Deep-Reinforcement-Learning-Based QoS-Aware Secure Routing for SDN-IoT

Guo, Xuancheng; Lin, Hui; Li, Zhiyang; Peng, Min

doi:10.1109/jiot.2019.2960033

Cited by 106 publications

(35 citation statements)

References 38 publications

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“…When a network attack occurs in an SDN, ML can be introduced as a detection technology to dynamically control and route the communication flow. Recently, studies using ML to detect and automatically respond to DDoS attacks, abnormal patterns, and data leaks against IoT networks and devices have increased [60,[189][190][191][192][193][194][195][196][197][198][199].…”

Section: Identification Of Topics In Iot Securitymentioning

confidence: 99%

Current Research Trends in IoT Security: A Systematic Mapping Study

Lee

2021

Mobile Information Systems

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The smart mobile Internet-of-things (IoT) network lays the foundation of the fourth industrial revolution, the era of hyperconnectivity, hyperintelligence, and hyperconvergence. As this revolution gains momentum, the security of smart mobile IoT networks becomes an essential research topic. This study aimed to provide comprehensive insights on IoT security. To this end, we conducted a systematic mapping study of the literature to identify evolving trends in IoT security and determine research subjects. We reviewed the literature from January 2009 to August 2020 to identify influential researchers and trends of keywords. We additionally performed structural topic modeling to identify current research topics and the most promising ones via topic trend estimation. We synthesized and interpreted the results of the systematic mapping study to devise future research directions. The results obtained from this study are useful to understand current trends in IoT security and provide insights into research and development of IoT security.

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Section: Identification Of Topics In Iot Securitymentioning

confidence: 99%

Current Research Trends in IoT Security: A Systematic Mapping Study

Lee

2021

Mobile Information Systems

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“…As SDN-IoT size increases, network attack becomes prevalent. To improve network security while optimizing routing, [35] proposed a deep reinforcement learning-based quality-of-service (QoS)-aware secure routing protocol (DQSP). Five attack nodes were used to generate gray hole and DDoS attack methods with convolutional neural network (CNN) as the training network for the RL agent.…”

Section: Review Of Similar Workmentioning

confidence: 99%

Multi-Agent Reinforcement Learning Framework in SDN-IoT for Transient Load Detection and Prevention

Dake¹,

Gadze²,

Klogo³

et al. 2021

Technologies

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The fast emergence of IoT devices and its accompanying big and complex data has necessitated a shift from the traditional networking architecture to software-defined networks (SDNs) in recent times. Routing optimization and DDoS protection in the network has become a necessity for mobile network operators in maintaining a good QoS and QoE for customers. Inspired by the recent advancement in Machine Learning and Deep Reinforcement Learning (DRL), we propose a novel MADDPG integrated Multiagent framework in SDN for efficient multipath routing optimization and malicious DDoS traffic detection and prevention in the network. The two MARL agents cooperate within the same environment to accomplish network optimization task within a shorter time. The state, action, and reward of the proposed framework were further modelled mathematically using the Markov Decision Process (MDP) and later integrated into the MADDPG algorithm. We compared the proposed MADDPG-based framework to DDPG for network metrics: delay, jitter, packet loss rate, bandwidth usage, and intrusion detection. The results show a significant improvement in network metrics with the two agents.

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“…It has been proved in these papers that deep learning methods can be used to design the routing strategy in WSNs. A Deep Reinforcement Learning (DRL)-based Quality-of-Services (QoS) routing strategy was proposed in [17] to handle the secure routing problem in a software-defined network. A deep-learning-based link prediction model was proposed in [18].…”

Section: Introductionmentioning

confidence: 99%

Deep Reinforcement Learning-Based Multi-Hop State-Aware Routing Strategy for Wireless Sensor Networks

et al. 2021

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With the development of wireless sensor network technology, the routing strategy has important significance in the Internet of Things. An efficient routing strategy is one of the fundamental technologies to ensure the correct and fast transmission of wireless sensor networks. In this paper, we study how to combine deep learning technology with routing technology to propose an efficient routing strategy to cope with network topology changes. First, we use the recurrent neural network combined with the deep deterministic policy gradient method to predict the network traffic distribution. Second, the multi-hop node state is considered as the input of a double deep Q network. Therefore, the nodes can make routing decisions according to the current state of the network. Multi-hop state-aware routing strategy based on traffic flow forecasting (MHSA-TFF) is proposed. Simulation results show that the MHSA-TFF can improve transmission delay, average routing length, and energy efficiency.

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Deep-Reinforcement-Learning-Based QoS-Aware Secure Routing for SDN-IoT

Cited by 106 publications

References 38 publications

Current Research Trends in IoT Security: A Systematic Mapping Study

Current Research Trends in IoT Security: A Systematic Mapping Study

Multi-Agent Reinforcement Learning Framework in SDN-IoT for Transient Load Detection and Prevention

Deep Reinforcement Learning-Based Multi-Hop State-Aware Routing Strategy for Wireless Sensor Networks

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