we formulate a novel expression for the players' utility function and establish a stable Nash equilibrium (NE) for the game. The proposed IEEE 802.15.4 MAC protocol is modeled as a smart game in which analytical expressions are derived for channel access probability, data transmission probability, and energy used. These analytical expressions are used in formulating an optimization problem (OP) that maximizes data transmission and minimizes energy consumption by nodes. The analysis and simulation results suggest that the proposed scheme is scalable and achieves better performance in terms of data transmission, energy-efficiency, and longevity, when compared with the default IEEE 802.15.4 access mechanism.

SIP DDoS attack is growing and has a real threat to crippling public communication infrastructure. The standard approach to building the defense is at or near the attack destination (i.e. victim's location). This approach is struggling to keep up with the growing volume and attack sophistication. To be better prepared for future attacks, the workload needs to be distributed, and the attack needs to be mitigated as close to the attack source as possible. This paper experiments with data plane programming (P4) and control plane programming of Ethernet switches to provide first-hop detection and mitigation capability for SIP INVITE DDoS attack at every switchport. This approach creates a distributed or source-based defense component which could be added to the existing destination-based components to create a more comprehensive overall solution that is extensible, economical, and scalable against SIP DDoS attack of the future.

The Session Initiation Protocol (SIP) is an application-layer control protocol used to establish and terminate calls that are deployed globally. A flood of SIP INVITE packets sent by an attacker causes a Telephony Denial of Service (TDoS) incident, during which legitimate users are unable to use telephony services. Legacy TDoS defense is typically implemented as network appliances and not sufficiently deployed to enable early detection. To make TDoS defense more widely deployed and yet affordable, this paper presents TDoSD@DP where TDoS detection and mitigation is programmed at the data plane so that it can be enabled on every switch port and therefore serves as distributed SIP sensors. With this approach, the damage is isolated at a particular switch and bandwidth saved by not sending attack packets further upstream. Experiments have been performed to track the SIP state machine and to limit the number of active SIP session per port. The results show that TDoSD@DP was able to detect and mitigate ongoing INVITE flood attack, protecting the SIP server, and limiting the damage to a local switch. Bringing the TDoS defense function to the data plane provides a novel data plane application that operates at the SIP protocol and a novel approach for TDoS defense implementation.

The vulnerability of wireless networks to selfish and misbehaving nodes is a well known problem. The Tit-For-Tat (TFT) strategy has been proposed as a game theoretic solution to the problem, however the TFT suffers from a deadlock vulnerability. We present a modified TFT algorithm, the Resilient Tit-For-Tat (RTFT) algorithm in which we introduce the concept of alternative strategies to complement the default strategy. This combination enables us to model a non-cooperative game in which nodes are able change their strategies in order to maximize their utilities in selfish and misbehaviour scenarios. We demonstrate the viability of our proposal with simulation results.