Language models pre-trained on code, such as CodeBERT and CodeGPT, have shown promising performance in software vulnerability detection. However, these models are vulnerable to evasion attacks, in which adversaries apply transformations such as identifier renaming or dead code insertion while maintaining program behaviors, in order to mislead predictions. Although prior research has primarily focused on improving model architectures and code representations to enhance accuracy, the robustness of these systems against evasion attacks is not well studied. In this paper, we propose VulLens, a defense framework compatible with various pre-trained language models to enhance the robustness of software vulnerability detection systems. VulLens begins with code cleaning to remove potentially misleading signals (i.e., malicious dead code), followed by a two-stage detection architecture. In the first stage, high-impact identifiers are selected through attention-based ranking and selectively masked. We then measure the change in prediction confidence between the original and masked inputs: a small change indicates a benign sample, a large change suggests an adversarial sample, and uncertain cases are forwarded to the second stage for further analysis. The second stage employs a self-ensemble strategy enhanced with adaptive diversity-prompting regularization, which explicitly encourages output diversity across multiple ensemble members. By promoting varied decision patterns among the models, this self-ensemble design improves robustness while maintaining flexibility in system deployment and maintenance. Finally, we evaluate VulLens on three pre-trained models across code datasets in Java and C/C++. The results show that VulLens can significantly reduce the attack success rate from 0.867 to 0.223, with minimal impact on detection performance under benign conditions.

The disaggregated, cloud-native design of the 5G Core (5GC) enables flexibility and scalability but introduces significant challenges. Control-plane procedures involve complex interactions across multiple Virtual Network Functions (VNFs), while the user plane must sustain diverse and resource-intensive traffic. Existing tools often benchmark these dimensions in isolation, rely on synthetic workloads, or lack visibility into fine-grained resource usage. This paper presents 5GC-Bench, a modular framework for stress-testing the 5GC under realistic workloads. 5GC-Bench jointly emulates signaling and service traffic, supporting both VNF profiling and end-to-end service-chain analysis. By characterizing bottlenecks and resource demands, it provides actionable insights for capacity planning and performance optimization. We integrated 5GC-Bench with the OpenAirInterface (OAI) 5GC and deployed it on a real 5G testbed, demonstrating its ability to uncover resource constraints and expose cross-VNF dependencies under scenarios that mirror operational 5G deployments. To foster reproducibility and further research, we release publicly all the artifacts.

This tutorial examines modern airspace monitoring technologies and their cybersecurity implications. We cover sensing approaches ranging from traditional radar to passive techniques using ambient radio-frequency (RF) signals, discuss threats such as spoofing and adversarial evasion targeting sensing infrastructure, and share insights from our deployed passive sensing system, along with open research directions.

The next generation of cellular networks is designed to provide ubiquitous connectivity to a wide range of devices. As Telecommunication Service Providers (TSPs) increasingly collaborate with public cloud providers to deploy 5G and beyond networks, a fundamental shift is underway, from hardware-bound Physical Network Functions (PNFs) to cloud-native, containerized deployments managed through platforms like Kubernetes. While this transition promises greater scalability, flexibility, and cost efficiency, it also introduces a complex set of technical and operational challenges that must be thoroughly understood before large-scale cellular deployments can take place in cloud environments. In this survey, we present a structured taxonomy that categorizes the design space of cloud-based cellular deployments across four dimensions: deployment architecture, resource management and orchestration, multi-tenancy and isolation, and economic and ownership models. Using this taxonomy as a foundation, we critically analyze six key investigation areas, security and privacy, scalability and elasticity, performance and latency, cost optimization, resilience and fault management, and compliance and sovereignty, examining each through a cloud-native lens. To benchmark the state of industry adoption, we examine the deployment strategies of leading Infrastructure-as-a-Service (IaaS) providers, namely Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP). Finally, we identify emerging trends such as AI-driven orchestration, quantum-safe protocols for virtualized network functions, and serverless networking for 6G, while articulating the open challenges that remain in realizing robust, scalable cloud-based cellular networks.

Emerging tactical communication scenarios demand agile, resilient, and infrastructure-independent connectivity, particularly in adversarial environments where conventional terrestrial Radio Access Networks (RANs) are impractical. This paper presents RAIDER (Resilient Aerial Intelligent Distributed Edge Relay), a lightweight framework that transforms low-cost Unmanned Aerial Vehicles (UAVs) into autonomous communication relays and compute nodes. RAIDER establishes a multi-hop aerial mesh network capable of extending wireless coverage across obstructed terrain while hosting containerized network functions directly on airborne platforms. We prototype RAIDER using Raspberry Pi-equipped UAVs and demonstrate basic service chaining and relay functionality between disconnected endpoints and a cloud-based backend. Our preliminary experiments validate the feasibility of in-flight edge VNF deployment and mesh-based coverage restoration using commercial off-the-shelf components, laying the foundation for a scalable, software-defined aerial RAN infrastructure.

Cellular networks are comprised of software-based entities, with main functions encapsulated as Virtual Network Functions (VNFs) deployed on Commercial-off-the-Shelf (COTS) hardware. As a key enabler of 5G, network slicing offers logically isolated Quality of Service (QoS) for diverse use cases. With the transition to cloud-native infrastructures, optimizing network slice placement across multi-cloud environments remains challenging due to heterogeneous resource capabilities and varying slice-specific demands. This paper presents SlicePilot, a modular framework that enables autonomous and near-optimal VNF placement using a disaggregated Multi-Agent Reinforcement Learning (MARL) approach. SlicePilot collects real-world traffic profiles to estimate resource needs for each slice type. These estimates guide a MARL-based scheduler that minimizes deployment costs while satisfying QoS constraints. We evaluate SlicePilot on a multi-cloud testbed and demonstrate a 19x speed-up over combinatorial optimization methods, while keeping deployment costs within 7.8

In the evolving landscape of next-generation wireless networks, ensuring secure communications in covert military operations is paramount. This paper proposes an advanced localization-based security system utilizing passive receivers and Time Difference of Arrival (TDOA) techniques to continuously authenticate the signals of a commander in dynamic operational scenarios. Our system effectively counters physical layer spoofing attacks by distinguishing between the precise locations of a legitimate entity and potential adversaries. To that end, we derive the positioning error bound (PEB) specific to TDOA systems, emphasizing the critical impact of receiver arrangement on localization accuracy. Furthermore, we introduce a novel application of deep Q-learning for the NP-hard problem of optimal placement of receivers, addressing the challenge of spatial geometry, which significantly influences localization accuracy. Through extensive testing, we demonstrate that our proposed approach notably outperforms traditional placement methods in mitigating geometry-induced errors and enhancing overall localization precision. Ultimately, this facilitates realizing and maintaining a secure zone where users can authenticate each other through localization.

Distributed Denial of Service (DDoS) attacks have plagued the Internet for decades. Despite the ever-increasing investments into mitigation solution development, DDoS attacks continue to grow with ever-increasing frequency and magnitude. To identify the root cause of the above-observed trend, in this paper, we conduct a systematic and architectural evaluation of volumetric DDoS detection and mitigation efforts over 24,000 papers, articles, and RFCs over 30+ years. To that end, we introduce a novel approach for systematizing comparisons of DDoS research, resulting in a comprehensive examination of the DDoS literature. Our analysis illustrates a small set of common design patterns across seemingly disparate solutions, and reveals insights into deployment traction and success of DDoS solutions. Furthermore, we discuss economic incentives and the lack of harmony between synergistic but independent approaches for detection and mitigation. As expected, defenses with a clear cost/benefit rationale are more prevalent than those that require extensive infrastructure changes. Finally, we discuss the lessons learned which we hope can shed light on future directions that can potentially turn the tide of the war against DDoS.

Federated Learning (FL) exposes vulnerabilities to targeted poisoning attacks that aim to cause misclassification specifically from the source class to the target class. However, using well-established defense frameworks, the poisoning impact of these attacks can be greatly mitigated. We introduce a generalized pre-training stage approach to Boost Targeted Poisoning Attacks against FL, called BoTPA. Its design rationale is to leverage the model update contributions of all data points, including ones outside of the source and target classes, to construct an Amplifier set, in which we falsify the data labels before the FL training process, as a means to boost attacks. We comprehensively evaluate the effectiveness and compatibility of BoTPA on various targeted poisoning attacks. Under data poisoning attacks, our evaluations reveal that BoTPA can achieve a median Relative Increase in Attack Success Rate (RI-ASR) between 15.3% and 36.9% across all possible source-target class combinations, with varying percentages of malicious clients, compared to its baseline. In the context of model poisoning, BoTPA attains RI-ASRs ranging from 13.3% to 94.7% in the presence of the Krum and Multi-Krum defenses, from 2.6% to 49.2% under the Median defense, and from 2.9% to 63.5% under the Flame defense.

Existing registration, discovery, and authorization mechanisms in the 5G core control plane present scalability and efficiency challenges. As cellular deployments scale to accommodate diverse user demands, the 5G core control plane suffers from increased inter-Virtual Network Function (VNF) communication latency, thus deteriorating the reliability of critical procedures. To address this problem, we propose 5G-STREAM (Service mesh Tailored for Reliable, Efficient, and Authorized Microservices) to optimize control plane traffic in distributed cloud environments by establishing a topology awareness of service chains across cloud hierarchies. Leveraging this awareness, 5G-STREAM dynamically configures communication pathways to reduce discovery and authorization signaling overhead, thus increasing the reliability of inter-VNF communication. We develop a prototype of 5G-STREAM and evaluate its performance. Our evaluation results show that 5G-STREAM significantly reduces the process completion time in core service chains by up to 2x inter VNF-Network Repository Function (NRF) latency per transaction, with more pronounced benefits in larger service chains. Furthermore, we show that the cost required to deploy 5G-STREAM is an additional 0.1 USD/hr on AWS for a VNF handling a sustained rate of 50,000 requests/minute.

This article proposes adopting named data networking (NDN) as a foundation for future cellular networks, including 6G, to shift the focus from connection-based to data-centric communication. Securing data at the network layer, NDN reduces control plane signaling overhead and enables secure distributed deployments.

The Open Radio Access Network (O-RAN) architecture enables a flexible, vendor-neutral deployment of 5G networks by disaggregating base station components and supporting third-party xApps for near real-time RAN control. However, the concurrent operation of multiple xApps can lead to conflicting control actions, which may cause network performance degradation. In this work, we propose a framework for xApp conflict management that combines explainable machine learning and causal inference to evaluate the causal relationships between RAN Control Parameters (RCPs) and Key Performance Indicators (KPIs). We use model explainability tools such as SHAP to identify RCPs that jointly affect the same KPI, signaling potential conflicts, and represent these interactions as a causal Directed Acyclic Graph (DAG). We then estimate the causal impact of each of these RCPs on their associated KPIs using metrics such as Average Treatment Effect (ATE) and Conditional Average Treatment Effect (CATE). This approach offers network operators guided insights into identifying conflicts and quantifying their impacts, enabling more informed and effective conflict resolution strategies across diverse xApp deployments.

The demand for accurate indoor localization has surged with the increasing deployment of autonomous healthcare robots for hospital automation. In this work, we introduce CURE: Coordinated Femtocell Placement for Ultra-Precise Retroreflective Tag Location Estimation, a novel positioning framework that leverages 5G femtocells as fixed anchors and retroreflective tags on mobile robots to enhance localization accuracy. Unlike conventional indoor positioning systems that rely on Wi-Fi, CURE utilizes low latency and high-resolution time of flight (ToF) measurements from the positioning reference signal (PRS) in 5G networks, while retroreflective tags mitigate synchronization and multipath issues. A key contribution of this work is the analysis of geometric dilution of precision (GDOP), demonstrating its substantial impact on positioning accuracy beyond raw ranging errors. We introduce an advanced optimization framework based on evolutionary algorithms (EAs) to address the NP-hard problem of optimal femtocell placement for minimizing GDOP. The proposed solution systematically improves localization performance by optimizing femtocell distribution in a 3D indoor space. Simulation results confirm that our optimization algorithm significantly reduces GDOP across the entire hospital floor plan. Compared to traditional femtocell placements, our approach achieves over 20 times lower GDOP.

Geofencing technologies have become pivotal in creating virtual boundaries for both real and virtual environments, offering a secure means to control and monitor designated areas. They are now considered essential tools for defining and controlling boundaries across various applications, from aviation safety in drone management to access control within mixed reality platforms like the metaverse. Effective geofencing relies heavily on precise tracking capabilities, a critical component for maintaining the integrity and functionality of these systems. Leveraging the advantages of 5G technology, including its large bandwidth and extensive accessibility, presents a promising solution to enhance geofencing performance. In this paper, we introduce MetaFence: Meta-Reinforcement Learning for Geofencing Enhancement, a novel approach for precise geofencing utilizing indoor 5G small cells, termed “5G Points", which are optimally deployed using a meta-reinforcement learning (meta-RL) framework. Our proposed meta-RL method addresses the NP-hard problem of determining an optimal placement of 5G Points to minimize spatial geometry-induced errors. Moreover, the meta-training approach enables the learned policy to quickly adapt to diverse new environments. We devised a comprehensive test campaign to evaluate the performance of MetaFence. Our results demonstrate that this strategic placement significantly improves tracking accuracy compared to traditional methods. Furthermore, we show that the meta-training strategy enables the learned policy to generalize effectively and perform efficiently when faced with new environments.

The next generation of mobile networks has been designed in a way to accommodate a wide variety of use cases with diverse Quality of Service (QoS) demands. Within the cellular networks of the future, services will be provided in isolated fragments referred to as network slices. Within this ecosystem, an up-and-coming use case is the advent of Augmented and Virtual Reality (AR/VR), which is crucial for fulfilling the vision of the Metaverse. To create a believable Metaverse, it's crucial to precisely locate user devices in addition to maintaining stringent latency controls. As we transition from 5G to 6G networks, it is expected that these networks will function at higher frequencies and in more compact environments. This shift poses a challenge for indoor ranging because signals at these frequencies behave differently. In pursuit of this objective, our work seeks to demystify the ranging performance of Metaverse Users (MUs) in heterogeneous cellular environments. Our evaluation approach leverages the established protocols of the Third Generation Partnership Project (3GPP), utilizing an integrated network of conventional cellular base stations and Wireless Fidelity (Wi-Fi) routers to facilitate both outdoor and indoor communications. Ultimately the goal of this paper is to show that the proposed hybrid approach effectively enhances indoor ranging by offloading the distance estimation responsibilities from outdoor base stations to indoor access points.

The Fifth Generation (5G) core network is designed as a set of Virtual Network Functions (VNFs) hosted on Commercial-Off-the-Shelf (COTS) hardware. This creates a growing demand for general-purpose computing resources. Given their elastic infrastructure, cloud services like Amazon Web Services (AWS) are attractive platforms to address this need. Therefore, it is crucial to understand the Quality of Service (QoS) requirements associated with deploying the 5G core in the cloud. We developed the 5G-MAP (5G Measurement and Assessment Platform) to understand the trade-offs between different deployment strategies. Our framework facilitates detailed control and user plane performance assessments in varied deployment scenarios. We integrated 5G-MAP with the OpenAirInterface (OAI) 5G core and utilized it in a series of deployments across seven countries, leveraging eight AWS regions and eighteen edge zones. Our evaluations cover from HTTP transactions to user plane throughput and packet loss. We identify topologies that can considerably lower the 5G core service chain latencies due to a significant reduction in the number of inter-site hops. Such actionable performance improvements illustrate how operators can leverage 5G-MAP to optimize their cloud-based 5G deployments.