Optimal Deep Learning Driven Intrusion Detection in SDN-Enabled IoT Environment

In recent years,wireless networks are widely used in different domains.This phenomenon has increased the number of Internet of Things(IoT)devices and their applications.Though IoT has numerous advantages,the commonly-used IoT devices are exposed to cyber-attacks periodically.This scenario necessitates real-time automated detection and the mitigation of different types of attacks in high-traffic networks.The Software-Defined Networking(SDN)technique and the Machine Learning(ML)-based intrusion detection technique are effective tools that can quickly respond to different types of attacks in the IoT networks.The Intrusion Detection System(IDS)models can be employed to secure the SDN-enabled IoT environment in this scenario.The current study devises a Harmony Search algorithmbased Feature Selection with Optimal Convolutional Autoencoder(HSAFSOCAE)for intrusion detection in the SDN-enabled IoT environment.The presented HSAFS-OCAE method follows a three-stage process in which the Harmony Search Algorithm-based FS(HSAFS)technique is exploited at first for feature selection.Next,the CAE method is leveraged to recognize and classify intrusions in the SDN-enabled IoT environment.Finally,the Artificial Fish SwarmAlgorithm(AFSA)is used to fine-tune the hyperparameters.This process improves the outcomes of the intrusion detection process executed by the CAE algorithm and shows the work’s novelty.The proposed HSAFSOCAE technique was experimentally validated under different aspects,and the comparative analysis results established the supremacy of the proposed model.

Networking Controller Based Real Time Traffic Prediction in Clustered Vehicular Adhoc Networks

The vehicular ad hoc network(VANET)is an emerging network tech-nology that has gained popularity because to its low cost,flexibility,and seamless services.Software defined networking(SDN)technology plays a critical role in network administration in the future generation of VANET withfifth generation(5G)networks.Regardless of the benefits of VANET,energy economy and traffic control are significant architectural challenges.Accurate and real-time trafficflow prediction(TFP)becomes critical for managing traffic effectively in the VANET.SDN controllers are a critical issue in VANET,which has garnered much interest in recent years.With this objective,this study develops the SDNTFP-C technique,a revolutionary SDN controller-based real-time trafficflow forecasting technique for clustered VANETs.The proposed SDNTFP-C technique combines the SDN controller’s scalability,flexibility,and adaptability with deep learning(DL)mod-els.Additionally,a novel arithmetic optimization-based clustering technique(AOCA)is developed to cluster automobiles in a VANET.The TFP procedure is then performed using a hybrid convolutional neural network model with atten-tion-based bidirectional long short-term memory(HCNN-ABLSTM).To optimise the performance of the HCNN-ABLSTM model,the dingo optimization techni-que was used to tune the hyperparameters(DOA).The experimental results ana-lysis reveals that the suggested method outperforms other current techniques on a variety of evaluation metrics.

A Controller-Based Architecture for Information Centric Network Construction and Topology management

Information-Centric Networking(ICN) has recently emerged as a result of the increased demand to access contents regardless of their location in the network services. This new approach facilitates content distribution as a service of the network with lower delay and higher security in comparison with the current IP network. Applying ICN in current IP infrastructure leads to major complexities. One approach to deploy ICN with less complexity is to integrate ICN with Software Defined Networking(SDN). The SDN controller manages the content distribution, caching, and routing based on the users' requests. In this paper, we extend these context by addressing the ICN topology management problem over the SDN network to achieve an improved user experience as well as network performance. In particular, a centralized controller is designed to construct and manage the ICN overlay. Experimental results indicate that this adopted topology management strategy achieves high performance, in terms of low failure in interest satisfaction and reduced download time compared to a plain ICN.

A Survivability Routing Mechanism in SDN Enabled Wireless Mesh Networks:Design and Evaluation

In software-defined networking,the separation of control plane from forwarding plane introduces new challenges to network reliability.This paper proposes a fault-tolerant routing mechanism to improve survivability by converting the survivability problem into two sub-problems:constructing an elastic-aware routing tree and controller selection.Based on the shortest path tree,this scheme continuously attempts to prune the routing tree to enhance network survivability.After a certain number of iterations,elastic-aware routing continues to improve network resiliency by increasing the number of edges in this tree.Simulation results demonstrate this fault-tolerant mechanism performs better than the traditional method in terms of the number of protected nodes and network fragility indicator.

Software defined satellite networks:A survey

In recent years,satellite networks have been proposed as an essential part of next-generation mobile communication systems.Software defined networking techniques are introduced in satellite networks to handle the growing challenges induced by time-varying topology,intermittent inter-satellite link and dramatically increased satellite constellation size.This survey covers the latest progress of software defined satellite networks,including key techniques,existing solutions,challenges,opportunities,and simulation tools.To the best of our knowledge,this paper is the most comprehensive survey that covers the latest progress of software defined satellite networks.An open GitHub repository is further created where the latest papers on this topic will be tracked and updated periodically.Compared with these existing surveys,this survey contributes from three aspects:(1)an up-to-date SDN-oriented review for the latest progress of key techniques and solutions in software defined satellite networks;(2)an inspiring summary of existing challenges,new research opportunities and publicly available simulation tools for follow-up studies;(3)an effort of building a public repository to track new results.