Chronology of the development of Active Queue Management algorithms of RED family. Part 1: from 1993 up to 2005

This work is the first part of a large bibliographic review of active queue management algorithms of the Random Early Detection (RED) family, presented in the scientific press from 1993 to 2023. The first part will provide data on algorithms published from 1993 to 2005

CBSeq: A Channel-level Behavior Sequence For Encrypted Malware Traffic Detection

Machine learning and neural networks have become increasingly popular solutions for encrypted malware traffic detection. They mine and learn complex traffic patterns, enabling detection by fitting boundaries between malware traffic and benign traffic. Compared with signature-based methods, they have higher scalability and flexibility. However, affected by the frequent variants and updates of malware, current methods suffer from a high false positive rate and do not work well for unknown malware traffic detection. It remains a critical task to achieve effective malware traffic detection. In this paper, we introduce CBSeq to address the above problems. CBSeq is a method that constructs a stable traffic representation, behavior sequence, to characterize attacking intent and achieve malware traffic detection. We novelly propose the channels with similar behavior as the detection object and extract side-channel content to construct behavior sequence. Unlike benign activities, the behavior sequences of malware and its variant's traffic exhibit solid internal correlations. Moreover, we design the MSFormer, a powerful Transformer-based multi-sequence fusion classifier. It captures the internal similarity of behavior sequence, thereby distinguishing malware traffic from benign traffic. Our evaluations demonstrate that CBSeq performs effectively in various known malware traffic detection and exhibits superior performance in unknown malware traffic detection, outperforming state-of-the-art methods.Comment: Submitted to IEEE TIF

A QoS-Based Fairness-Aware BBR Congestion Control Algorithm Using QUIC

Congestion control is a fundamental technology to balance the traffic load and the network. The Internet Engineering Task Force (IETF) Quick UDP Internet Connection (QUIC) protocol has flexible congestion control and at the same time possesses the advantages of high efficiency, low latency, and easy deployment at the application layer. Bottleneck bandwidth and round-trip propagation time (BBR) is an optional congestion control algorithm adopted by QUIC. BBR can significantly increase throughput and reduce latency, in particular over long-haul paths. However, BBR results in high packet loss in low bandwidth and low fairness in multi-stream scenarios. In this article, we propose the enhanced BBR congestion control (eBCC) algorithm, which improves the BBR algorithm in two aspects: (1) 10.87% higher throughput and 74.58% lower packet loss rate in the low-bandwidth scenario and (2) 8.39% higher fairness in the multi-stream scenario. This improvement makes eBCC very suitable for IoT communications to provide better QoS services

ARQUITECTURAS DE REDES DE COMPUTADORAS DEFINIDAS POR SOFTWARE: REVISIÓN BIBLIOGRÁFICA

Nos encontramos en el umbral de la quinta revolución industrial (5RI), donde emerge una tecnología dinámica, adaptable, escalable y rentable que desvincula las funciones de control y datos en la infraestructura de la red. Las Redes Definidas por Software (SDN), por sus siglas en inglés) adoptan una arquitectura que proporciona la programabilidad y centralización de la red. Esta investigación se centrará en el núcleo o cerebro de las SDN, conocida como controladores, son aquellos que poseen la inteligencia basada en software y está compuesta por tres capas: Infraestructura, Control y Aplicaciones. Según estudios previos y las recomendaciones de la Open Networking Foundation (ONF), se detalla los componentes, funcionamiento y elementos para su selección. El despliegue en un entorno simulado basado en los indicadores de rendimiento, presentó resultados que permitirán a los administradores de red seleccionar el óptimo controlador según sus requerimientos. Los resultados más notables, indican que la arquitectura SDN soportado con el controlador ONOS presenta mejor rendimiento.Trabajo de investigació

Intelligent beam blockage prediction for seamless connectivity in vision-aided next-generation wireless networks

The upsurge in wireless devices and real-time service demands force the move to a higher frequency spectrum. Millimetre-wave (mmWave) and terahertz (THz) bands combined with the beamforming technology offer significant performance enhancements for future wireless networks. Unfortunately, shrinking cell coverage and severe penetration loss experienced at higher spectrum render mobility management a critical issue in high-frequency wireless networks, especially optimizing beam blockages and frequent handover (HO). Mobility management challenges have become prevalent in city centres and urban areas. To address this, we propose a novel mechanism driven by exploiting wireless signals and on-road surveillance systems to intelligently predict possible blockages in advance and perform timely HO. This paper employs computer vision (CV) to determine obstacles and users’ location and speed. In addition, this study introduces a new HO event, called block event (BLK), defined by the presence of a blocking object and a user moving towards the blocked area. Moreover, the multivariate regression technique predicts the remaining time until the user reaches the blocked area, hence determining best HO decision. Compared to conventional wireless networks without blockage prediction, simulation results show that our BLK detection and proactive HO algorithm achieves 40% improvement in maintaining user connectivity and the required quality of experience (QoE)