Novel modeling and optimization for joint Cybersecurity-vs-QoS Intrusion Detection Mechanisms in 5G networks

The rapid emergence of 5G technology brings new cybersecurity challenges that hold significant implications for our economy, society, and environment. Among these challenges, ensuring the effectiveness of Intrusion Detection Mechanisms (IDMs) in monitoring networks and detecting 5G-related cyberattacks is of utmost importance. However, optimizing cybersecurity levels and selecting appropriate IDMs remain as critical and ongoing challenges. This work considers multiple pre-deployed distributed Security Agents (SAs) across the network, each capable of running various IDMs, where they differ by their effectiveness in detecting the attacks (referred to as security term) and the consumption of resources (referred to as Quality of Service (QoS) costs). We formulate a joint security and QoS utility function leveraging the Cobb–Douglas production utility function. There are several parameters that impact the joint objective problem, including the set of elasticity parameters, that reflect the importance of the two objectives. We derive an optimal set of elasticity parameters in closed form to identify the balancing point where both objectives have equal utility values. Through comprehensive simulations, we demonstrate that increasing the detection level of SAs enhances the security utility while simultaneously diminishing the QoS utility, as more computational, bandwidth, and monetary resources are utilized for IDM processing. After optimization, our mechanism can strike an effective balance between cybersecurity and QoS overhead while demonstrating the importance of different parameters in the joint problem

Intrusion Response Systems for the 5G Networks and Beyond: A New Joint Security-vs-QoS Optimization Approach

Network connectivity exposes the network infrastructure and assets to vulnerabilities that attackers can exploit. Protecting network assets against attacks requires the application of security countermeasures. Nevertheless, employing countermeasures incurs costs, such as monetary costs, along with time and energy to prepare and deploy the countermeasures. Thus, an Intrusion Response System (IRS) shall consider security and QoS costs when dynamically selecting the countermeasures to address the detected attacks. This has motivated us to formulate a joint Security-vs-QoS optimization problem to select the best countermeasures in an IRS. The problem is then transformed into a matching game-theoretical model. Considering the monetary costs and attack coverage constraints, we first derive the theoretical upper bound for the problem and later propose stable matching-based solutions to address the trade-off. The performance of the proposed solution, considering different settings, is validated over a series of simulations

Three-dimensional Access Point Assignment in Hybrid VLC, mmWave and WiFi Wireless Access Networks:2020 IEEE International Conference on Communications, ICC 2020

To improve data speed and reliability, hybrid wireless networks combine two different Radio Access Technologies (RATs), such as Visible Light Communications (VLC), millimetre wave (mmWave), Wireless Fidelity (WiFi), 4G Long Term Evolution (LTE), etc. The Internet of Radio Light (IoRL) is a cutting-edge system paradigm to combine three RATs for taking advantage the vast VLC and mmWave spectrum with the ubiquitous coverage of WiFi. In this respect, this work introduces a new convex optimisation-based solution method to optimise the three-dimensional (3D) Access Point Assignment (APA) problem of the IoRL system under individual user positioning, priority and minimum Quality-of-Service (QoS) constraints. We use both the IoRL real-world testbed and large-scale Maltab simulations to evaluate that our solution converges in linear time, and attains higher throughput-vs-fairness trade-off than existing efforts. © 2020 IEEE

A Novel Game-Theoretic Cross-Layer Design for OFDMA Broadband Wireless Networks

This paper proposes a novel game-theoretic cross-layer design for orthogonal frequency division multiple access (OFDMA) wireless networks, which operates optimal subcarrier, power and rate allocation. Based on the Nash bargaining solution (NBS) and coalitions, the proposed scheme not only maximizes the system's effective data rate but also supports proportional fairness among the users by considering the heterogeneity of their requirements, as well as the rate outage due to imperfect channel state information (CSI) available at the transmitter (CSIT). The simulation results confirm that the proposed scheme achieves an optimum tradeoff between effective data rate and proportional fairness, while it guarantees the quality of service (QoS) requirements, and outperforms the existing solutions in terms of power consumption, resilience to CSIT errors and stability

Cross-layer Design for Multiuser OFDMA Systems with Cooperative Game and MMPP Queuing Considerations

In this paper, a queue aware game theoretic cross-layer scheme is introduced for multiuser orthogonal frequency division multiple access (OFDMA) wireless systems. A joint scheduling process that combines an adaptive modulation (AM) at the physical layer (PHYL) along with a discrete Markov modulated Poisson process (dMMPP) at the medium access control (MAC) layer, is devised. Moreover, relied on this cooperative game theoretic architecture, we formulate an optimization problem on a Nash bargaining solution (NBS) basis. Finally, the allocation policies result an optimal tradeoff between proportional fairness and throughput maximization that fits in realistic wireless networks scenarios

Novel modeling and optimization for joint Cybersecurity-vs-QoS Intrusion Detection Mechanisms in 5G networks

The rapid emergence of 5G technology brings new cybersecurity challenges that hold significant implications for our economy, society, and environment. Among these challenges, ensuring the effectiveness of Intrusion Detection Mechanisms (IDMs) in monitoring networks and detecting 5G-related cyberattacks is of utmost importance. However, optimizing cybersecurity levels and selecting appropriate IDMs remain as critical and ongoing challenges. This work considers multiple pre-deployed distributed Security Agents (SAs) across the network, each capable of running various IDMs, where they differ by their effectiveness in detecting the attacks (referred to as security term) and the consumption of resources (referred to as Quality of Service (QoS) costs). We formulate a joint security and QoS utility function leveraging the Cobb–Douglas production utility function. There are several parameters that impact the joint objective problem, including the set of elasticity parameters, that reflect the importance of the two objectives. We derive an optimal set of elasticity parameters in closed form to identify the balancing point where both objectives have equal utility values. Through comprehensive simulations, we demonstrate that increasing the detection level of SAs enhances the security utility while simultaneously diminishing the QoS utility, as more computational, bandwidth, and monetary resources are utilized for IDM processing. After optimization, our mechanism can strike an effective balance between cybersecurity and QoS overhead while demonstrating the importance of different parameters in the joint problem