arXiv:2504.11575v2 Announce Type: replace-cross 
Abstract: Multi-environment (M-En) networks integrate diverse traffic sources, including Internet of Things (IoT) and traditional computing systems, creating complex and evolving conditions for malicious traffic detection. Existing machine learning (ML)-based approaches, typically trained on static single-domain datasets, often fail to generalize across heterogeneous network environments. To address this gap, we develop a realistic Docker-NS3-based testbed that emulates both IoT and traditional traffic conditions, enabling the generation and capture of live, labeled network flows. The resulting M-En Dataset combines this traffic with curated public PCAP traces to provide comprehensive coverage of benign and malicious behaviors. Building on this foundation, we propose Multi-LF, a real-time continuous learning framework that combines a lightweight model (M1) for rapid detection with a deeper model (M2) for high-confidence refinement and adaptation. A confidence-based coordination mechanism enhances efficiency without compromising accuracy, while weight interpolation mitigates catastrophic forgetting during continuous updates. Features extracted at 1-second intervals capture fine-grained temporal patterns, enabling early recognition of evolving attack behaviors. Implemented and evaluated within the Docker-NS3 testbed on live traffic, Multi-LF achieves an accuracy of 0.999 while requiring human intervention for only 0.0026 percent of packets, demonstrating its effectiveness and practicality for real-time malicious traffic detection in heterogeneous network environments.

تم تطوير إطار العمل Multi-LF لتحسين الكشف في الوقت الحقيقي عن حركة المرور الضارة في الشبكات متعددة البيئات، والتي تشمل إنترنت الأشياء (IoT) والأنظمة التقليدية. يعالج هذا الإطار القيود التي تواجهها أساليب التعلم الآلي الحالية التي تكافح مع مصادر حركة المرور المتنوعة. يحقق الإطار دقة مثيرة للإعجاب تبلغ 0.999 مع الحاجة إلى تدخل بشري ضئيل، مما يجعله تقدمًا كبيرًا في مجال الأمن السيبراني.

El marco Multi-LF se ha desarrollado para mejorar la detección en tiempo real de tráfico malicioso en redes de múltiples entornos, que incluyen IoT y sistemas tradicionales. Aborda las limitaciones de los enfoques de aprendizaje automático que luchan con diversas fuentes de tráfico. El marco logra una impresionante precisión de 0.999 mientras requiere una intervención humana mínima, lo que lo convierte en un avance significativo en ciberseguridad.

Le cadre Multi-LF a été développé pour améliorer la détection en temps réel des trafics malveillants dans des réseaux multi-environnements, incluant l'IoT et les systèmes traditionnels. Il répond aux limitations des approches d'apprentissage automatique qui peinent avec des sources de trafic diverses. Le cadre atteint une précision impressionnante de 0,999 tout en nécessitant une intervention humaine minimale, ce qui en fait une avancée significative en cybersécurité.

The Multi-LF framework has been developed to enhance real-time detection of malicious traffic in multi-environment networks, which include IoT and traditional systems. It addresses the limitations of existing machine learning approaches that struggle with diverse traffic sources. The framework achieves an impressive accuracy of 0.999 while requiring minimal human intervention, making it a significant advancement in cybersecurity.

MULTI-LF: A Continuous Learning Framework for Real-Time Malicious Traffic Detection in Multi-Environment Networks

The SanDisk ExtremeFit USB-C flash drive is barely three grams, but offers 1TB of external storage and impressive speeds.

I refused to believe this coin-sized gadget was a storage drive, until I tried it for myself

The Tabwee T60 Pro is a strong value pick for Android tablet users. Here's how.

Yes, there exists $200 Android tablets that are actually worth the money - this one proves it

We tested the best tablets from brands like Apple, Samsung, and OnePlus. These are our top picks, plus tablet deals you can find for Black Friday.

The best tablets of 2025: Lab-tested recommendations

GPT-5.1-Codex-Max is ready to take on your next massive coding job. Here's what's new.

OpenAI's Codex Max solves one of my biggest AI coding annoyances - and it's a lot faster

Georgia Tech researchers are using AI to quickly train exoskeleton devices, making it much more practical to develop, improve, and ultimately deploy wearable robots for people with impaired mobility.

Real-world helper exoskeletons come closer to reality with AI training

Lenovo's 16-inch Legion Pro 7i is a high-performance beast with a 240Hz OLED display. It's currently over 30% off ahead of Black Friday.

My favorite gaming laptop of 2025 is $1,150 off in this pop-up early Black Friday deal

arXiv:2511.11464v1 Announce Type: cross 
Abstract: The routing protocol for low-power and lossy networks (RPL) has become the de facto routing standard for resource-constrained IoT systems, but its lightweight design exposes critical vulnerabilities to a wide range of routing-layer attacks such as hello flood, decreased rank, and version number manipulation. Traditional countermeasures, including protocol-level modifications and machine learning classifiers, can achieve high accuracy against known threats, yet they fail when confronted with novel or zero-day attacks unless fully retrained, an approach that is impractical for dynamic IoT environments. In this paper, we investigate incremental learning as a practical and adaptive strategy for intrusion detection in RPL-based networks. We systematically evaluate five model families, including ensemble models and deep learning models. Our analysis highlights that incremental learning not only restores detection performance on new attack classes but also mitigates catastrophic forgetting of previously learned threats, all while reducing training time compared to full retraining. By combining five diverse models with attack-specific analysis, forgetting behavior, and time efficiency, this study provides systematic evidence that incremental learning offers a scalable pathway to maintain resilient intrusion detection in evolving RPL-based IoT networks.

تناقش الورقة البحثية ثغرات بروتوكول التوجيه للشبكات منخفضة الطاقة وفقدان البيانات (RPL) الذي يُستخدم على نطاق واسع في أنظمة إنترنت الأشياء ذات الموارد المحدودة. تسلط الضوء على مجموعة متنوعة من الهجمات على مستوى التوجيه، بما في ذلك فيضانات hello، وانخفاض الرتبة، والتلاعب برقم الإصدار. تواجه التدابير التقليدية صعوبات ضد الهجمات الجديدة أو هجمات اليوم صفر دون إعادة تدريب كاملة. يقترح المؤلفون التعلم التدريجي كاستراتيجية تكيفية للكشف عن التسلل في الشبكات المعتمدة على RPL، مع تقييم خمس عائلات من النماذج لتحسين أداء الكشف ضد التهديدات المتطورة.

El artículo discute las vulnerabilidades del Protocolo de Enrutamiento para Redes de Bajo Consumo y Pérdida (RPL), que se utiliza ampliamente en sistemas IoT con recursos limitados. Destaca varios ataques a nivel de enrutamiento, incluyendo inundaciones de hello, disminución de rango y manipulación de números de versión. Las contramedidas tradicionales tienen dificultades contra ataques nuevos o de día cero sin un reentrenamiento completo. Los autores proponen el aprendizaje incremental como una estrategia adaptativa para la detección de intrusiones en redes RPL, evaluando cinco familias de modelos para mejorar el rendimiento de detección frente a amenazas en evolución.

Cet article traite des vulnérabilités du protocole de routage pour les réseaux à faible consommation d'énergie et à perte (RPL), largement utilisé dans les systèmes IoT à ressources limitées. Il met en lumière diverses attaques au niveau du routage, telles que l'inondation de hello, la diminution de rang et la manipulation du numéro de version. Les contre-mesures traditionnelles peinent face aux nouvelles attaques sans réentraînement complet. Les auteurs proposent l'apprentissage incrémental comme stratégie adaptative pour la détection d'intrusions dans les réseaux RPL, en évaluant cinq familles de modèles pour améliorer la performance de détection contre les menaces évolutives.

The paper discusses the vulnerabilities of the Routing Protocol for Low-Power and Lossy Networks (RPL), which is widely used in resource-constrained IoT systems. It highlights various routing-layer attacks, including hello flood, decreased rank, and version number manipulation. Traditional countermeasures struggle against new or zero-day attacks without complete retraining. The authors propose incremental learning as an adaptive strategy for intrusion detection in RPL networks, evaluating five model families to enhance detection performance against evolving threats.

Adaptive Intrusion Detection for Evolving RPL IoT Attacks Using Incremental Learning

arXiv:2511.14268v1 Announce Type: cross 
Abstract: Heterogeneous porous materials play a crucial role in various engineering systems. Microstructure characterization and reconstruction provide effective means for modeling these materials, which are critical for conducting physical property simulations, structure-property linkage studies, and enhancing their performance across different applications. To achieve superior controllability and applicability with small sample sizes, we propose a statistically controllable microstructure reconstruction framework that integrates neural networks with sliced-Wasserstein metric. Specifically, our approach leverages local pattern distribution for microstructure characterization and employs a controlled sampling strategy to generate target distributions that satisfy given conditional parameters. A neural network-based model establishes the mapping from the input distribution to the target local pattern distribution, enabling microstructure reconstruction. Combinations of sliced-Wasserstein metric and gradient optimization techniques minimize the distance between these distributions, leading to a stable and reliable model. Our method can perform stochastic and controllable reconstruction tasks even with small sample sizes. Additionally, it can generate large-size (e.g. 512 and 1024) 3D microstructures using a chunking strategy. By introducing spatial location masks, our method excels at generating spatially heterogeneous and complex microstructures. We conducted experiments on stochastic reconstruction, controllable reconstruction, heterogeneous reconstruction, and large-size microstructure reconstruction across various materials. Comparative analysis through visualization, statistical measures, and physical property simulations demonstrates the effectiveness, providing new insights and possibilities for research on structure-property linkage and material inverse design.

تم اقتراح إطار عمل جديد لإعادة بناء الميكروستركشر للمواد المسامية غير المتجانسة، حيث يتم دمج الشبكات العصبية مع مقياس ووترستين المقطوع. تعزز هذه الطريقة من توصيف وإعادة بناء الميكروستركشر، وهما أمران أساسيان لنمذجة هذه المواد في التطبيقات الهندسية. من خلال استخدام توزيع الأنماط المحلية واستراتيجية أخذ عينات محكومة، يهدف الإطار إلى تحسين القابلية للتحكم والتطبيق في إعادة بناء الميكروستركشر، حتى مع أحجام عينات صغيرة.

Se ha propuesto un nuevo marco para la reconstrucción de la microestructura de materiales heterogéneos porosos, integrando redes neuronales con la métrica de Wasserstein cortada. Este enfoque mejora la caracterización y reconstrucción de la microestructura, que son esenciales para modelar materiales en aplicaciones de ingeniería. Al utilizar la distribución de patrones locales y una estrategia de muestreo controlado, el marco busca mejorar la controlabilidad y aplicabilidad de la reconstrucción de microestructuras, incluso con tamaños de muestra pequeños.

Un nouveau cadre pour la reconstruction de la microstructure des matériaux hétérogènes poreux a été proposé, intégrant des réseaux de neurones avec la métrique de Wasserstein tranchée. Cette approche améliore la caractérisation et la reconstruction de la microstructure, essentielles pour modéliser les matériaux dans les applications d'ingénierie. En utilisant la distribution des motifs locaux et une stratégie d'échantillonnage contrôlé, le cadre vise à améliorer la contrôlabilité et l'applicabilité de la reconstruction de la microstructure, même avec de petites tailles d'échantillons.

A new framework for reconstructing the microstructure of heterogeneous porous materials has been proposed, integrating neural networks with the sliced-Wasserstein metric. This approach enhances microstructure characterization and reconstruction, which are essential for modeling materials in engineering applications. By utilizing local pattern distribution and a controlled sampling strategy, the framework aims to improve the controllability and applicability of microstructure reconstruction, even with small sample sizes.

Statistically controllable microstructure reconstruction framework for heterogeneous materials using sliced-Wasserstein metric and neural networks

arXiv:2408.00540v4 Announce Type: replace-cross 
Abstract: Artificial Intelligence (AI) is being incorporated in several optimization, scheduling, orchestration as well as in native communication network functions. This paradigm shift results in increased energy consumption, however, quantifying the end-to-end energy consumption of adding intelligence to communication systems remains an open challenge since conventional energy consumption metrics focus on either communication, computation infrastructure, or model development. To address this, we propose a new metric, the Energy Cost of AI Lifecycle (eCAL) of an AI model in a system. eCAL captures the energy consumption throughout the development, deployment and utilization of an AI-model providing intelligence in a communication network by (i) analyzing the complexity of data collection and manipulation in individual components and (ii) deriving overall and per-bit energy consumption. We show that as a trained AI model is used more frequently for inference, its energy cost per inference decreases, since the fixed training energy is amortized over a growing number of inferences. For a simple case study we show that eCAL for 100 inferences is 2.73 times higher than for 1000 inferences. Additionally, we have developed a modular and extendable open-source simulation tool to enable researchers, practitioners, and engineers to calculate the end-to-end energy cost with various configurations and across various systems, ensuring adaptability to diverse use cases.

يتناول المقال دمج الذكاء الاصطناعي (AI) في شبكات الاتصال، مشيرًا إلى زيادة استهلاك الطاقة المرتبطة بهذا التحول. يقدم مقياسًا جديدًا يسمى تكلفة الطاقة لدورة حياة الذكاء الاصطناعي (eCAL)، والذي يقيس الطاقة المستخدمة خلال تطوير ونشر واستخدام نماذج الذكاء الاصطناعي في أنظمة الاتصال. تؤكد الدراسة على الحاجة إلى فهم شامل لمقاييس استهلاك الطاقة، التي تركز تقليديًا على الاتصال أو بنية الحوسبة أو تطوير النماذج.

El artículo aborda la integración de la inteligencia artificial (IA) en las redes de comunicación, destacando el aumento del consumo de energía asociado con este cambio. Presenta una nueva métrica llamada Costo Energético del Ciclo de Vida de la IA (eCAL), que cuantifica la energía utilizada durante el desarrollo, implementación y utilización de modelos de IA en sistemas de comunicación. El estudio enfatiza la necesidad de una comprensión integral de las métricas de consumo de energía, que tradicionalmente se centran en la comunicación, infraestructura de computación o desarrollo de modelos.

L'article traite de l'intégration de l'intelligence artificielle (IA) dans les réseaux de communication, soulignant l'augmentation de la consommation d'énergie associée à ce changement. Il présente un nouveau métrique appelé le Coût Énergétique du Cycle de Vie de l'IA (eCAL), qui quantifie l'énergie utilisée lors du développement, du déploiement et de l'utilisation des modèles d'IA dans les systèmes de communication. L'étude met en avant la nécessité d'une compréhension globale des métriques de consommation d'énergie, qui se concentrent traditionnellement sur la communication, l'infrastructure…

The article discusses the integration of Artificial Intelligence (AI) into communication networks, highlighting the increased energy consumption associated with this shift. It presents a new metric called the Energy Cost of AI Lifecycle (eCAL), which quantifies the energy used during the development, deployment, and utilization of AI models in communication systems. The study emphasizes the need for a comprehensive understanding of energy consumption metrics, which traditionally focus on communication, computation infrastructure, or model development.

The Energy Cost of Artificial Intelligence Lifecycle in Communication Networks

arXiv:2511.14465v1 Announce Type: new 
Abstract: Mechanistic interpretability research requires reliable tools for analyzing transformer internals across diverse architectures. Current approaches face a fundamental tradeoff: custom implementations like TransformerLens ensure consistent interfaces but require coding a manual adaptation for each architecture, introducing numerical mismatch with the original models, while direct HuggingFace access through NNsight preserves exact behavior but lacks standardization across models. To bridge this gap, we develop nnterp, a lightweight wrapper around NNsight that provides a unified interface for transformer analysis while preserving original HuggingFace implementations. Through automatic module renaming and comprehensive validation testing, nnterp enables researchers to write intervention code once and deploy it across 50+ model variants spanning 16 architecture families. The library includes built-in implementations of common interpretability methods (logit lens, patchscope, activation steering) and provides direct access to attention probabilities for models that support it. By packaging validation tests with the library, researchers can verify compatibility with custom models locally. nnterp bridges the gap between correctness and usability in mechanistic interpretability tooling.

يتناول المقال nnterp، وهي أداة جديدة مصممة لتعزيز البحث في التفسير الميكانيكي لنماذج المحولات. تواجه الأساليب الحالية تحديات في التوحيد والدقة العددية عند تحليل هياكل مختلفة. تعمل nnterp كغلاف خفيف حول NNsight، مما يوفر واجهة موحدة لتحليل المحولات مع الحفاظ على تنفيذات HuggingFace الأصلية. تتيح هذه الأداة للباحثين كتابة كود التدخل مرة واحدة وتطبيقه عبر أكثر من 50 نموذجًا متنوعًا من 16 عائلة معمارية، مما يسهل الاختبارات الشاملة للتفسير.

El artículo presenta nnterp, una nueva herramienta diseñada para mejorar la investigación sobre la interpretabilidad mecanicista de los modelos de transformadores. Los métodos actuales enfrentan desafíos en la estandarización y precisión numérica al analizar diferentes arquitecturas. nnterp actúa como un envoltorio ligero alrededor de NNsight, proporcionando una interfaz unificada para el análisis de transformadores mientras mantiene las implementaciones originales de HuggingFace. Permite a los investigadores escribir código de intervención una vez y aplicarlo a más de 50 variantes de modelos …

L'article présente nnterp, un nouvel outil conçu pour améliorer la recherche sur l'interprétabilité mécaniste des modèles de transformateurs. Les méthodes actuelles rencontrent des défis en matière de standardisation et de précision numérique lors de l'analyse de différentes architectures. nnterp agit comme un wrapper léger autour de NNsight, offrant une interface unifiée pour l'analyse des transformateurs tout en maintenant les implémentations originales de HuggingFace. Il permet aux chercheurs d'écrire un code d'intervention une fois et de l'appliquer à plus de 50 variantes de modèles proven…

The article discusses nnterp, a new tool designed to enhance mechanistic interpretability research for transformer models. Current methods face challenges in standardization and numerical accuracy when analyzing different architectures. nnterp serves as a lightweight wrapper around NNsight, providing a unified interface for transformer analysis while maintaining the original HuggingFace implementations. It allows researchers to write intervention code once and apply it across over 50 model variants from 16 architecture families, facilitating comprehensive interpretability testing.

MULTI-LF: A Continuous Learning Framework for Real-Time Malicious Traffic Detection in Multi-Environment Networks

Was this article worth reading? Share it