Recursive‐clustering‐based approach for denial of service (DoS) attacks in wireless sensors networks

This paper presents a novel approach for detecting denial of service attacks. In particular, the concern is on the sleeping deprivation attacks such as the malicious nodes that use flooding technique. Our approach is based on wireless sensor network (WSN) clustering. It consists in recursively clustering sensors until a required granularity (chosen by the expert) is achieved. We apply our approach with two different clustering algorithms. Indeed, we use the common clustering WSN algorithm Low Energy Algorithm Adaptive Clustering Hierarchy and the general clustering method Fast and Flexible Unsupervised Clustering Algorithm (FFUCA) based on ultrametric properties. We discuss the behavior of the approach with the two algorithms. Also, we present numerical results that show the efficiency of recursive clustering using the FFUCA algorithm. Copyright © 2013 John Wiley & Sons, Ltd.     

面向5G无线接入业务的传输前传组网研究与实践

为了满足5G大带宽低延时广连接的业务特性,深入研究了C-RAN基站接入传输组网模式,并全面开展了部署和实践。针对全部基站接入业务,统一采用C-RAN传输前传组网模式,实现了降低OPEX和CAPEX的目标,同时提升了网络稳定性,解决了业务潮汐效应,加强了面向未来网络演进的快速部署能力。     

Attack modeling and intrusion detection system for 5G wireless communication network

Research has been going around the globe to overcome the challenges that are associated with the increase in the number of users, such as interference management, load sharing, and increased capacity. 5G is emerging as a budding prospect for fulfilling demand and overcoming these challenges. For meeting the increased demands, new technologies such as relays in device‐to‐device communication and small cell access points have been introduced. However, these introductions have opened up security issues in the 5G wireless communication networks. This article focuses on security issues of the 5G wireless communication networks and analyzes the effect of a bandwidth spoofing attack using game theory on the small cell access point in 5G wireless communication network. This article also proposes an adaptive intrusion detection system using a hidden Markov Model for detecting an intrusion on small cell access point in a 5G wireless communication networks.     

5G行业无线专网技术与业务需求模型

5G行业无线专网已成为运营商基于5G技术赋能垂直行业的最重要产品服务之一。虚拟化、多接入边缘计算、网络切片等技术的逐渐成熟为5G行业无线专网的发展提供了关键技术保障。介绍了5G行业无线专网的发展现状、关键技术、需求模型、面临挑战和发展建议。对5G行业无线专网的概念和分类进行归纳,对实现5G行业无线专网的关键技术进行分析,并针对业务需求模型提出了设计思路和主要参考指标,最后对运营商开展5G行业无线专网类业务当前面临的挑战进行了总结并给出发展建议。     

Energy-efficient resource allocation model for device-to-device communication in 5G wireless personal area networks

In general, there are several many devices that can overload the network and reduce performance. Devices can minimize interference and optimize bandwidth usage by using directional antennas and by avoiding overlapping communication ranges. In addition, devices need to carefully manage their use of resources, such as bandwidth and energy. Bandwidth is limited in wireless personal area networks (WPANs), so devices need to carefully select which data to send and receive. In this paper, an intelligent performance analysis of energy-efficient resource optimization model has been proposed for device-to-device (D2D) communication in fifth-generation (5G) WPAN. The proposed energy-efficient resource allocation in D2D communication is important because it helps reduce energy consumption and extend the lifespan of devices that are communicating with each other. By allocating resources in an efficient manner, communication between two devices can be optimized for maximum efficiency. This helps reduce the amount of energy needed to power the communication, as well as the amount of energy needed to power the device that is communicating with another device. Additionally, efficient resource allocation helps reduce the overall cost of communication, as the use of fewer resources results in a lower overall cost. The proposed efficient resource allocation helps reduce the environmental impact of communication, as less energy is used for communication. The proposed energy-efficient resource allocation model (EERAM) has reached 92.97% of energy allocation, 88.72% of power allocation, 87.79% of bandwidth allocation, 87.93% of spectrum allocation, 88.43% of channel allocation, 25.47% of end-to-end delay, 94.33% of network data speed, and 90.99% of network throughput.     

Energy and spectral efficiency optimization using probabilistic based spectrum slicing (PBSS) in different zones of 5G wireless communication network

Telecommunication Systems - Spectrum Slicing is arising as an important notion for 5G wireless network as it helps in increasing the data rate, capacity and therefore energy efficiency and spectral...     

Implementation of Intrusion Detection System using Adaptive Neuro-Fuzzy Inference System for 5G wireless communication network

In the present scenario, the demand for extended coverage, low latency and increased data rate is the need of the hour. Many emerging 5G technologies like massive Multiple Input Multiple Output and device to device communication are introduced in order to meet these increasing demands. For extending the coverage region, new wireless components are introduced in the network like relays, Small Cell Access Point and hotspots. But these components are highly vulnerable to security breaches and thus provides an entry point for the intruder to enter into the network. In this paper, a general 5G wireless communication network with an incorporated relay is proposed. This paper focuses on the implementation of Intrusion Detection System using Adaptive Neuro-Fuzzy Inference System using KDD cup 99 data set for detecting an attack on the relay. The effect of varying the membership function and learning algorithms have also been analyzed.     

A game-theoretic analysis of denial of service attacks in wireless random access

In wireless access, transmitter nodes need to make individual decisions for distributed operation and do not necessarily cooperate with each other. We consider a single-receiver random access system of non-cooperative transmitters with the individual objectives of optimizing their throughput rewards, transmission energy costs and delay costs. The non-cooperative transmitter behavior may be purely selfish or may also reflect malicious objectives of generating interference to prevent the successful transmissions of the other nodes as a form of denial of service attack. Our goal is to evaluate the interactions between selfish and malicious nodes that have the dual objectives of optimizing their individual performance measures and blocking the packet transmissions of the other selfish nodes. We assume saturated packet queues of infinite buffer capacities and consider a general multi-packet reception channel that allows packet captures in the presence of simultaneous transmissions. In this context, we formulate a non-cooperative random access game of selecting the individual probabilities of transmitting packets to a common receiver. We derive the non-cooperative transmission strategies in Nash equilibrium. The analysis provides insights for the optimal strategies to block random access of selfish nodes as well as the optimal defense mechanisms against the possible denial of service attacks of malicious nodes in wireless networks. The results are also compared with the cooperative equilibrium strategies that optimize the total system utility (separately under random access and scheduled access). A pricing scheme is presented to improve the non-cooperative operation. For distributed implementation, we formulate a repeated game of the best-response strategy updates and introduce adaptive heuristics (based on the channel feedback only) provided that the system parameters are not explicitly known at the individual transmitters.

5G-ZOOM-Game: small cell zooming using weighted majority cooperative game for energy efficient 5G mobile network

Wireless Networks - The rapid escalation of user traffic and service innovation has made the deployment of small cell base stations essential for eventually decreasing energy consumption in future...     

TPAAD: Two-phase authentication system for denial of service attack detection and mitigation using machine learning in software-defined network

Software-defined networking (SDN) has received considerable attention and adoption owing to its inherent advantages, such as enhanced scalability, increased adaptability, and the ability to exercise centralized control. However, the control plane of the system is vulnerable to denial-of-service (DoS) attacks, which are a primary focus for attackers. These attacks have the potential to result in substantial delays and packet loss. In this study, we present a novel system called Two-Phase Authentication for Attack Detection that aims to enhance the security of SDN by mitigating DoS attacks. The methodology utilized in our study involves the implementation of packet filtration and machine learning classification techniques, which are subsequently followed by the targeted restriction of malevolent network traffic. Instead of completely deactivating the host, the emphasis lies on preventing harmful communication. Support vector machine and K-nearest neighbours algorithms were utilized for efficient detection on the CICDoS 2017 dataset. The deployed model was utilized within an environment designed for the identification of threats in SDN. Based on the observations of the banned queue, our system allows a host to reconnect when it is no longer contributing to malicious traffic. The experiments were run on a VMware Ubuntu, and an SDN environment was created using Mininet and the RYU controller. The results of the tests demonstrated enhanced performance in various aspects, including the reduction of false positives, the minimization of central processing unit utilization and control channel bandwidth consumption, the improvement of packet delivery ratio, and the decrease in the number of flow requests submitted to the controller. These results confirm that our Two-Phase Authentication for Attack Detection architecture identifies and mitigates SDN DoS attacks with low overhead.     

5G无线网络智能规划与仿真

传统基于人工经验的规划方法已不能满足5G网络的需求,亟需引入基于大数据分析的5G无线网智能规划方法。提出5G智能站址规划、参数规划和后评估三步闭环规划方案,搭建完成5G无线网络智能规划系统,应用于实际5G无线网规划仿真,高效地支撑面向未来的5G无线网络组网部署,确保资源精准投入,为建设5G精品目标网络奠定基础。     

5G无线通信技术概念及相关应用

5G移动通信是面向2020年及未来移动通信产业需求的新一代无线通信系统,为了解决未来高速率、高数据密度、海量连接设备以及研究热点.本文5G无线通信技术的概念特征进行分析.     

A systematic review on distributed denial of service attack defense mechanisms in programmable networks

Design flaws and vulnerabilities inherent to network protocols, devices, and services make Distributed Denial of Service (DDoS) a persisting threat in the cyberspace, despite decades of research efforts in the area. The historical vertical integration of traditional IP networks limited the solution space, forcing researchers to tweak network protocols while maintaining global compatibility and proper service to legitimate flows. The advent of Software-Defined Networking (SDN) and advances in Programmable Data Planes (PDP) changed the state of affairs and brought novel possibilities to deal with such attacks. In summary, the ability of bringing together network intelligence to a control plane, and offloading flow processing tasks to the forwarding plane, opened up interesting opportunities for network security researchers unlike ever. In this article, we dive into recent research that relies on SDN and PDP to detect, mitigate, and prevent DDoS attacks. Our literature review takes into account the SDN layered view as defined in RFC7426 and focuses on the data, control, and application planes. We follow a systematic methodology to capture related articles and organize them into a taxonomy of DDoS defense mechanisms focusing on three facets: activity level, deployment location, and cooperation degree. From the analysis of existing work, we also highlight key research gaps that may foster future research in the field.     

命名数据网络中的DoS/DDoS攻击研究综述

随着网络的发展,现行的网络结构出现了难以解决的问题。以内容为中心的网络重新设计了网络架构解决了这些问题,命名数据网络(Named Data Network,NDN)是现今典型的以内容为中心的网络,但它的发展也出现了新的安全问题。文中主要分析了传统的DoS/DDoS攻击对NDN网络的影响,对NDN网络中产生的新型DoS/DDoS攻击进行了剖析,并简述了其相应的解决方案。     

5G无线通信系统信道建模的现状和挑战

研究第五代(the 5th Generation, 5G)移动通信场景下的无线信道新特征及其对5G关键使能技术的影响是当前亟待开展的工作.文章从5G应用的主要场景和其中部分关键使能技术出发, 基于国内外相关文献调研结果, 阐述了以高频段通信、大规模天线阵列和终端直接通信技术为背景的无线信道建模的主要需求、当前进展、存在的挑战并指出未来5G信道建模理论发展的五个主要趋势, 可能的研究成果将为相关算法在5G连续广域覆盖场景、热点高容量场景、低功耗大连接场景和低时延高可靠场景下的匹配设计和准确性能评估打下坚实的基础.     

5G网络技术特点分析及无线网络规划的研究

如今,世界已步入5G的科技革命之中,华为更是率先一步,将5G技术在产品中进行融入,好评如潮,从而加强了自身的竞争能力。5G技术是以第五代网络通信技术为切入点,是具有特征性、使用性的现代网络通信技术,其将无线网络作为载体进行信息之间的互相传送,具有现实性的研究意义。文章将5G网络技术作为切入点,对其显著特点以及对无线网络的未来规划进行深入研究,以此为5G技术的完善建设贡献一份微薄之力。     

5G技术在移动通信网络中的应用

文章介绍了5G通信技术的概念和基本特点,通过将5G技术与通信领域的先进技术相结合,以期5G能够为移动通信技术带来新的突破和创新,同时也能够使5G技术在通信技术领域更加完善和系统化。     

Multi-layer neural network algorithm for vehicle-to-everything communication in 5G networks

Internet of Vehicles (IoVs), the emerging trend of Internet of Things (IoTs), has undoubtedly become a promising trend to improve communication among vehicles on the roads. Vehicle-to-everything (V2X) communication that is based on 5G technology enables vehicle users to communicate and collaborate with each other to enhance road traffic efficiency and safety. Owing to the increased traffic load and restricted resources of existing network substructure, a channel that responds to the latency and reliability needs of V2X communication must be designed. Thereby, several intelligent spectrum allocation techniques have been proposed to improve the system's overall effectiveness. In this paper, we discuss the spectrum sharing issue of V2X communication in Device-to-Device (D2D)-based cellular networks. We propose a new multi-layer neural network (MLNN)-based Resource Allocation and sharing approach (MNNRA) for D2D-based V2X communications. According to the main advantages of MLNN, the proposed algorithm takes several profits by improving system performance while reducing computational complexity. Numerical analysis is presented to approve the effectiveness of our proposed solution in terms of network sum rate, packet reception ratio, resource utilization ratio, and time complexity.     

A deep learning approach with Bayesian optimization and ensemble classifiers for detecting denial of service attacks

Detecting malicious behavior is important for preventing security threats in a computer network. Denial of Service (DoS) is among the popular cyber attacks targeted at web sites of high‐profile organizations and can potentially have high economic and time costs. In this paper, several machine learning methods including ensemble models and autoencoder‐based deep learning classifiers are compared and tuned using Bayesian optimization. The autoencoder framework enables to extract new features by mapping the original input to a new space. The methods are trained and tested both for binary and multi‐class classification on Digiturk and Labris datasets, which were introduced recently for detecting various types of DDoS attacks. The best performing methods are found to be ensembles though deep learning classifiers achieved comparable level of accuracy.     

5G行业网无线规划面临的挑战与规划技术探讨

垂直行业的业务需求是一系列子业务需求的集合,各子业务需求之间千差万别,仅以室外上行eMBB为例,需求区间为2～100 Mbit/s,该数值跨度巨大.采用单一上下行边缘速率指标的规划体系,难以满足ToB客户终端级/业务级的高SLA保障需求.本文尝试从研究ToB的室外上下行边缘指标、建立ToB的单用户多维度指标体系、提升小...