High-Fidelity Router Emulation Technologies Based on Multi-Scale Virtualization

Song, He; Wang, Xiaofeng; Zhai, Mengdong; Zhang, Guangjie

doi:10.3390/info11010047

Cited by 4 publications

(3 citation statements)

References 17 publications

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“…Hong et al [10] used the NS3 tool to simulate a network for generating and capturing training datasets. Song and Wang [11] proposed LDDoS emulation technologies based on lightweight virtualization, which can construct an LDDoS emulation scenario with 400 routing nodes in a single physical server. Charlier et al [12] presented the novel framework SynGAN that generated adversarial network attacks using the Generative Adversarial Networks (GAN) based on real attack traffic.…”

Section: Related Workmentioning

confidence: 99%

An Asynchronous Federated Learning Arbitration Model for Low-Rate DDoS Attack Detection

et al. 2023

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Low-rate Distributed Denial of Service (LDDoS) attacks have been one of the most notorious network security threats, which use periodic slight multi-variate time series pulse flows to degrade network quality. Limited by the poor data in a single client, a powerful and satisfactory LDDoS attack detection model is hard to be trained. Federated Learning (FL) is a promising paradigm offering joint learning through multiple clients. We propose an asynchronous federated learning arbitration framework based on bidirectional LSTM (bi-LSTM) and attention mechanism (AsyncFL-bLAM). In the AsyncFL-bLAM, the leader node election algorithm is proposed for constructing the framework of asynchronous federated learning. The proposed bLAM model composed of feature extracter and arbitrator takes on the responsibility of LDDoS detection locally. Furthermore, the novel AsyncFL framework helps to upload and aggregate the bLAM models' parameters asynchronously between leader node and client nodes. Experimental results show that the AsyncFL-bLAM outperforms the state-of-the-art models in accuracy, and reduces the overall communication rounds.INDEX TERMS Arbitration mechanism, asynchronous federated learning, deep learning, low-rate distributed denial-of-service. I. INTRODUCTIONWith the increasing number of Internet of Things (IoT) devices, network attacks are increasing in both intensity and frequency. Recently, LDDoS attacks are reported as the most common ones in IoT. According to CNCERT [1], as many as 8,423 hacked IoT botnets, with no less than 100 IoT devices, were used to orchestrate and launch LDDoS attacks in 2021. Various variants of LDDoS attacks are found recently. Hivenets [2] could transform a single under-controlled IoT device into an intelligent robot to make autonomous decisions with minimal supervision. A multi-targets LDDoS attack model [3] used the bots' unused gaps between bursts to fire another attacks. The novel LSTM-CGAN [4] and TTS-GAN [5] methods could generate high-quality LDDoSThe associate editor coordinating the review of this manuscript and approving it for publication was Giovanni Pau .

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Section: Related Workmentioning

confidence: 99%

An Asynchronous Federated Learning Arbitration Model for Low-Rate DDoS Attack Detection

et al. 2023

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“…That is, the ZMW attack verification and analysis under large-scale network topology were hard to be completed. In order to fix above issue, [6] proposed an LDDoS emulation technology based on lightweight virtualization, instead of real testbed, to set up an LDDoS emulation scenario with 400 routing nodes. But this is just to alleviate the pressure of hardware equipment.…”

Section: Related Workmentioning

confidence: 99%

LSTM-CGAN: Towards Generating Low-Rate DDoS Adversarial Samples for Blockchain-Based Wireless Network Detection Models

Liu

Yin

2021

IEEE Access

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Low-rate Distributed DoS (LDDoS) attack is a complex large-scale attack behavior with strong time-domain characteristics in blockchain-based wireless network. Blockchain with Machine learning-based models, as promising ways, are taken to detect them and secure wireless network. However, researchers focused on how to improve models' detection performance and work out new blockchain-based protection technologies during the past decades. Due to lack of evolving data, these models and technologies may have poor stability in the face of confrontational samples. To cope with the problem, this paper proposes a novel LSTM-CGAN method to generate high-quality LDDoS adversarial samples for blockchain-based wireless network detection models. In this method, we give a brief feature analysis about LDDoS attack in blockchainbased wireless network and work out its corresponding time series model firstly. And then, we take use of Long Short-Term Memory Networks (LSTM) to learn relationships among sequenced network packages in the same flow. At last, we establish a Condition Generative Adversarial Networks (CGAN) model to use above relationships as specific conditions for generating mimicking behaviors of LDDoS attacks in blockchain-based wireless network. The experimental results show that these generated adversarial samples based on both public and private datasets can cheat the machine learning detection models, and have the similar attack characteristics with the real samples. Consequently, they can be used as blockchain-based wireless network dataset of machine learning classifiers for training to enhance models' stability.

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“…What is more, network emulation has been studied along with virtualization and attack emulation for replicating realistic scenarios and for security evaluation purposes [45]. Moreover, the benefits of virtualization and Dockers have also been considered by analyzing their capabilities for network virtualization [46,47]. The networking functionalities of containerization technology in the content of lightweight virtualization instances have been under research [30,48].…”

Section: Related Workmentioning

confidence: 99%

PocketCTF: A Fully Featured Approach for Hosting Portable Attack and Defense Cybersecurity Exercises

et al. 2021

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Capture the flag (CTF) challenges are broadly used for engaging trainees in the technical aspects of cybersecurity, maintaining hands-on lab exercises, and integrating gamification elements. However, deploying the appropriate digital environment for conducting cybersecurity exercises can be challenging and typically requires a lot of effort and system resources by educators. In this paper, we present PocketCTF, an extensible and fully independent CTF platform, open to educators to run realistic virtual labs to host cybersecurity exercises in their classrooms. PocketCTF is based on containerization technologies to minimize the deployment effort and to utilize less system resources. A proof-of-concept implementation demonstrates the feasibility of deploying CTF challenges that allows the trainees to engage not only in offensive security but also in defensive tasks that have to be conducted during cybersecurity incidents. When using PocketCTF, educators can deploy hands-on labs, spending less time on the deployment and without necessarily having the advanced technical background to deploy complex labs and scenarios.

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High-Fidelity Router Emulation Technologies Based on Multi-Scale Virtualization

Cited by 4 publications

References 17 publications

An Asynchronous Federated Learning Arbitration Model for Low-Rate DDoS Attack Detection

An Asynchronous Federated Learning Arbitration Model for Low-Rate DDoS Attack Detection

LSTM-CGAN: Towards Generating Low-Rate DDoS Adversarial Samples for Blockchain-Based Wireless Network Detection Models

PocketCTF: A Fully Featured Approach for Hosting Portable Attack and Defense Cybersecurity Exercises

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