The biodegradable design could help keep plastic from becoming fish food. Breakthroughs, discoveries, and DIY tips sent every weekday. Chances are sushi aficionados have left a restaurant take-out in tow and with a handful of adorable, but environmentally problematic, fish-shaped soy sauce packets. These single-use plastic " shoyu-tai " drip bottles are as iconic as they are convenient, but their small size and disposability mean they often end up sliding down sinks and into drains. Once in the big blue, they slowly break down into microplastics that are then eaten by fish .

In domains of cybersecurity, recent advancements in Machine Learning (ML) and Deep Learning (DL) have significantly enhanced Network Intrusion Detection Systems (NIDS), improving the effectiveness of cybersecurity operations. However, attackers have also leveraged ML/DL to develop sophisticated models that generate adversarial packets capable of evading NIDS detection. Consequently, defenders must study and analyze these models to prepare for the evasion attacks that exploit NIDS detection mechanisms. Unfortunately, conventional generation models often rely on unrealistic assumptions about attackers' knowledge of NIDS components, making them impractical for real-world scenarios. To address this issue, we present DiffuPac, a first-of-its-kind generation model designed to generate adversarial packets that evade detection without relying on specific NIDS components. DiffuPac integrates a pre-trained Bidirectional Encoder Representations from Transformers (BERT) with diffusion model, which, through its capability for conditional denoising and classifier-free guidance, effectively addresses the real-world constraint of limited attacker knowledge. By concatenating malicious packets with contextually relevant normal packets and applying targeted noising only to the malicious packets, DiffuPac seamlessly blends adversarial packets into genuine network traffic. Through evaluations on real-world datasets, we demonstrate that DiffuPac achieves strong evasion capabilities against sophisticated NIDS, outperforming conventional methods by an average of 6.69 percentage points, while preserving the functionality and practicality of the generated adversarial packets.

Abstract--The rise of 5G/6G network technologies promises to enable applications like autonomous vehicles and virtual reality, resulting in a significant increase in connected devices and necessarily complicating network management. Even worse, these applications often have strict, yet heterogeneous, performance requirements across metrics like latency and reliability. Much recent work has thus focused on developing the ability to predict network performance. However, traditional methods for network modeling, like discrete event simulators and emulation, often fail to balance accuracy and scalability. Network Digital Twins (NDTs), augmented by machine learning, present a viable solution by creating virtual replicas of physical networks for real-time simulation and analysis. State-of-the-art models, however, fall short of full-fledged NDTs, as they often focus only on a single performance metric or simulated network data. We introduce M3Net, a Multi-Metric Mixture-of-experts (MoE) NDT that uses a graph neural network architecture to estimate multiple performance metrics from an expanded set of network state data in a range of scenarios. We show that M3Net significantly enhances the accuracy of flow delay predictions by reducing the MAPE (Mean Absolute Percentage Error) from 20.06% to 17.39%, while also achieving 66.47% and 78.7% accuracy on jitter and packets dropped for each flow. Emerging 5G and 6G mobile network architectures aim to support new applications like autonomous vehicles and mixed reality [1], [2], both of which require significantly expanded network capabilities. These and other new applications envisioned as part of the 5G and 6G network ecosystem will lead to massive numbers of connected devices with heterogeneous performance expectations, which increases the complexity and cost of managing communication networks [2]. For example, interactive applications like augmented reality generally require response latencies under 200ms [3], while safety-critical applications like autonomous vehicles might require highly reliable delivery of high-priority packets [4].

The proliferation of autonomous Unmanned Aerial Vehicles (UAVs) in Beyond Visual Line of Sight (BVLOS) applications is critically dependent on resilient, high-bandwidth, and low-latency communication links. Existing solutions face critical limitations: TCP's head-of-line blocking stalls time-sensitive data, UDP lacks reliability and congestion control, and cellular networks designed for terrestrial users degrade severely for aerial platforms. This paper introduces AQUILA, a cross-layer communication architecture built on QUIC to address these challenges. AQUILA contributes three key innovations: (1) a unified transport layer using QUIC's reliable streams for MAVLink Command and Control (C2) and unreliable datagrams for video, eliminating head-of-line blocking under unified congestion control; (2) a priority scheduling mechanism that structurally ensures C2 latency remains bounded and independent of video traffic intensity; (3) a UAV-adapted congestion control algorithm extending SCReAM with altitude-adaptive delay targeting and telemetry headroom reservation. AQUILA further implements 0-RTT connection resumption to minimize handover blackouts with application-layer replay protection, deployed over an IP-native architecture enabling global operation. Experimental validation demonstrates that AQUILA significantly outperforms TCP- and UDP-based approaches in C2 latency, video quality, and link resilience under realistic conditions, providing a robust foundation for autonomous BVLOS missions.

Network routing is a distributed decision problem which naturally admits numerical performance measures, such as the average time for a packet to travel from source to destination. OLPOMDP, a policy-gradient reinforcement learning algorithm, was successfully applied to simulated network routing under a number of network models. Multiple distributed agents (routers) learned co-operative behavior without explicit inter-agent communication, and they avoided behavior which was individually desirable, but detrimental to the group's overall performance. Furthermore, shaping the reward signal by explicitly penalizing certain patterns of sub-optimal behavior was found to dramatically improve the convergence rate.

--Networks built on the IEEE 802.11 standard have experienced rapid growth in the last decade. Their field of application is vast, including smart home applications, Internet of Things (IoT), and short-range high throughput static and dynamic inter-vehicular communication networks. Within such networks, Channel State Information (CSI) provides a detailed view of the state of the communication channel and represents the combined effects of multipath propagation, scattering, phase shift, fading, and power decay. In this work, we investigate the problem of jamming attack detection in static and dynamic vehicular networks. We utilize ESP32-S3 modules to set up a communication network between an Unmanned Aerial V ehicle (UA V) and a Ground Control Station (GCS), to experimentally test the combined effects of a constant jammer on recorded CSI parameters, and the feasibility of jamming detection through CSI analysis in static and dynamic communication scenarios. The rapid expansion of IEEE 802.11 networks over the past decade has revolutionized wireless communications, particularly in such applications as smart homes [1], Internet of Things (IoT) [2], industrial automation, and short-range high-throughput vehicular networks [3]. This can be contributed to their high throughput capabilities, ease of deployment, and increasingly growing demand for internet connectivity. However, the widespread usage and extensive deployment of these networks make them an attractive target for malicious actors, and thus, more exposed and susceptible to jamming attacks.

Digital neuromorphic processors are emerging as a promising computing substrate for low-power, always-on EdgeAI applications. In this tutorial paper, we outline the main architectural design principles behind fully digital neuromorphic processors and illustrate them using the SENECA platform as a running example. Starting from a flexible array of tiny RISC-V processing cores connected by a simple Network-on-Chip (NoC), we show how to progressively evolve the architecture: from a baseline event-driven implementation of fully connected networks, to versions with dedicated Neural Processing Elements (NPEs) and a loop controller that offloads fine-grained control from the general-purpose cores. Along the way, we discuss software and mapping techniques such as spike grouping, event-driven depth-first convolution for convolutional networks, and hard-attention style processing for high-resolution event-based vision. The focus is on architectural trade-offs, performance and energy bottlenecks, and on leveraging flexibility to incrementally add domain-specific acceleration. This paper assumes familiarity with basic neuromorphic concepts (spikes, event-driven computation, sparse activation) and deep neural network workloads. It does not present new experimental results; instead, it synthesizes and contextualizes findings previously reported in our SENECA publications to provide a coherent, step-by-step architectural perspective for students and practitioners who wish to design their own digital neuromorphic processors.

Tor is a widely used anonymity network that conceals user identities by routing traffic through encrypted relays, yet it remains vulnerable to traffic correlation attacks that deanonymize users by matching patterns in ingress and egress traffic. However, existing correlation methods suffer from two major limitations: limited robustness to noise and partial observations, and poor scalability due to computationally expensive pairwise matching. To address these challenges, we propose RECTor, a machine learning-based framework for traffic correlation under realistic conditions. RECTor employs attention-based Multiple Instance Learning (MIL) and GRU-based temporal encoding to extract robust flow representations, even when traffic data is incomplete or obfuscated. These embeddings are mapped into a shared space via a Siamese network and efficiently matched using approximate nearest neighbor (aNN) search. Empirical evaluations show that RECTor outperforms state-of-the-art baselines such as DeepCorr, DeepCOFFEA, and FlowTracker, achieving up to 60% higher true positive rates under high-noise conditions and reducing training and inference time by over 50%. Moreover, RECTor demonstrates strong scalability: inference cost grows near-linearly as the number of flows increases. These findings reveal critical vulnerabilities in Tor's anonymity model and highlight the need for advanced model-aware defenses.

Network Intrusion Detection Systems (NIDS) are essential tools for detecting network attacks and intrusions. While extensive research has explored the use of supervised Machine Learning for attack detection and characterisation, these methods require accurately labelled datasets, which are very costly to obtain. Moreover, existing public datasets have limited and/or outdated attacks, and many of them suffer from mislabelled data. To reduce the reliance on labelled data, we propose AutoGraphAD, a novel unsupervised anomaly detection approach based on a Heterogeneous Variational Graph Autoencoder. AutoGraphAD operates on heterogeneous graphs, made from connection and IP nodes that capture network activity within a time window. The model is trained using unsupervised and contrastive learning, without relying on any labelled data. The reconstruction, structural loss, and KL divergence are then weighted and combined in an anomaly score that is then used for anomaly detection. Overall, AutoGraphAD yields the same, and in some cases better, results than previous unsupervised approaches, such as Anomal-E, but without requiring costly downstream anomaly detectors. As a result, AutoGraphAD achieves around 1.18 orders of magnitude faster training and 1.03 orders of magnitude faster inference, which represents a significant advantage for operational deployment.

Connected cyber-physical systems perform inference based on real-time inputs from multiple data streams. Uncertain communication delays across data streams challenge the temporal flow of the inference process. State-of-the-art (SotA) non-blocking inference methods rely on a reference-modality paradigm, requiring one modality input to be fully received before processing, while depending on costly offline profiling. We propose a novel, neuro-inspired non-blocking inference paradigm that primarily employs adaptive temporal windows of integration (TWIs) to dynamically adjust to stochastic delay patterns across heterogeneous streams while relaxing the reference-modality requirement. Our communication-delay-aware framework achieves robust real-time inference with finer-grained control over the accuracy-latency tradeoff. Experiments on the audio-visual event localization (AVEL) task demonstrate superior adaptability to network dynamics compared to SotA approaches.