Autonomous Vehicles (AVs) represent a transformative advancement in the transportation industry. These vehicles have sophisticated sensors, advanced algorithms, and powerful computing systems that allow them to navigate and operate without direct human intervention. However, AVs’ systems still get overwhelmed when they encounter a complex dynamic change in the environment resulting from an accident or a roadblock for maintenance. The advanced features of Sixth Generation (6G) technology are set to offer strong support to AVs, enabling real-time data exchange and management of complex driving maneuvers. This paper proposes a Multi-Agent Reinforcement Learning (MARL) framework to improve AVs’ decision-making in dynamic and complex Intelligent Transportation Systems (ITS) utilizing 6G-V2X communication. The primary objective is to enable AVs to avoid roadblocks efficiently by changing lanes while maintaining optimal traffic flow and maximizing the mean harmonic speed. To ensure realistic operations, key constraints such as minimum vehicle speed, roadblock count, and lane change frequency are integrated. We train and test the proposed MARL model with two traffic simulation scenarios using the SUMO and TraCI interface. Through extensive simulations, we demonstrate that the proposed model adapts to various traffic conditions and achieves efficient and robust traffic flow management. Specifically, the proposed approach results in a harmonic mean speed increase of up to 15% and a reduction in lane-change frequency by 10%. The trained model effectively navigates dynamic roadblocks, promoting improved traffic efficiency in AV operations with more than 70% efficiency over other benchmark solutions.

Securing Next Generation Networks (NGNs) remains a prominent topic of discussion in academia and industries alike, driven by the rapid evolution of cyber attacks. As these attacks become increasingly complex and dynamic, it is crucial to develop sophisticated security strategies with automated dynamic policy enforcement. In this paper, we propose a security strategy based on the zero-trust model, incorporating dynamic policy decisions through the utilization of stochastic games and Reinforcement Learning (RL). Our approach involves the development of an attack and defense strategy evolution model, specifically tailored to combat cyber attacks in NGNs. To achieve this, we employ RL techniques to update and adapt dynamic policies. To train the agents, we utilize the Generalized Proximal Policy Optimization with sample reuse (GePPO) algorithm, including its modified version, GePPO-ML, which incorporates meta-learning to initialize the agent’s policy and parameters. Additionally, we employ the Sample Dropout PPO with meta-learning (SDPPO-ML), a modified version of the SD-PPO algorithm, to train the agents. To evaluate the performance of these algorithms, we conduct a comparative analysis against the REINFORCE and PPO algorithms. The results illustrate the superior performance of both GePPO-ML and SDPPO-ML when compared to these baseline algorithms, with GePPO-ML exhibiting the best performance.

Autonomous Vehicles (AVs), furnished with sensors capable of capturing essential vehicle dynamics such as speed, acceleration, and precise location, possess the capacity to execute intelligent maneuvers, including lane changes, in anticipation of approaching roadblocks. Nevertheless, the sheer volume of sensory data and the processing necessary to derive informed decisions can often overwhelm the vehicles, rendering them unable to handle the task independently. Consequently, a common approach in traffic scenarios involves transmitting the data to servers for processing, a practice that introduces challenges, particularly in situations demanding real-time processing. In response to this challenge, we present a novel DL-based semantic traffic control system that entrusts semantic encoding responsibilities to the vehicles themselves. This system processes driving decisions obtained from a Reinforcement Learning (RL) agent, streamlining the decision-making process. Specifically, our framework envisions scenarios where abrupt roadblocks materialize due to factors such as road maintenance, accidents, or vehicle repairs, necessitating vehicles to make determinations concerning lane-keeping or lane-changing actions to navigate past these obstacles. To formulate this scenario mathematically, we employ a Markov Decision Process (MDP) and harness the Deep Q Learning (DQN) algorithm to unearth viable solutions.

To ensure uninterrupted access to critical VM applications, a robust VM migration plan is necessary, especially during natural disasters or various cyber incidents such as state-led actions, espionage, and attacks. Existing literature often neglects VM migration security. To address this, we formulate a model that treats secure VM migration as an optimal stopping problem with partial observability in the context of Next Generation Core Networks (NGCN). We envision a scenario wherein a malevolent actor launches attacks during migration, and a defender oversees the process to ensure security. The migration is guaranteed to occur only via a secure path fortified with security checkpoints. We postulate that the VMs host diverse 5G Core (5GC) entities, a critical component of NGCN. This scenario is modeled via a Partial Observable Markov Decision Process (POMDP), with the Generalized Proximity Policy Optimization (GePPO) algorithm employed to address the POMDP. Our results indicate that the defender's policy for secure VM migration converges quicker than the benchmark Proximity Policy Optimization (PPO) algorithm, highlighting the effectiveness of our approach in NGCN.

A sudden roadblock on highways due to many reasons such as road maintenance, accidents, and car repair is a common situation we encounter almost daily. Autonomous Vehicles (AVs) equipped with sensors that can acquire vehicle dynamics such as speed, acceleration, and location can make intelligent decisions to change lanes before reaching a roadblock. A number of literature studies have examined car-following models and lane-changing models. However, only a few studies proposed an integrated car-following and lane-changing model, which has the potential to model practical driving maneuvers. Hence, in this paper, we present an integrated car-following and lane-changing decision-control system based on Deep Reinforcement Learning (DRL) to address this issue. Specifically, we consider a scenario where sudden construction work will be carried out along a highway. We model the scenario as a Markov Decision Process (MDP) and employ the well-known DQN algorithm to train the RL agent to make the appropriate decision accordingly (i.e., either stay in the same lane or change lanes). To overcome the delay and computational requirement of DRL algorithms, we adopt an MEC-assisted architecture where the RL agents are trained on MEC servers. We utilize the highly reputable SUMO simulator and OPENAI GYM to evaluate the performance of the proposed model under two policies; E-greedy policy and Boltzmann policy. The results unequivocally demonstrate that the DQN agent trained using the e-greedy policy significantly outperforms the one trained with the Boltzmann policy.

What advancements might Next Generation Networks (NGN) unleash that existing ones cannot? NGNs are envisioned to empower the connection between billions of people and zillions of heterogeneous Internet of Things (IoT) devices while consolidating intelligence and autonomy. However, several security-related challenges will be introduced and apparently, the security solutions and architectures used in previous network generations will not be sufficient. The Cloud Security Alliance's (CSA) Software Defined Perimeter (SDP) is a potential candidate to provide the much-needed security framework for next-generation networks. However, the lack of a scalable SDP controller will be a considerable drawback for the wide adoption of the SDP framework. Therefore, this paper focuses on modeling a multi-SDP controller placement problem as a VNF-FGE in a Geo-distributed NFV-based environment as a potential solution to secure next-generation networks. Due to its NP-hard nature, this type of problem can be addressed by extending the NCO approach via Reinforcement Learning (RL) to optimize the reward policy in accordance with the constraints of the problem. The agent developed can learn the placement decisions of the SDP controllers by inference (i.e., policy strategy) through the RL process. The experiment's analysis reveals the RL approach's superiority over the well-known Gecode optimization solver.

The rapid increase in data traffic is forcing mobile network operators to enhance and expand their network infrastructure to meet the new requirements of customers’ Service Level Agreements (SLA). Network Function Virtualization (NFV) provides abstractions of core network functions from the vendor-specific hardware. This allows the network functions to move around the cloud, providing better performance and scaling capabilities. However, deploying virtualized mobile core network in the cloud environment opens many security concerns not only regarding communication between the Radio Access Network (RAN) and the mobile core network but also within the core network itself. In this paper, we propose a framework called virtual Evolved Packet Core - virtual Software Defined Perimeter (vEPC-vSDP) to provide secure communications within the mobile core network by using an authentication-based approach. The SDP components are virtualized and placed within the virtualized core network to provide a zero-trust environment where only authenticated and authorized core network elements can have access to one another. The analysis of the proposed vEPC-vSDP framework confirms its ability to shield the core network traffic from both external and internal attacks. The vEPC-vSDP framework was implemented and tested against Denial of Service (DoS), Distributed Denial of Service (DDoS) and port scanning attacks to demonstrate the resilience of the proposed framework. The results show the capability of vEPC-vSDP to provide secure communication path to mobile core network elements.

The rise in embedded and IoT device usage comes with an increase in LTE usage as well. About 70% of an estimated 18 billion IoT devices will be using cellular LTE networks for efficient connections. This introduces several challenges, such as security, latency, scalability, and quality of service, for which reason edge computing or fog computing has been introduced. The edge is capable of offloading resources to the edge to reduce workload at the cloud. Several security challenges come with multiaccess edge computing (MEC), such as location-based attacks, the man- in-the-middle attacks, and sniffing. This article proposes a software-defined perimeter (SDP) framework to supplement MEC and provide added security. The SDP is capable of protecting the cloud from the edge by only authorizing authenticated users at the edge to access services in the cloud. The SDP is implemented within a mobile-edge LTE network. Delay analysis of the implementation is performed, followed by a Denial-of-Service (DoS) attack to demonstrate the resilience of the proposed SDP. Further analyses, such as CPU usage and port scanning were performed to verify the efficiency of the proposed SDP. This analysis is followed by concluding remarks with insight into the future of the SDP in MEC.