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نتیجه جستجو - SDN

تعداد مقالات یافته شده: 49
ردیف عنوان نوع
1 The “Cyber Security via Determinism” Paradigm for a Quantum Safe Zero Trust Deterministic Internet of Things (IoT)
پارادایم «امنیت سایبری از طریق جبرگرایی» برای اینترنت اشیا قطعی (IoT) ایمن صفر کوانتومی-2022
The next-generation Internet of Things (IoT) will control the critical infrastructure of the 21st century, including the Smart Power Grid and Smart Cities. It will also support Deterministic Communications, where ‘deterministic traffic flows’ (D-flows) receive strict Quality-of-Service (QoS) guarantees. A ‘Cybersecurity via Determinism’ paradigm for the next-generation ‘Industrial and Tactile Deterministic IoT’ is presented. A forwarding sub-layer of simple and secure ‘deterministic packet switches’ (D-switches) is introduced into layer-3. This sub-layer supports many deterministic Software Defined Wide Area Networks (SD-WANs), along with 3 new tools for improving cyber security: Access Control, Rate Control, and Isolation Control. A Software Defined Networking (SDN) control-plane configures each D-switch (ie FPGA) with multiple deterministic schedules to support D-flows. The SDN control-plane can embed millions of isolated Deterministic Virtual Private Networks (DVPNs) into layer 3. This paradigm offers several benefits: 1) All congestion, interference, and Distributed Denial-of-Service (DDOS) attacks are removed; 2) Buffer sizes in D-switches are reduced by 1000C times; 3) End-to-end IoT delays can be reduced to ultra-low latencies, i.e., the speed-of-light in fiber; 4) The D-switches do not require Gigabytes of memory to store large IP routing tables; 5) Hardware support is provided in layer 3 for the US NIST Zero Trust Architecture; 6) Packets within a DVPN can be entirely encrypted using Quantum Safe encryption, which is impervious to attacks by Quantum Computers using existing quantum algorithms; 7) The probability of an undetected cyberattack targeting a DVPN can be made arbitrarily small by using long Quantum Safe encryption keys; and 8) Savings can reach $10s of Billions per year, through reduced capital, energy and operational costs.
INDEX TERMS: Cyber security | deterministic, the Internet of Things (IoT) | quantum computing, zero trust | encryption | privacy | Software Defined Networking (SDN) | industrial internet of things (IIoT) | tactile Internet of Things | FPGA | Industry 4.0 | deterministic Internet of Things.
مقاله انگلیسی
2 LAMD: Location-based Alert Message Dissemination scheme for emerging infrastructure-based vehicular networks
LAMD: طرح انتشار پیام هشدار مبتنی بر مکان برای شبکه‌های خودرویی مبتنی بر زیرساخت در حال ظهور-2022
Requiring low dissemination delays and thorough vehicles coverage in the vicinity of an emergency event, Vehicular Ad-hoc NETworks (VANETs) were considered as the most adapted communication network to support alert messages dissemination. With the advent of Cooperative ITS services, emerging vehicular networks are expected to increasingly rely on Vehicle to Infrastructure (V2I) communication links, which are expected to be nominally available, with some transient and time-limited connectivity losses. The presence of V2I links paves the way to centralized network control, which can leverage vehicle-related and contextual information provided by the cloud to make more informed decisions. This paper proposes an effective alert message dissemination procedure called LAMD (Location-based Alert Messages Dissemination) for emerging vehicular networks that combines V2I broadcasts with selected V2V (Vehicle to Vehicle) rebroadcasts. The originality of our scheme lies in the selection process of V2V rebroadcasts, which is based on vehicles’ location regarding predefined rebroadcast points selected by a centralized controller. This leads to very limited collisions, low delivery delays, and high information coverage with insignificant signaling and network overhead compared to legacy VANET based dissemination techniques.
keywords: ارتباطات وسایل نقلیه | انتشار پیام هشدار | برنامه های امن | Vehicular communications | Alert message dissemination | Safety applications | SDN
مقاله انگلیسی
3 A survey on blockchain, SDN and NFV for the smart-home security
مروری بر بلاک چین، SDN و NFV برای امنیت خانه های هوشمند-2022
Due to millions of loosely coupled devices, the smart-home security is gaining the attention of industry professionals, attackers, and academic researchers. The smart home is a typical home where many sensors, actuators, and IoT devices are used to automate home users’ daily activities. Although a smart home provides comfort, safety, and satisfaction to users, it opens up multiple challenging security issues when automating and offering intelligent services. Recent studies have investigated not only blockchain but SDN and NFV to address these challenges. We present a comprehensive survey on blockchain, SDN, and NFV for smart-home security. The paper also proposes a new architecture of the smart-home security. First, we describe the features of the smart home and its current security issues. Next, we outline the characteristics of blockchain, SDN, and NFV, including their contribution to improving the smart-home security. While SDN enhances the management and access control of the home network by providing a programmable controller to home nodes, NFV implements the functions of network appliances (e.g., network monitoring, firewall) as virtual machines and ensures the high availability of the network. Blockchain reinforces IoT data’s privacy, integrity, and security and improves the trust in transactions among untrusted IoT devices. Finally, we discuss open issues and challenges in the field and propose recommendations towards high-level security for the smart home.
Keywords: Smart homes | IoT | Privacy | Security | Trust | Blockchain | SDN | NFV
مقاله انگلیسی
4 Novel Four-Layered Software Defined 5G Architecture for AI-based Load Balancing and QoS Provisioning
نرم افزار جدید چهار لایه معماری 5G تعریف شده برای تعادل بار مبتنی بر هوش مصنوعی و تأمین کیفیت QoS -2020
Software defined 5G network (SD-5G) is an evolving networking technology. The integration of SDN and 5G brings scalability, and efficiency. However, Quality of Service (QoS) provision is still challenging in SD-5G due to improper load balancing, traffic unawareness and so on. To overwhelm these issues this paper designs a novel load balancing scheme using Artificial Intelligence (AI) techniques. Firstly, novel fourlayered SD-5G network is designed with user plane, smart data plane, load balancing plane, and distributed control plane. In the context to 5G, the data transmission rate must satisfy the QoS constraints based on the traffic type such as text, audio, video etc. Thus, the data from the user plane is classified by Smart Traffic Analyzer in the data plane. For traffic analysis, Enriched Neuro-Fuzzy (ENF) classifier is proposed. In the load balancing plane, Primary Load balancer and Secondary Load Balancer are deployed. This plane is responsible for balancing the load among controllers. For controller load balancing, switch migration is presented. Overloaded controller is predicted by Entropy function. Then decision for migration is made by Fitness-based Reinforcement Learning (F-RL) algorithm. Finally, the four-layered SD-5G network is modeled in the NS-3.26. The observations shows that the proposed work improves the SD-5G network in terms of Loss Rate, Packet Delivery Rate, Delay, and round trip time.
Keywords: QoS | software defined 5G network | Artificial intelligence | distributed control plane
مقاله انگلیسی
5 MARVEL: Enabling controller load balancing in software-defined networks with multi-agent reinforcement learning
MARVEL: امکان ایجاد توازن بار کنترل کننده در شبکه های تعریف شده توسط نرم افزار با یادگیری تقویتی چند عامل-2020
The control plane plays a significant role in Software-Defined Networking (SDN). A large SDN usually implements its control plane with several distributed controllers, each controlling a subset of switches and synchronizing with other controllers to maintain a consistent network view. Under the fluctuating network traffic, a static controller- switch mapping relationship could lead to imbalanced workload allocation. Controllers may getoverloaded and reject new requests, eventually reducing the control plane’s request processing ability. Most existing schemes have relied heavily on iterative optimization algorithms to manipulate the mapping relationship between con- trollers and switches, which are either time-consuming or less satisfactory in terms of performance. In this paper, we propose a dynamic controller workload balancing scheme, that is termed MARVEL, based on multi-agent re- inforcement learning for generation of switch migration actions. MARVEL works in two phases: offline training and online decision making. In the training phase, each agent learns how to migrate switches through interacting with the network. In the online phase, MARVEL is deployed to make decisions on migrating switches. Experimen- tal results show that MARVEL outperforms competing existing schemes by improving the control plane’s request processing ability at least 27.3% while using 25% less processing time.
Index Terms: Multi-agent reinforcement learning | Neural networks | Software-defined networking | Switch migration
مقاله انگلیسی
6 SmartFCT: Improving power-efficiency for data center networks with deep reinforcement learning
SmartFCT: بهبود بهره وری انرژی برای شبکه های مرکز داده با یادگیری تقویتی عمیق-2020
Reducing the power consumption of Data Center Networks (DCNs) and guaranteeing the Flow Completion Time (FCT) of applications in DCNs are two major concerns for data center operators. However, existing works cannot realize the two goals together because of two issues: (1) dynamic traffic pattern in DCNs is hard to accurately model; (2) an optimal flow scheduling scheme is computationally expensive. In this paper, we propose SmartFCT, which employs the Deep Reinforcement Learning (DRL) coupled with Software-Defined Networking (SDN) to improve the power efficiency of DCNs and guarantee FCT. SmartFCT dynamically collects traffic distribution from switches to train its DRL model. The well-trained DRL agent of SmartFCT can quickly analyze the complicated traffic characteristics using neural networks and adaptively gen- erate a action for scheduling flows and deliberately configuring margins for different links. Following the gen- erated action, flows are consolidated into a few of active links and switches for saving power, and fine-grained margin configuration for active links avoids FCT violation of unexpected flow bursts. Simulation results show that SmartFCT can guarantee FCT and save up to 12.2% power consumption, compared with the state-of-the-art solutions.
Keywords: Data center networks | Software-Defined networking | Power efficiency | Flow completion time | Deep reinforcement learning
مقاله انگلیسی
7 Multi-layered intrusion detection and prevention in the SDN/NFV enabled cloud of 5G networks using AI-based defense mechanisms
شناسایی و جلوگیری از نفوذ چند لایه در SDN / NFV ابر شبکه های 5G را با استفاده از مکانیسم های دفاعی مبتنی بر هوش مصنوعی فعال می کند-2020
Software defined networking (SDN), network function virtualization (NFV), and cloud computing are receiving significant attention in 5G networks. However, this attention creates a new challenge for security provisioning in these integrated technologies. Research in the field of SDN, NFV, cloud computing, and 5G has recently focused on the intrusion detection and prevention system (IDPS). Existing IDPS solutions are inadequate, which could cause large resource wastage and several security threats. To alleviate security issues, timely detection of an attacker is important. Thus, in this paper, we propose a novel approach that is referred to as multilayered intrusion detection and prevention (ML-IDP) in an SDN/NFV-enabled cloud of 5G networks. The proposed approach defends against security attacks using artificial intelligence (AI). In this paper, we employed five layers: data acquisition layer, switches layer, domain controllers (DC) layer, smart controller (SC) layer, and virtualization layer (NFV infrastructure). User authentication is held in the first layer using the Four-Q-Curve algorithm. To address the flow table overloading attack in the switches layer, the game theory approach, which is executed in the IDP agent, is proposed. The involvement of the IDP agent is to completely avoid a flow table overloading attack by a deep reinforcement learning algorithm, and thus, it updates the current state of all switches. In the DC layer, packets are processed and classified into two classes (normal and suspicious) by a Shannon Entropy function. Normal packets are forwarded to the cloud via the SC. Suspicious packets are sent to the VNF using a growing multiple self-organization map (GM-SOM). The proposed ML-IDP system is evaluated using NS3.26 for different security attacks, including IP Spoofing, flow table overloading, DDoS, Control Plane Saturation, and host location hijacking. From the experiment results, we proved that the ML-IDP with AI-based defense mechanisms effectively detects and prevents attacks.
Keywords: SDN/NFV Cloud of 5G | Multilayered architecture | Intrusion detection and prevention | And artificial intelligence
مقاله انگلیسی
8 Data-driven software defined network attack detection : State-of-the-art and perspectives
تشخیص حمله به شبکه تعریف شده نرم افزار داده محور: حالت پیشرفته و چشم انداز-2020
SDN (Software Defined Network) has emerged as a revolutionary technology in network, a substantial amount of researches have been dedicated to security of SDNs to support their various applications. The paper firstly analyzes State-of-the-Art of SDN security from data perspectives. Then some typical network attack detection (NAD) methods are surveyed, in- cluding machine learning based methods and statistical methods. After that, a novel tensor based network attack detection method named tensor principal component analysis (TPCA) is proposed to detect attacks. After surveying the last data-driven SDN frameworks, a ten- sor based big data-driven SDN attack detection framework is proposed for SDN security. In the end, a case study is illustrated to verify the effectiveness of the proposed framework.
Keywords: Network attack detection | Data-driven | Tensor | Network security | Software defined network (SDN)
مقاله انگلیسی
9 Intelligent Content-Aware Traffic Engineering for SDN: An AI-Driven Approach
مهندسی ترافیک آگاه از محتوای هوشمند برای SDN: رویکرد مبتنی بر هوش مصنوعی-2020
TE is a critical and difficult problem that tries to map traffic with various requirements to paths in dynamic communication networks. The emerging SDN enables centralized TE optimization with a global view. From the architectural viewpoint, ICN facilitates TE from many aspects, such as in-network caching which can reduce redundant traffic and content-awareness which can extract prior knowledge of content type directly. Thus, we leverage ICN to optimize SDN TE. However, ICN brings more complexities and dynamics to the network environment, which makes model-based TE methods inefficient. Inspired by recent advances in applying artificial intelligence techniques to solve complex online control problems, we investigate deep learning for content-awareness and DRL for TE decision. In addition, we propose a parallel online learning mechanism to safely utilize DRL which has trial-and-error nature. Results show that our proposal significantly improves network performance in terms of total network throughput, bandwidth utilization, and load balance.
مقاله انگلیسی
10 QoS provisioning for various types of deadline-constrained bulk data transfers between data centers
تامین کیفیت سرویس برای انواع مختلف انتقال داده های فشرده محدود بین مراکز داده-2020
An increasing number of applications in scientific and other domains have moved or are in active transition to clouds, and the demand for big data transfers between geographically distributed cloudbased data centers is rapidly growing. Many modern backbone networks leverage logically centralized controllers based on software-defined networking (SDN) to provide advance bandwidth reservation for data transfer requests. How to fully utilize the bandwidth resources of the links connecting data centers with guaranteed quality of service for each user request is an important problem for cloud service providers. Most existing work focuses on bandwidth scheduling for a single request for data transfer or multiple requests using the same service model. In this work, we construct rigorous cost models to quantify user satisfaction degree, and formulate a generic problem of bandwidth scheduling for multiple deadline-constrained data transfer requests of different types to maximize the request scheduling success ratio while minimizing the data transfer completion time of each request. We prove this problem to be not only NP-complete but also non-approximable, and hence design a heuristic algorithm. For performance evaluation, we establish a proof-of-concept emulated SDN testbed and also generate large-scale simulation networks. Both experimental and simulation results show that the proposed scheduling scheme significantly outperforms existing methods in terms of user satisfaction degree and scheduling success ratio.
Keywords: Big data | Data center | High-performance networks | Software-defined networking | Bandwidth scheduling
مقاله انگلیسی
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