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RL-Shield: Mitigating Target Link-Flooding Attacks Using SDN and Deep Reinforcement Learning routing algorithm

IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING 2022年第6期19卷 4052-4067页

作者： Rezapour, Amir Tzeng, Wen-Guey Natl Yang Ming Chiao Tung Univ Dept Comp Sci Taipei 30010 Taiwan

Link-flooding attacks (LFAs) are a new type of distributed denial-of-service (DDoS) attacks that can substantially damage network connectivity. LFAs flows are seemingly legitimate at the origin. But their cumulative volume at critical links causes congestion. We propose RL-Shield, a reinforcement learning based defense system against LFAs. It mitigates LFAs and, at the same time, effectively forwards data traffic in the network. RL-Shield introduces a new detection algorithm for monitoring IP behaviors using the Dirichlet distribution and Bayesian statistics. It monitors the interplay of LFAs and traffic engineering and identifies source IPs that persistently react to re-routing events. The detection algorithm controls two reinforcement learning based routing algorithms that use a hop-by-hop technique to connect related node *** evaluate RL-Shield on various network topologies by simulating several attack strategies. The simulation results demonstrate the effectiveness and high-accuracy of RL-Shield.

关键词： routing Reinforcement learning Delays Throughput Monitoring Bandwidth Network topology DDoS Link-flooding attacks routing algorithm software defined networks deep reinforcement learning

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A DTN Oriented Adaptive routing algorithm Based on Node Load

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China Communications 2022年第12期19卷 54-63页

作者： Hongyan Cui Diyue Chen Roy E.Welsch State Key Laboratory of Networking and Switching Technology Beijing University of Posts and TelecommunicationsBeijing 100876China Beijing Laboratory of Advanced Information Networks Beijing University of Posts and TelecommunicationsBeijing 100876China Massachusetts Institute of Technology CambridgeMA 02139USA

In recent years,Delay Tolerant Networks(DTN)have received more and more *** the same time,several existing DTN routing algorithms generally have disadvantages such as poor scalability and inability to perceive changes in the network *** paper proposes an AdaptiveSpray routing *** algorithm can dynamically control the initial maximum message copy number according to the cache occupancy rate of the node itself,and the cache occupancy rate is added as an impact factor to the calculation of the probability of each node meeting the destination *** the forwarding phase,the node will first compare the meeting probability of itself and the meeting node to the destination node,and then choose different forwarding *** simulation shows that the AdaptiveSpray algorithm proposed in this paper has obvious advantages compared with the existing routing algorithms in terms of message delivery rate and average delay.

关键词： DTN routing algorithm adaptive cache management

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RAPAR: routing algorithm based on node relationship mining in opportunistic network

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PEER-TO-PEER NETWORKING AND APPLICATIONS 2022年第4期15卷 1953-1963页

作者： Yuan, Peiyan Shao, Saike Huang, Xiaoyan Henan Normal Univ Sch Comp & Informat Engn Xinxiang 453007 Henan Peoples R China Big Data Engn Lab Teaching Resources Xinxiang Henan Peoples R China

Information-assisted routing algorithms improve the performance of mobile opportunistic networks (MONs), where node utility and packet redundancy are used in the data forwarding process. Making a tradeoff between the forwarding performance and network overhead is the key problem in MONs. In this study, we propose a multiple-copy routing scheme based on node relationship mining. This scheme considers the influence of the difference and dynamic variability of the relationship between nodes on the routing packets. First, it uses the complex contact information of the nodes to reflect the social relationship of the nodes. Second, to further reduce the network overhead, the relationship model is constructed using the Apriori algorithm to calculate the social forwarding utility of the node. The experimental results show that the proposed scheme can achieve better network performance. This effectively reduces the network overhead on the premise of higher delivery rate and lower transmission delay.

关键词： Apriori Opportunistic networks routing algorithm Social relationship

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Route Packets Uneven, Consume Energy Even: A Lifetime Expanding routing algorithm for WSNs

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WIRELESS PERSONAL COMMUNICATIONS 2022年第4期125卷 3133-3151页

作者： Kamarei, Meisam Tavakoli, Fatemeh Patooghy, Ahmad Univ Appl Sci & Technol Dept Comp Sci Tehran Iran Islamic Azad Univ Dept Comp Sci Aligoudarz Branch Aligudarz Iran North Carolina A&T State Univ Dept Comp Syst Technol Greensboro NC 27411 USA

Wireless Sensor Networks (WSNs) are infrastructure-free networks consisting of tiny and simple environmental sensing devices. Sensor nodes collaborate through wireless and limited-range communication links to report environmental conditions to the network base-station (BS). These features need to establish efficient routing algorithms in WSNs to optimize various parameters of WSNs, including energy consumption, end-to-end delay, network lifetime, and network congestion. This paper proposes an efficient routing algorithm to evenly consume energy over the network area that eventually expands the network lifetime. The proposed algorithm directs an event occurrence report toward the BS in a multi-hop manner. Intermediate sensor nodes decide about the next receiving node of the report using a lightweight optimization process based on the network's mean residual energy, sensors' distance to BS, and the positions of neighboring sensors. Evaluations show that the proposed algorithm directs traffic toward the boundaries of the network, which leads to better balance in traffic and energy consumption network-wide. The proposed algorithm reduces the standard deviation of the sensor node residual energy up to 6x and, through this, expands the network lifetime by at least 36%.

关键词： Binary particle swarm optimization algorithm (BPSO) routing algorithm Wireless sensor networks

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A Q-Learning-Based Fault-Tolerant and Congestion-Aware Adaptive routing algorithm for Networks-on-Chip

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IEEE EMBEDDED SYSTEMS LETTERS 2022年第4期14卷 203-206页

作者： Liu, Yi Guo, Rujia Xu, Changqing Weng, Xiaodong Yang, Yintang Xidian Univ Sch Microelect Xian 710071 Peoples R China Xidian Univ Guangzhou Inst Technol Xian 710071 Peoples R China

Link failure and network congestion have a serious impact on the performance degradation of network on chip (NoC). Adaptive routing algorithms, which have the capability of fault tolerance and congestion awareness, can make NoCs adapt to complex and changeable applications. The proposed algorithm in this letter is an adaptive routing algorithm based on Q -learning. By introducing H - Q table to learn the congestion and fault information of the paths between the source node and the destination node, it can avoid the unnecessary detour of data in the fault area and select the path with less congestion. Aiming at the problem caused by the untimely updating of some values in Q -learning, the Q -value decay and dynamic learning rate mechanism are proposed. Experimental results demonstrate that the proposed routing algorithms still can obtain a good performance under a high failure rate (Fault rate > 25%) with modest extra hardware overhead.

关键词： Congestion aware fault tolerant network on chip (NoC) routing algorithm

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A Multi-Region Division routing algorithm Based on Fuzzy-Shortest-Path-First for LEO Satellite Networks 19

A Multi-Region Division Routing Algorithm Based on Fuzzy-Sho...

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19th IEEE International Wireless Communications and Mobile Computing (IEEE IWCMC)

作者： Li, Shangyi Wang, Fu Yao, Haipeng Dong, Ze Dong, Tao Yin, Jie Beijing Inst Technol Sch Informat & Elect Beijing Peoples R China Beijing Univ Posts Telecommun BUPT Sch Elect & Engn Beijing Peoples R China Space Star Technol Co Ltd State Key Lab Space Ground Integrated Informat Te Beijing Peoples R China

ISBN: (纸本)9798350333398

As an important complement to the terrestrial network and an essential component of the future 6G, Low Earth Orbit (LEO) satellite network is expected to provide higher-quality communication services in combination with terrestrial network and attracts widespread research interest. Since the unbalanced distribution of terrestrial services may lead to inter-satellite link (ISL) congestion, balancing the network load has become one of the key issues for LEO satellite networks. In order to prevent ISL congestion in LEO networks, we propose a multi-region division routing algorithm based on fuzzy-shortest-path-first (FSPF-MDR) for LEO satellite networks. We divide the satellite network into multiple small regions and share link information within the regions to achieve congestion avoidance. Simulation results show that, using the proposed algorithm, the computing complexity is reduced significantly with a slightly cost of increasing average hop count. Furthermore, it is able to achieve congestion prevention while using fewer resources. With different scales of LEO networks, the computing complexity can be reduced by 45% to 70%.

关键词： LEO satellite networks routing algorithm region division load balancing

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GAPPO - A Graph Attention Reinforcement Learning based Robust routing algorithm 34

GAPPO - A Graph Attention Reinforcement Learning based Robus...

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IEEE 34th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

作者： Li, Xinyuan Xiao, Yang Liu, Sixu Lu, Xucong Liu, Fang Zhou, Wenli Liu, Jun Beijing Univ Posts & Telecommun Sch Artificial Intelligence Beijing Peoples R China

ISBN: (纸本)9781665464833

routing algorithms, which determine how to deliver traffic from the source to the destination, are essential for next-generation networks and the internet. To make optimal routing decisions in complex network environments, researchers have leveraged Deep Reinforcement Learning (DRL) to design next-generation routing mechanisms. However, most existing DRLbased routing algorithms are implemented using traditional Neural Networks (NN), which lack robustness against topology changes. In this paper, we propose a novel algorithm called GAPPO, which integrates Graph Attention Network (GAT) and Proximal Policy Optimization (PPO) to optimize routing policies with the objective of minimizing end-to-end (E2E) latency in networks affected by link failures. To evaluate the performance of the proposed algorithm, we conduct a series of experiments on dynamically changing topologies with link failures under different loads. Experimental results demonstrate that GAPPO outperforms benchmark algorithms in both simulated and real-world networks, confirming its powerful robustness against link failures.

关键词： Deep Reinforcement Learning Graph Neural Network routing algorithm Attention Robustness

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An enhanced routing algorithm based on a re-position particle swarm optimization (RA-RPSO) for wireless sensor network

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JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES 2022年第10期34卷 10304-10318页

作者： Elshrkawey, Mohamed Al-Mahdi, Hassan Atwa, Walid Suez Canal Univ Fac Comp & Informat Dept Informat Syst Ismailia 41522 Egypt Suez Canal Univ Fac Comp & Informat Dept Comp Sci Ismailia 41522 Egypt Menoufia Univ Fac Comp & Informat Dept Comp Sci Menoufia Egypt

Energy saving is a major requirement in the design of routing algorithms to maximize the throughput and lifetime of wireless sensor networks. To address this requirement, this work proposes a routing algorithm based on a novel Reposition Particle Swarm Optimization (RPSO) algorithm and a new fitness function. The role of the RPSO algorithm is to salvage particles that may have fallen into local minima, if any. The algorithm is evaluated using three types of benchmark functions, namely unimodal, multimodal, and rotated multimodal. The evaluation results demonstrate that RPSO outperforms native Particle Swarm Optimization and Particle Swarm Optimization with Levy Flight in terms of convergence speed and global optimum identification. The proposed RPSO is then employed to develop an efficient routing algorithm. The routing algorithm is tested in terms network lifetime, number of dead sensor nodes, energy consumption, and number of packets delivered to base station, against four competitive MetaHeuristic algorithms. The test results clearly indicate that the routing algorithm outperforms all the competitive algorithms for all the performance metrics. In particular, it achieves 14% to 29% higher network lifetime, 52% to 58% less dead sensor nodes, 61% to 70% less energy consumption, and 13% to 36% more throughput.

关键词： PSO Mult-objective function Wireless sensor network Clustering routing algorithm Power consumption

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Multi-user routing algorithm for indoor spaces-Adapted for social distancing

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JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES 2022年第9期34卷 7045-7058页

作者： Alamri, Abdullah Alturki, Rayan Ali Alamri, Sultan Univ Jeddah Coll Comp Sci & Engn Jeddah Saudi Arabia Saudi Elect Univ Coll Comp & Informat Riyadh Saudi Arabia

The development of techniques and systems for indicating routes in outdoor and indoor spaces has received a great deal of attention. While outdoor route planning is based on criteria such as the shortest, fastest route, and the least number of turns, indoor route planning is primarily focused on accessibility and safety. routing systems for indoor spaces have become the focus of many researchers due to significant differences that make indoor routing is more complex than outdoor routing. An indoor environment is complex, which makes it difficult to find a route due to closed corridors, multiple floors, and other indoor features. Because of the present global scenario following the advent of the COVID-19 pandemic, indoor routing has become increasingly crucial. Wi-Fi, Bluetooth, and RFID are among the technologies used for indoor routing. For these technologies to help and guide users to the optimal route to their chosen destinations, they require accurate information, appropriate processing and modeling, and route density monitoring to ensure social distancing. In this paper, a new multi-user routing algorithm for indoor spaces is proposed. It has been adapted to the need for social distancing, and is based on multiple users, allowing more than one user to take the route or separate routes at one time without causing congestion on the same route. Despite the complexity of the system, the evaluation indicated that the proposed algorithm successfully controlled the flow of moving objects while ensuring social distancing and maintaining low costs. (c) 2022 The Author(s). Published by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY license (http://***/licenses/by/4.0/).

关键词： routing algorithm Indoor space Spatial databases Social distancing COVID-19 Indoor technology

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A fuzzy-based fast routing algorithm with guaranteed latency-throughput over software defined networks

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JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES 2022年第10期34卷 8221-8233页

作者： Li, Hongsheng Osmani, Amjad Aziz, Amira Sayed A. Huanghuai Univ Sch Intelligent Manufacture Zhumadian 463000 Henan Peoples R China Islamic Azad Univ Dept Comp Engn Qazvin Branch Qazvin Iran Future Univ Egypt Fac Comp & Informat Technol Digital Media Dept New Cairo 11835 Egypt

With the development of online communications over the Internet, the network is required to guarantee the Quality of Service (QoS) requirements for different traffic flows. High quality and fast communication can be provided by Software-Defined Networks (SDN). SDN is a promising architecture for efficient man-agement of network resources by separating control logic from data planes. In order to ensure the QoS requirements of traffic flows in SDN, this paper proposes a Fuzzy-based fast and efficient routing algo-rithm with guaranteed Latency-Throughput (FRLR). The FRLR develops the dynamic routing problem in SDN using a traffic flow classification mechanism based on a fuzzy system. The purpose of the fuzzy sys-tem is to efficiently distribute resource allocation to improve the handling of future requests. In addition, the FRLR classifies traffic flows based on latency to reduce computational complexity. Besides, FRLR can effectively direct traffic flows and reduce network energy consumption by identifying critical links and selecting appropriate paths. Simulations on standard topologies with different scenarios confirm the bet-ter performance of FRLR by guaranteeing QoS requirements. The FRLR classifies traffic flows efficiently and offers significant improvements in the scalability and throughput of the network. Also, FRLR has pro-vided superior results compared to some state-of-the-art algorithms such as MDA, MDWCRA, FARP, ECMP, and QI-RM. Experimental results show that FRLR works on average between 7% and 24% better than other algorithms.(c) 2022 The Authors. Published by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY license (http://***/licenses/by/4.0/).

关键词： Software defined networks Latency Throughput Traffic flows Fuzzy systems routing algorithm

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