The emergence of beyond-fifth-generation (B5G) networks and the increasing demand for Internet of Things (IoT) requires deploying a cloud-edge-terminal computing network with the Software Defined Network as the controller. However, this network is vulnerable to various threats, notably SYN flood attacks. This paper adopts queuing theory and game theory to explore Mobile Edge Computing (MEC) attack and defense interaction in different IoT businesses. Moreover, we propose a utility model of the packet flow in MEC networks featuring the delay and packet loss rate in the SYN flood attacks. For the attacker and defender’s strategy, we use game theory to model the interaction between strategy and resource allocation. A search algorithm analyzing MEC cell impact on strategy selection is developed, and we investigate the impact of the attacker’s possession of prior knowledge versus lack thereof regarding MEC cell characteristics under SYN flood attacks. The proposed game models are solved, and the results show that under the defender’s strategy, the attacker has no chance to launch SYN flood attacks under the defender’s defense cost of four times MEC computing resources; the cost of defense resources is lower than other related schemes.

Intelligent Reflecting Surface (IRS) assisted challenge-response physical layer authentication (CR-PLA) offers a promising solution for enhancing the security and stability of authentication channels between base stations and legitimate users in wireless environments with obstacles and eavesdroppers. However, there is a tradeoff between communication efficiency and security, with improved security often reducing efficiency. To address this, we propose a power-budget IRS-assisted CR-PLA scheme that balances these competing factors. Our approach involves generating multiple optimal IRS configurations within transmit power budgets and applying them to the authentication channel. We enhance both efficiency and security by randomly employing IRS configurations used for channel reconstruction as a pre-shared key. Experiments demonstrate that our scheme achieves high authentication efficiency and security under power budget constraints. Notably, even if the eavesdropper knows the secret channel configurations in man-in-the-middle attack scenarios, our scheme reduces their authentication success rate by 15.7% to 34.1%, rendering the attack ineffective.

The recent surge in large-scale Distributed Denial-of-Service (DDoS) attacks, primarily driven by Internet of Things (IoT) botnets, has drawn significant concerns. Defending against IoT botnet DDoS attacks is challenging, especially as attackers’ techniques and strategies become increasingly sophisticated. We propose a novel traffic-level differential game model from holistic and dynamic perspectives to address these challenges, integrating both the botnet formation and DDoS attack stages. Based on the actual operation mechanism of IoT botnet DDoS attacks, we extract two key attack features: 1) the incubation period of infected devices and 2) the threshold effects of DDoS attacks. Considering these, the proposed model more effectively captures the dynamic strategic behaviors of attackers and defenders. Using optimal control theory, we derive the optimal timing and frequency for attackers to activate latent devices and for defenders to restore compromised ones. Numerical simulations show the effectiveness of our defense strategy under both traditional and intelligent attack scenarios, reducing the payoff function of the attack-defense system by approximately 48% compared to static and adaptive strategies. Additionally, we examine the impact of key parameters on network security, providing valuable insights for developing effective defense strategies against botnet-driven DDoS attacks.

The proliferation of Distributed Denial-of-Service (DDoS) attacks in the Internet of Things (IoT) and the emergence of variants such as Distributed Reflection Denial-of-Service (DRDoS) attacks severely threaten the fifth-generation (5G) networks. More importantly, as attack methods continue to escalate, attackers can obtain deployed critical defense strategies through continuous probing, increasing the defense difficulty. We propose a hybrid strategy model and corresponding Software Defined Network (SDN) paradigm-based framework based on existing defense strategies to deploy it flexibly and conveniently into the 3GPP-defined 5G architecture. In addition, after quantifying the proposed hybrid strategy's defense capacity, the Stackelberg game is employed to model the hybrid strategy and solve the optimal packet sampling rate. The simulation shows that the optimal packet sampling rate is effective and robust and can force a rational attacker to give up DRDoS attacks and achieve the effect of subduing the enemy without fighting.

Network slicing is an emerging architecture that allows multiple virtual networks to be created on top of a shared physical infrastructure, each tailored to a specific type of service or application. Managing network slices using a Software Defined Network (SDN) controller makes the network more scalable and manageable but also vulnerable due to the centralized control of SDN. Low-rate denial-of-service (LDoS) is periodic and stealthy, and its attack frequency is lower than that of ordinary distributed denial-of-service attacks, making it one of the most severe threats on SDN. To cope with the above challenges, we propose a real-time Lightweight LDoS Detection and Mitigation (L2DM) framework. Moreover, an Adaptive Weight XGBoost (AW-XGBoost) algorithm is designed to extract features and obtain the detection model via adjusting adaptive weights. We also leverage the architecture of network slicing to build honeypot slices to gather information from attackers. Experimental results show that the proposed framework and corresponding algorithm can be deployed on SDN controllers to achieve LDoS attack detection and mitigation at low cost with high accuracy and effectiveness.

The emergence of fifth-generation (5G) networks and heterogeneous Internet of Things (IoT) demand requires deploying a Cloud-Edge-Terminal Computing distributed network with Software Defined Network (SDN) as the controller. However, this network faces threats, notably Denial-of-Service (DoS) attacks. This paper adopts game theory to explore Mobile Edge Computing (MEC) attack and defense interaction in diverse businesses. Moreover, we propose a three-layer 5G distributed Cloud-Edge- Terminal network featuring an attack-defense game model between DoS attackers and SDN defenders. For the defender's strategy, we use the Eisenberg-Gale (G-E) resource allocation model considering delay, packet loss rate, utility functions, values, sensitivities, and busyness. Heuristic search algorithms analyzing MEC cell impact on strategy selection are developed. The proposed game models are solved, and results are validated through simulations.

Many interconnected terminals in the Internet of Things (IoT) networks raise significant security risks. From a competitive perspective, the many heterogeneous nodes, including various wireless terminals, access points, and base stations are the competing targets between the defenders and potential attackers. This article proposes a security competition model based on the game theory to address the security competition problem. The model has two players, a defender and an attacker, who allocate resources to each IoT node according to their strategies. A novel metric, security entropy, derived from the security probability, quantifies each node’s security status. Based on the node heterogeneity, the overall security performance of the considered IoT network is evaluated with a topology-determined weighted security entropy. The defender and the attacker, respectively, aim to decrease and increase the weighted security entropy while balancing the cost, which constitutes their utility functions. The existence of the unique Nash equilibrium is proved. A best response selection algorithm for the optimal solutions is designed. The experimental results demonstrate that the proposed model effectively represents the goal orientation and interaction between the attackers and defenders in various scenarios. Additionally, increasing the cost for the attackers significantly reduces their resource allocation, especially to the attackers leading to a decrease in the system’s security entropy.

The power grid is rapidly transforming, and while recent grid innovations increased the utilization of advanced control methods, the next-generation grid demands technologies that enable the integration of distributed energy resources (DERs)- and consumers that both seamlessly buy and sell electricity. This paper develops an optimization model and blockchainbased architecture to manage the operation of crowdsourced energy systems (CESs), with peer-to-peer (P2P) energy trading transactions (ETTs). An operational model of CESs in distribution networks is presented considering various types of ETT and crowdsourcees. Then, a two-phase operation algorithm is presented: Phase I focuses on the day-ahead scheduling of generation and controllable DERs, whereas Phase II is developed for hour-ahead or real-time operation of distribution networks. The developed approach supports seamless P2P energy trading between individual prosumers and/or the utility. The presented operational model can also be used to operate islanded microgrids. The CES framework and the operation algorithm are then prototyped through an efficient blockchain implementation, namely, the IBM Hyperledger Fabric. This implementation allows the system operator to manage the network users to seamlessly trade energy. Case studies and prototype illustration are provided.