Machine learning for routing

Rischke, Justus; Sossalla, Peter

doi:10.1016/b978-0-12-820488-7.00031-1

Cited by 6 publications

(2 citation statements)

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“…In addition to IDSs, machine learning is widely used in a variety of communication and networking applications. Rischke and Sossalla 7 proposed a system aimed at minimizing the average flow latency in routing by evaluating different routing configurations using reinforcement learning. This agent used multiple paths in the network instead of relying only on one shortest path.…”

Section: Introductionmentioning

confidence: 99%

ML‐IDSDN: Machine learning based intrusion detection system for software‐defined network

Alzahrani

Alenazi

2022

Concurrency and Computation

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Software-defined networking (SDN) has been developed to separate network control plane from forwarding plane which can decrease operational costs and the time it takes to deploy new services compared to traditional networks. Despite these advantages, this technology brings threats and vulnerabilities. Consequently, developing high-performance real-time intrusion detection systems (IDSs) to classify malicious activities is a vital part of SDN architecture. This article introduces two created datasets generated from SDN using Mininet and Ryu controller with different feature extraction tools that contain normal traffic and different types of attacks (Fin flood, UDP flood, ICMP flood, OS probe scan, port probe scan, TCP bandwidth flood, and TCP syn flood) that is used for training a number of supervised binary classification machine learning algorithms such as k-nearest neighbor, AdaBoost, decision tree (DT), random forest, naive Bayes, multilayer perceptron, support vector machine, and XGBoost. The DT algorithm has achieved high scores to fit a real-time application achieving F1 score on attack class of 0.9995, F1 score on normal class of 0.9983, and throughput score of 6,737,147.275 samples per second with a total number of three features. In addition, using data preprocessing to reduce the model complexity, thereby increasing the overall throughput to fit a real-time system.

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Section: Introductionmentioning

confidence: 99%

ML‐IDSDN: Machine learning based intrusion detection system for software‐defined network

Alzahrani

Alenazi

2022

Concurrency and Computation

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“…Recently, Machine Learning (ML) based routing techniques are widely used in many wireless ad hoc scenarios [3]. Reinforcement learning (RL) is a biologically inspired branch of the ML approach that acquires knowledge through interactions between the agent and environment without external supervision [4] [5].…”

Section: Introductionmentioning

confidence: 99%

A Deep Reinforcement Learning-Based Geographic Packet Routing Optimization

Bai

Zhang

et al. 2022

IEEE Access

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Packet routing is a fundamental problem in wireless networks in which routers decide the next hop for each packet in order to deliver it to its destination as quickly as possible. In order to overcome the shortcoming of non optimal forwarding path caused by fixed forwarding mode in geographic location-based routing algorithm, we investigated a new efficient packet routing strategy which combined with Deep Reinforcement Learning (DRL) algorithm (Proximal Policy Optimization: PPO) to minimize the hops and reduce the probability of encountering "routing hole" during the forwarding of the packets in complex networks. Each node in our network can make routing decisions to learn a routing policy that can not only use the greedy mode to maintain the efficiency of routing policy but also reduce the use of peripheral forwarding mode. The learning process of the DRL agent is based on the status information of the packet being transmitted and information of neighbor nodes within the communication range. Extensive simulations over dynamic network topologies and the number of nodes have shown that our packets routing agent can learn the optimal policy in terms of average packets delivery rate and the number of hops compared with Greedy Perimeter Stateless Routing (GPSR) protocol. The performance of PPO in large-scale action space is also verified, which provides a basis for the future researches to combine PPO technique with packet routing optimization.

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Survey on security issues of routing and anomaly detection for space information networks

Zhuo

Liu

Zhou

et al. 2021

Sci Rep

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Space information networks is network systems that can receive, transmit, and process spatial information lively. It uses satellites, stratosphere airships, Unmanned Aerial Vehicles, and other platforms as the carrier. It supports high-dynamic, real-time broadband transmission of earth observations and ultra-long-distance, long-delay reliable transmission of deep space exploration. The deeper the network integration, the higher the system’s security concerns and the more likely SINs will be controlled and destroyed in terms of cybersecurity. How to integrate new IT technologies such as artificial intelligence, digital twins, and blockchain to diverse application scenarios of SINs while maintaining SIN cybersecurity will be a long-term critical technical issue. This study is a review of the security issues for space information networks. First, this paper examines space information networks’ security issues and figures out the relationship between the main security threats, services, and mechanisms. Then, this article selects secure routing and anomaly detection from many security technologies to conduct a detailed overview from two perspectives of traditional methods and artificial intelligence. Subsequently, this paper investigates anomaly detection schemes for spatial information networks and proposes a deep learning-based anomaly detection scheme. Finally, we suggest the potential research directions and opening problems of space information network security. Overall, this paper aims to give readers an overview of the newly emerging technologies in space information networks’ security issues and provide inspiration for future exploration.

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Machine learning for routing

Cited by 6 publications

References 0 publications

ML‐IDSDN: Machine learning based intrusion detection system for software‐defined network

ML‐IDSDN: Machine learning based intrusion detection system for software‐defined network

A Deep Reinforcement Learning-Based Geographic Packet Routing Optimization

Survey on security issues of routing and anomaly detection for space information networks

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