Papers

A SUPL\_INIT message is a network-initiated trigger that can be sent to a handset using an SMS to unilaterally start a location session: on receipt, the handset is instructed to determine its own position and report it, together with an identifier such as its IMSI, to a server specified in the message, without any action by the phone's user. The concern motivating this investigation is whether such a message could be used to silently exfiltrate a handset's location and subscriber identity to a server under an attacker's control. We investigated this on a Google Pixel 8 handset, which uses a Samsung Exynos modem and a Broadcom GPS/GNSS subsystem. We find no privacy issue: the handset never sends location data to an attacker-chosen server as a result of an unsolicited SUPL\_INIT delivered by SMS.

Recent advances in AI models and agentic technologies make AI for network operations (NetOps) within reach. However, scalability remains a key bottleneck of agentic NetOps when analyzing hyperscale networks, which comprise hundreds of datacenters, each housing thousands of network devices. The scalability challenge is rooted in the requirement of many NetOps tasks that must conduct global reasoning on how a local change of device behavior affects all relevant routing paths, known as routing-path analysis. This paper studies this scalability problem and evaluates how different agentic approaches, namely in-context learning, iterative reasoning, and agent skills, can scale routing-path analysis to large, complex networks. We present RoutingBench for evaluating agentic routing-path analysis, with varying network size and complexity, for various types of device changes. Our results show that agentic analysis is promising: agent skills curated with a principle termed "explore more; digest less" enables routing-path analysis on hyperscale networks of 50K routers with an accuracy of 99.5%, significantly outreaching the scalability of traditional symbolic analysis. Meanwhile, RoutingBench also reveals the boundary of AI agent capability on complex inter-datacenter networks and compound changes, posing open challenges for AI and agentic research.

Natural-language service requests can require a language-model decision before execution starts, consuming part of the request's latency budget. We integrate Jev's decision-oriented application programming interface (API) into edge service orchestration to reduce this overhead while retaining service completion. The integration extracts four to eight bounded intent fields and applies a shared validator, admission policy, and scheduler, accounting for decision waiting throughout the request timeline. We compare Jev, two self-hosted decision models, and three hosted large language models (LLMs) on 8,280 verified requests and on a live admission path with modeled execution and a real optical character recognition service. Across 33 test conditions, Jev reduces median decision latency by 22.7-64.5% relative to the fastest LLM. This latency barely moves with input size, contract width, or catalog size. On four-field contracts, Jev's API fees per correct decision are 59.7-80.9% lower at a cost of a few exact-match points, while wide contracts mark the limit of the substitution. Receiving the service catalog with each request, Jev names unseen services as accurately as known ones. On the live admission path, Jev keeps 0.91-0.95 of requests exact and on time at loads where the LLMs fall below 0.1. Since caching repeated descriptions gives the interpreters nearly the same latency, Jev's gain lies in fresh decisions. These results support decision-model substitution for latency-bound admission on bounded contracts.

Large Language Model (LLM) agents are increasingly trusted to operate live networks: they read state, change configuration, and verify the result. A first-order question is left implicit: at which level of abstraction should the agent operate? We make the interface-abstraction level an explicit, controlled experimental variable, organizing agent-network interfaces into a spectrum from raw CLI (A0) through bounded wrappers (A1) and standardized model-driven configuration (A2) to typed transactional service intent (A3-T), reconciled source-of-truth automation (A3-R), and their combination (A4). We present ANI-Gamut, a reproducible, open-source playground that exposes the same task at several levels on a single, densely populated brownfield substrate, where many coexisting services share resources and collateral damage actually arises. We instantiate and measure four points of the spectrum and describe the others only at the conceptual level, and record three dependent variables as the substrate is stressed by injected faults: task reliability, collateral damage against pre-existing tenants, and operational cost. In a pilot with small run counts (n=20, n=6 and n=4 per level), the interface level moves reliability and cost sharply: on a live change-set task a raw-shell agent fails on all six seeds while a typed transactional interface succeeds on all six (paired McNemar p=0.031, on six discordant pairs), at roughly an order of magnitude less cost, as the engineering effort migrates from the agent to a reusable transaction layer. Collateral damage, by contrast, is absent at every level, whether benign or under faults: in a tenant-isolated substrate the agents fail safe, and the blast radius is held by the substrate's isolation, which moves the safety question from the agent to the substrate.

Monitoring lower airspace over critical infrastructure using cellular signals of opportunity is highly practical because transmitters are pre-deployed, licensed, and continuously active. Digital twins can evaluate the feasibility of such integrated sensing and communication (ISAC) architectures, but their predictive accuracy must be validated against real-world data. This paper reports validation results for TiamiTwin, a digital twin developed for bistatic ISAC drone sensing, using empirical measurements from an operational 5G deployment featuring a commercial band n41 gNB and a receiver separated by 572.8 m over a non-line-of-sight (NLOS) channel. TiamiTwin incorporates three parallel channel representations evaluated on a 240-subcarrier grid: the 3GPP TR 38.901 (Release 19) bistatic ISAC model, a ray-traced site model, and the captured field measurements. Empirical results demonstrate that both statistical and ray-tracing models under-predict the measured root-mean-square (RMS) delay spread by approximately a factor of three. Furthermore, target reflections sit 68 dB below static clutter in power, making target detection entirely dependent on Doppler separation to isolate the drone from zero-Doppler background returns. Despite this severe clutter environment, the target remains separable along 88% of the flight path in the delay, Doppler, or joint delay-Doppler domains.

This letter covers a broad comparison of methods for classification and regression applications for a user-level handover decision making in scenarios with adverse propagation conditions involving buildings, coverage holes, and shadowing effects. The simulation campaigns are based on network simulator ns-3. The comparison encompasses classical machine learning approaches, such as KNN, SVM, and neural networks, but also state-of-the-art fuzzy logic systems and latter boosting machines. The results indicate that SVM and MLP are the most suitable for the classification of the best handover target, although fuzzy system SOFL can perform similarly with lower processing time. Additionally, for the download time estimation, LightGBM provides the smallest error with short processing time, even in hard propagation scenarios.

The Precision Time Protocol (PTP) provides the time and phase synchronization required by disaggregated Open Radio Access Networks (O-RAN). Yet, in current open fronthaul deployments, PTP traffic lacks mandatory authentication and integrity protection, leaving synchronization vulnerable to spoofing, replay, and delay manipulation attacks that can degrade radio access performance. Existing protections are poorly suited to this setting: they either add excessive latency, do not support multicast dissemination efficiently, or fail to contain key exposure under partially trusted RUs. This paper analyzes the security risks of unprotected O-RAN PTP and develops a threat model for open fronthaul deployments. We then introduce PRTESLA-C, a lightweight synchronization protection mechanism that combines per-round delayed key disclosure with ASCON-based message authentication. PRTESLA-C uses an apply-then-verify-and-correct paradigm: timing samples are applied immediately to preserve real-time control, verified after key disclosure, and removed from persistent synchronization state if authentication fails. This design maintains sub-microsecond synchronization accuracy, provides strong protection against spoofing and replay, and bounds the impact of delay manipulation with minimal computational and latency overhead.

Large language models (LLMs) increasingly rely on external sources when answering questions that require proprietary information or up-to-date live web content, through both traditional single-shot retrieval-augmented generation (RAG) and multi-turn agentic RAG. Yet today's web infrastructure is still built for human clients. Given a query, current search services return a list of URLs and snippets ranked for generic relevance; content delivery networks (CDNs) cache URL-addressed objects (texts, images, videos, etc.) without knowing which passage an agent needs. LLMs, in contrast, consume short, semantically coherent passages, hereafter "chunks", selected for downstream task utility rather than similarity alone, and may retrieve statefully across reasoning turns. Uncoordinated agents also repeat search, data acquisition, and semantic processing, duplicating work that could be shared. We argue that semantic chunk retrieval should become a first-class network-delivery abstraction. We propose Semantics Delivery Network (SemDN): an origin-authorized, hierarchical edge substrate that indexes, searches, and smart-caches web content at chunk granularity. SemDN serves agents on behalf of participating websites, amortizes data acquisition and processing across agents, and supports tenant-specific retrieval policies. Because, unlike URL caching, semantic retrieval provides no explicit miss signal, SemDN must estimate when its enrolled corpus may be incomplete or stale and trigger scoped discovery or refresh. It raises open questions about shareable retrieval state, hierarchical caching, coverage risk, and deployment. Our preliminary probes reveal a large gap between page content processed and chunks consumed, substantial task-local reuse, and higher answer quality per context token from chunk delivery.

A companion paper found that what a defender must block over time has units: bits per period [Takashita, 2026a]. This paper sets it. Hold every crossing in escrow for one period, audit each held item independently with probability r, and revoke the window if any audit catches something. An adversary crossing k times, each carrying c bits, expects kc(1-r)^k, maximised at k* = 1/ln(1/(1-r)), a bound of L(r) ~ c/(er) per window. The bound is a supremum over the adversary's choice, so the scheme may be public; simulation matches it to 7.7 standard errors. It is a rate, not a total: escrow alone still lets the secret assemble in every run. But if the secret decays at a fraction mu of held bits per period, holdings converge to g/mu at any horizon, so an L-bit secret is unreachable once mu > g/L – an error threshold in Eigen's sense, sharp where the closed form puts it (100% of runs assemble at 0.9mu*, 0% at 2mu*, over 20,000 windows). Deception that needs the adversary to reason badly fails: a surface whose names lie left accuracy at 18 of 18, and 100% at three reader strengths. Withholding reference works, and differently: no reader would commit at all. Keying the entry points hides 0.10 bits of what a module does; keying the denotation hides 2.64 of 3.00 at chance accuracy, while 100% of readers still call it ordinary Python. Variance must be removed from the audit rate, where loot is convex in r, and added to the activation budget, where survival is multiplicative: extinction 70% to 100% at a fixed mean. End to end the stack takes the leak from 100,000 to 59 bits, a factor of 1,704, leaving 12% of legitimate work standing; keying the window to the caller restores that to 100% at no cost in leakage, at the price of a bound that is per principal. Of 65 read-only tools, escrow leaves 2,400 bits per call: a factor of 10, not infinity.

As services are increasingly centralized in public clouds, understanding the nature of Internet Background Radiation (IBR) hitting these particular environments is an important part of understanding their overall security posture. Classical network telescopes, long the cornerstone of IBR research, face hurdles here: their surface area is shrinking, and their well-known address ranges are easily avoided. We present a multi-cloud view of IBR in this paper. We deploy a passive, distributed network telescope with 336 IPs across five major cloud providers. We compare traffic from our cloud telescope with data from two classical telescopes, a large well-known (/9 + /10) and a small unknown /16, to analyze observational biases. To enable a fair comparison across very different telescope sizes, we tune a scan detection algorithm to determine appropriate thresholds. Our findings reveal that IBR in the cloud is strongly provider-dependent rather than geography-dependent, highlighting the necessity of multi-cloud deployments for comprehensive visibility of IBR in the cloud. Our cloud telescope also captures a distinct set of scanners, confirming that scanning activity is not uniform across the IPv4 space, and we confirm that small, distributed telescopes are ill-suited for observing random events like DDoS backscatter. Our work underscores that monitoring must evolve beyond classical telescopes to include diverse, multi-cloud vantage points to accurately capture IBR.

Reconfigurable intelligent surfaces (RISs) are poised to revolutionize wireless multicasting by enabling extended coverage and reliable operation in obstructed environments. These benefits, however, can be undermined by security vulnerabilities arising from practical deployment factors. This work addresses three such critical factors, (i) the discrete nature of RIS phase shifts, (ii) the presence of colluding eavesdroppers, and (iii) the inefficiency of static illumination beams, each threatening security if not properly accounted for in system design. To mitigate these issues, we formulate a joint resource allocation problem that minimizes the wiretap signal-to-noise ratio (SNR) across all eavesdroppers by co-optimizing the RIS configuration and the base station (BS) beam management. This yields a complex, nonconvex mixed-integer nonlinear program (MINLP), which we equivalently reformulate into a tractable mixed-integer quadratically constrained program (MIQCP) solvable to global optimality. Numerical results confirm that the proposed scheme significantly bolsters security, suppressing the wiretap SNR by up to 58% compared to existing baselines.

The network virtualization (NV) technology has enabled the sharing of multiple resources among virtual networks (VNs) in cloud data centers. One of the key challenges is to allocate resources in real-time for virtual network request (VNR), which is known as online virtual network embedding (VNE). However, the existing online VNE methods do not exploit the multi-dimensional complementary relationship among diverse VNRs, resulting in the fragmentation and waste of substrate resources. In this paper, we propose the pattern matching based online VNE approach by constructing appropriate matching rules among observed patterns to maximize resources utilization. We devise the clustering based VNRs quantization method and conduct rigorous study on the pattern combination filtering problem. Then, we utilize the column generation to solve it and construct the pattern matching rules. Based on the rules, we propose an online pattern matching VNE algorithm with linear worst-case complexity. Evaluation on a 106-server testbed using Alibaba production cluster trace dataset shows that our algorithm achieves close-to-offline performance and more accepted workloads that outperforms traditional designs by 25%-30%.

Anycast allows for providing services from multiple, geographically distant Points of Presence (PoPs), using a single IP address, to, e.g., improve resilience. Due to its opaqueness, it is often unknown which addresses are provisioned using anycast and, if so, where the PoPs are located. As anycast is widely used for critical Internet infrastructures (e.g., the DNS) efforts have been made to map anycast deployments. The current state-of-the-art mapping technique, iGreedy, relies on latency-based measurements, and is adversely affected by noise caused by, e.g., network processing delays. Previous work has shown that traceroute can alternatively be used to detect anycast. As traceroute reveals the hops a packet traverses, it may also be used to locate sites using geolocation data for hops near the anycast PoPs. This paper is the first to assess the performance of the traceroute-based approach at scale, by targeting 14k prefixes from an anycast census. Using ground truth we show traceroute achieves a slight increase in enumeration and geolocation precision over iGreedy. However, it suffers from overestimating the number of PoPs and incurs a 4 times increase in probing cost, making it unattractive for anycast censuses.

Automotive Ethernet carries heterogeneous multi-protocol traffic in modern in-vehicle networks, where labeled attack data are rarely available and the strongest prior unsupervised detector still relies on handcrafted traffic features. This article presents X-SPUR, an explainable, surprisal-based, protocol-aware unsupervised reasoning framework that instead represents raw packet fields as token sequences, learns benign traffic patterns through causal language modeling, and detects anomalies from per-token cross-entropy surprisal. To incorporate temporal context, we introduce a bimodal fusion architecture that combines payload-token embeddings with inter-packet timing through additive fusion and a Hadamard interaction. To handle the heterogeneous score distributions of different protocol families, we further propose a dual top-$k$% per-protocol $Z$-score calibration that jointly captures moderately distributed and sparse anomaly signatures. On the TOW-IDS dataset, X-SPUR achieves an AUC of 0.9987. This is marginally higher than the 0.9969 reported for AERO. X-SPUR also eliminates handcrafted feature engineering. We train a separate CarDS model using the same architecture and training hyperparameters. This model retains strong performance on the second automotive Ethernet dataset. Beyond detection, per-token surprisal provides fine-grained explainability by attributing anomaly scores to specific protocol fields, supporting interpretable security analysis in heterogeneous in-vehicle networks.

As digital commerce ecosystems expand into low-end consumer electronics (CE), hardware constraints-specifically limited CPU duty cycles and volatile heap fragmentation-become significant bottlenecks for complex transactional flows. Traditional on-device middleware requires high "network chattiness" to manage multi-step state machines, leading to increased latency and potential transaction failure on unstable residential networks. This paper proposes a novel Transactional Backend-for-Front-End (T-BFF) Orchestration Framework designed for resource-constrained embedded platforms. By migrating the transactional state machine and microservice orchestration to a cloud-side layer, we achieve a significant reduction in client-side overhead. Our framework introduces a "Double-Handshake" protocol utilizing non-volatile flash memory for state recovery after hardware reboots. Experimental results on an ARM Cortex-A53 platform demonstrate a 35% reduction in maximum heap usage and a 40% improvement in end-to-end transaction latency. This framework provides a scalable, sustainable blueprint for maintaining transactional integrity on legacy hardware in the 2026 IoT landscape.

Modern networks are large in scale and heterogeneous in configuration, making manual policy management increasingly impractical. Intent-Based Networking (IBN) addresses this by automating the translation of high-level operator goals into low-level network configurations. Yet existing IBN systems rely on static heuristics and fixed-feature classifiers that generalize poorly to distribution shifts such as new service definitions or evolving phrasing in operator requests. Large Language Models (LLMs), with strong reasoning and translation capabilities demonstrated across many domains, are a natural candidate for IBN policy generation. However, it is unclear whether LLMs can be reliably applied to this task, nor whether their use mitigates or worsens the underlying security risk. In this work, we show that while fine-tuned LLMs excel at intent translation, they exhibit false negative rates when checking whether a proposed intent violates an existing security policy. The root cause is not a lack of logical reasoning capability, but LLMs lack of persistent grounding in network topology and group hierarchy. Motivated by this finding, we introduce NetInspector, a three-layer agentic framework that enforces a verify-then-act protocol, decoupling information retrieval from reasoning so that the LLM focuses on symbolic reasoning while every policy decision is grounded in verifiable network facts retrieved from a live Environment Layer before approval. On NetInspector-Bench, a 2,224-sample synthetic benchmark spanning campus, enterprise, and WAN topologies, NetInspector reduces FNR by over 30% relative to ungrounded baselines and remains robust under linguistic distribution shifts.

Geofencing refers to controlling the availability of wireless services within a network perimeter. In this paper, we study how RIS can be used to achieve geofencing in outdoor-to-indoor network scenarios. Particularly, we propose GeoRIS, a RIS controller that achieves geofencing by exploiting beam management procedures to control beam alignment in networks with steerable directional transmission links. GeoRIS does not require control over the outdoor base station and can work with or without channel state information. Through simulations based on 3GPP network models, we validate GeoRIS and demonstrate that it can "shield" an indoor network area (for example, by weakening communication links to the point where indoor users cannot meet the minimum requirements of 5G eMBB services). We also highlight an interesting insight: the same RIS can play a dual role, extending or inhibiting outdoor-to-indoor communication. In our simulations, we show that GeoRIS can easily change an indoor space from a "strongly covered area" (e.g., supports eMBB services in approx. 90% of network area) to an "out-of-service area" (e.g., inhibits eMBB services in approx. 90% of network area), or vice-versa, an attractive characteristic in scenarios in which dynamic indoor coverage control is needed.

Accurate positioning is increasingly required in indoor and dense urban environments; nonetheless, satellite-based systems are not always available, and standardized 5G localization solutions remain difficult to deploy with commercial devices. This paper presents RUN-O-RAN, an O-RAN-native framework that enables network-centric uplink localization using standard Sounding Reference Signal (SRS) transmissions from commercial 5G devices. RUN-O-RAN xApp coordinates serving and neighboring base stations, enabling non-serving base stations to retrieve SRS-based uplink timing measurements that would be unavailable in conventional RAN deployments, without modifying the UE or existing 3GPP signaling procedures. The framework combines cooperative SRS collection, first-path time-of-arrival estimation, timing-advance tracking, clock-drift compensation, and multi-anchor position estimation into a complete network-side localization service. Experimental evaluation over $150,000$ SRS transmissions validates the proposed framework, achieving meter-level localization under diverse propagation conditions while revealing the impact of anchor geometry and multipath on positioning accuracy. These findings demonstrate that cooperative SRS-based localization can be realized within the O-RAN ecosystem without modifying commercial UEs, providing a practical foundation for future network-native ISAC positioning services.

Connected-vehicle safety evaluations rely on coupled traffic and network simulations, but standard channel models ignore radio resource competition in 5G NR sidelink Mode-2, reporting unrealistically high message delivery in dense traffic. This study introduces resource-competition losses without requiring full protocol reimplementation. We labeled 10.5 million reception outcomes from ns-3 5G-LENA traces (calibrated on 3GPP scenarios and driven by SUMO trajectories) to fit NS3Learn - a closed-form model capturing half-duplex loss, scheduling collisions, receiver capture, and decoding. Evaluation spanned two signalized urban networks, six penetration levels (1-100%), and five random seeds per condition. NS3Learn achieved a mean absolute deviation of 0.06 in per-instant delivery compared to ns-3 5G-LENA, outperforming alternative models (0.44 and 0.55 deviation). Fitted parameters transferred to a distinct intersection with only 20% additional error. Crucially, using realistic communication models reversed simulated traffic speed trends and more than doubled predicted hard-braking events. The framework transfers reception realism between simulators via model distillation instead of full reimplementation. Every stage maps directly to an explicit physical mechanism. Researchers and transportation agencies can maintain existing simulation pipelines while accurately accounting for dense-traffic packet loss and denial-of-service impacts. Adapting to new radio configurations requires only offline refitting rather than code modification.

The goal-oriented communication paradigm is poised to enable novel real-time applications by easing the burden on communication networks while still delivering task-relevant information. However, efforts so far have focused on the encoding problem, while the design of medium access schemes is still in the early stages of development, especially when connectivity is to be provided to a massive number of devices, e.g., for remote monitoring. In this respect, existing goal-oriented approaches are often centralized or based on simplified underlying mechanisms, requiring unrealistic assumptions. In this work, we present the Goal-oriented Irregular Repetition Slotted ALOHA (GO-IRSA) scheme, which combines modern random access techniques with belief-based policies. GO-IRSA does not impose significant computing loads on the sensors or require frequent feedback, and it can reduce the average and worst-case error of the estimate of a distributed Wiener process by over 30% with respect to the optimal centralized solution in a network with thousands of sensors, and is robust to imperfect interference cancellation and inaccurate process knowledge.

LEO satellite networks feature dynamic topologies, time-varying links, and diverse service requirements, which make conventional routing schemes difficult to support fine-grained quality-of-service (QoS) provisioning. Existing studies mainly optimize routing over network states with predefined objectives, but rarely address the practical challenge of translating unstructured natural-language service requests into adaptive routing decisions. To bridge this gap, we propose STR-Agent, an LLM-driven framework for QoS-aware routing in LEO satellite networks. The key innovation of STR-Agent lies in unifying intent perception, tool-based execution, experience accumulation, and reflection-based policy adaptation within a single agent architecture. Specifically, the Perception Module converts natural-language requests into structured routing semantics, while the Reflection Module dynamically adjusts the service-to-routing-policy mapping according to real-time congestion conditions and historical routing outcomes, rather than relying on a fixed routing objective. In addition, we develop a specialized perception model, and construct a domain-specific supervised fine-tuning dataset for LEO service understanding. Simulation results in a Walker-Delta constellation show that STR-Agent significantly outperforms conventional baselines: it reduces end-to-end delay by up to 60% compared with DQ-Dijkstra, improves average intent-understanding accuracy from 45.4% to 92.45% after supervised fine-tuning, and the Reflection Module further reduces the delay by 120 ms at 600 Mbps. These results demonstrate the potential of LLM-driven agent architectures to enable service-aware and adaptive QoS routing in future LEO satellite networks.

Large Language Models (LLMs) transform vast collections of unstructured text into semantic patterns used for language generation and reasoning tasks. Behind their ease of use lies a complex process: words become tokens, tokens become vectors, and vectors ultimately give rise to streams of bits that flow through High-Performance Computing (HPC) systems. As modern LLMs grow to billions or trillions of parameters, this path increasingly unfolds across thousands of interconnected accelerators, making the underlying communication fabric a critical and often opaque component of model training. This tutorial aims to walk the reader through the journey from words to network traffic, shedding light on how language is translated into communication flows within HPC training systems. Using concrete examples from Dante's Divine Comedy, we illustrate how model architecture, tokenization, embeddings, and parallelization strategies shape the volume, structure, and timing of data exchanged across the network. We combine architectural analysis with analytical traffic models and numerical examples to characterize the communication requirements of LLM training. We try to demystify how words travel across the network and provide practical insights into the network requirements needed to support the journey from text to trained model.

Dynamic ad insertion comparisons can conflate trigger, reach, readiness, playback, billability and measurement even when the accounting is arithmetically correct. We separate these events with an observed-event ledger, a candidate-invariant reference deadline and pod-level contribution accounting. The deadline rule is fixed before candidate assignment and tests whether an admissible transition state remains valid, not whether preparation merely finished earlier. A restricted monotone-playback representation states when media-position summaries suffice; a counterexample shows why they fail over a wider path class. An offline synthetic study exercises the definitions over nine short-lifetime conditions informative for the readiness comparison and nine long-lifetime conditions serving as analytic controls. Across 45,000 shared scripts, two trigger policies share imposed playback paths, latency draws and hypothetical value and cost coefficients. Playhead summaries substantially misclassify reach events in the nonmonotone mixtures, yet neither shortcut reverses the contribution contrast in this grid, because some errors cancel under the shared design. Replacing validity at the deadline with completion by the deadline reverses the comparison in three of the nine informative conditions, all at one of the three latency settings. Scoring readiness at actual viewer arrival rather than at the reference deadline shifts pause-path readiness but changes no contribution sign. These outcomes are consequences of the event definitions applied to established misclassification mechanisms. Conservative bounds retain uncertainty when records are missing, and the artifact records code, seeds, event histories and checking procedures. The evidence is synthetic, uses no commercial telemetry, and ranks neither server-side nor server-guided insertion.

As streaming services become more commonplace, analyzing their behavior effectively under different network conditions is crucial. This is normally quite expensive, requiring multiple players with different bandwidth configurations to be emulated by a powerful local machine or a cloud environment. Furthermore, emulating a realistic network behavior or guaranteeing adherence to a real network trace is challenging. This paper presents PyStream, a simple yet powerful way to emulate a video streaming network, allowing multiple simultaneous tests to run locally. By leveraging a network of Docker containers, many of the implementation challenges are abstracted away, keeping the resulting system easily manageable and upgradeable. We demonstrate how PyStream not only reduces the requirements for testing a video streaming system but also improves the accuracy of the emulations with respect to the current state-of-the-art. On average, PyStream reduces the error between the original network trace and the bandwidth emulated by video players by a factor of 2-3 compared to Wondershaper, a common network traffic shaper in many video streaming evaluation environments. Moreover, PyStream decreases the cost of running experiments compared to existing cloud-based video streaming evaluation environments such as CAdViSE.

Containment decisions – where to put a firewall, which links to monitor, what a program may reach – are computed from an observed structure, and observation is a snapshot. We ask what a snapshot misses when the wiring changes over time. On a hypercube whose active dimension rotates, a balanced split shows zero crossing edges at 93% of instants, yet 8,192 edges must be blocked permanently; adding one always-on ring, a defender sees 2 where 8,194 must be blocked, a factor of 4,097. A time-aware defender holds 585 blocks on average, but one step of clock lag drops its containment to 0%. On the real Internet the same gap is only x1.8-2.0 (1997-2000) and x1.5-1.6 (2024-2026) once growth is removed, so the blind spot is introduced by design, not inherited from the world – and it has been designed: the round-robin schedules of optical datacentre fabrics have a gap equal to their period. A declared capability map, checked by static reachability over a real application's call graph, catches all 8 planted holes; the string deny-list previously in place catches 2. One calculation generates every number from one parameter, the period, and reads three boundaries not usually called schedules: frequency hopping, whose standard results are these closed forms with channels in place of edges; the air gap, whose always-on crossing set is empty and whose known breach came at the one phase a snapshot misses; and the tool surface of a coding agent, inventoried from the inside. Turning the cuts on that agent: idle, its instantaneous state cut is zero, while 6 channels carry it across a context reset, none of them the network, so severing the network removes 0. The channel that spawns copies is a branching process with a sharp threshold at approval rate 1/b, below which denial is unnecessary and above which denial is insufficient. Contain by the paths that exist, not by the behaviour that was seen.

Quantum computers are technologically novel and unusual, but at system scale they should be engineered using many of the same principles that govern classical heterogeneous accelerators. This paper argues that utility-scale quantum architecture is primarily a cost-performance problem across a coupled quantum-classical system, leading to a blueprint for scalable quantum processing unit (QPU) design. Our architecture blueprint is organized around clean logical abstractions, hiding details and complexity of physical qubit modalities below the instruction set architecture (ISA) boundary, and specializes recurring functions aggressively to minimize the cost for utility scale quantum computations. Its low-level implementation through specialized local hardware for control, readout, and quantum error correction (QEC) closely resembles the architecture of high-performance network stacks. One of our main insights is that the design principles and the resulting architecture closely follow established practice from classical computing and networking.

Hybrid terrestrial-satellite quantum networks combine terrestrial fiber infrastructure with free-space links to enable long-distance entanglement distribution. Entanglement can be routed over different combinations of terrestrial and satellite links, resulting in paths with different entanglement generation rates (EGRs) and fidelities. Existing routing algorithms, however, typically reduce routing to a single-objective problem by optimizing either EGR or fidelity, or by optimizing one while constraining the other, and thus do not explicitly capture the trade-off between the two. In this paper, we present a bi-objective routing framework for hybrid quantum networks that jointly optimizes end-to-end EGR and fidelity. We formulate routing as a Pareto optimization problem and show that it possesses a special mathematical structure: the bottleneck nature of end-to-end EGR and the multiplicative nature of end-to-end fidelity allow the problem to be transformed into a MAXMIN-MINSUM bi-criterion path problem. This transformation enables the exact polynomial-time computation of a minimal complete Pareto set using Martins' bicriterion routing algorithm. Our results demonstrate that hybrid networks expose substantially richer Pareto frontiers than terrestrial-only networks and that the proposed framework outperforms representative single-objective routing policies by allowing applications to select paths based on their EGR and fidelity requirements.

The Model Context Protocol (MCP) standardizes communication between autonomous Artificial Intelligence (AI) agents and remote tools over Streamable HTTP. This shift introduces a class of machine-generated, authenticated, and high-frequency JSON-RPC traffic directly into enterprise networks. Enterprise network defenders have historically relied on machine-like cadence as an Indicator of Compromise (IoC). In this study, we show that without explicit network-layer indication, MCP traffic structurally and temporally resembles Command and Control (C2) beaconing behavior, specifically the polling architectures used by advanced persistent threats like Cobalt Strike. Counter to theoretical assumptions about machine-generated polling, our measurements reveal a visibility gap: standard enterprise Intrusion Detection Systems (IDS) and behavioral beacon-scoring frameworks do not classify MCP remote tool usage as anomalous within our testbed scope. Through a controlled Docker-based testbed simulating eleven mathematically defined traffic profiles across three TLS conditions (Opaque, TLS-Inspected, and Cleartext), we evaluate Suricata signature matching and RITA behavioral scoring against MCP JSON-RPC patterns. Our results show that MCP traffic, regardless of temporal smearing (jitter) or TLS inspection visibility, evades detection within this configuration, yielding a consistent 0.0 behavioral beacon score and near-zero IDS content alerts under the Emerging Threats (ET) Open ruleset. While opaque TLS obscures HTTP content, it exposes agent traffic to flow-level temporal analysis; however, NIDS heuristics tuned to identify traditional malware do not flag the lognormal inter-arrival distributions characteristic of generative AI reasoning loops. To address this gap, we propose an agent-native network indication standard including Agent-Native ALPN and standardized out-of-band headers.

The O-RAN control plane is becoming agentic: autonomous AI agents, deployed as rApps by different vendors, independently close control loops over shared radio resources. We demonstrate on a live O-RAN system that this independence is unsafe. Two agents with individually correct objectives, one protecting a latency SLA and one maximizing utilization for energy efficiency, jointly drive recurring opposing excursions of the shared resource partition that neither produces alone. Existing conflict-mitigation mechanisms presume a statically known application population and cannot govern agents whose behavior emerges at run time. We present AURA, a lightweight arbitration layer that admits agent actions only when they satisfy feasibility invariants, per-variable dwell times, and a deadband, and we prove the arbitrated system converges to a feasible operating point. Implemented on an OpenAirInterface (OAI) testbed with measured one-way latency and throughput, AURA reduces recurring shared-state excursions by more than an order of magnitude (from 8.4 to 0.4 PRB amplitude) and virtually eliminates cross-slice throughput starvation (from 40-55% to 0.3%), while leaving the protected slice's own latency compliance unchanged, a trade-off the convergence guarantee makes explicit.

The rise of large satellite constellations and Distributed Space Systems (DSS) demands generalized frameworks that enable fault-tolerant, autonomous distributed space applications. Conventional ground-centric command and control does not scale to systems of tens or hundreds of satellites, motivating the adoption of distributed computing. This paper introduces a conceptual distributed computing framework for satellite swarms, covering distributed state, command and control, and scientific mission. As a first validation step, a strongly eventually consistent distributed state service is designed and implemented using Conflict-free Replicated Data Types (CRDT), specifically a Last-Write-Wins Register-based key-value store. The service is evaluated in the context of Space Situational Awareness catalog dissemination across a simulated 66-satellite constellation emulated with GoNetEm. Results show that the CRDT-based approach reduces ground-to-satellite communication from 66 messages to a single uplink per update, with tree traversal matching the total message count of direct uplink. Under concurrent updates, sub-linear inter-satellite message growth is observed as nodes discard outdated versions, further reducing network overhead. These results demonstrate the viability of a CRDT-based distributed state as a scalable and fault-tolerant foundation for satellite swarm applications.