A DRDoS Detection and Defense Method Based on Deep Forest in the Big Data Environment

Xu, Ruomeng; Cheng, Jieren; Wang, Fengkai; Tang, Xiangyan; Xu, Jinying

doi:10.1007/978-3-030-05234-8_21

Cited by 3 publications

(2 citation statements)

References 14 publications

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“…Karim et al [11] investigated the performance of Snort-based IDS on a network. Xu et al [12] proposed a deep forest-based distributed denial-of-service detection and defense model. They concentrate on attacks on smart nodes and the significant data context.…”

Section: Wireless Communications and Mobile Computingmentioning

confidence: 99%

ML-DDoSnet: IoT Intrusion Detection Based on Denial-of-Service Attacks Using Machine Learning Methods and NSL-KDD

Esmaeili

Goki

Masjidi

et al. 2022

Wireless Communications and Mobile Computing

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The Internet of Things (IoT) is a complicated security feature in which datagrams are protected by integrity, confidentiality, and authentication services. The network is protected from external interruptions and intrusions. Because IoT devices run with a range of heterogeneous technologies and process data over time, standard solutions may not be practical. It is necessary to develop intelligent procedures that can be used for multiple levels of data flow in the system. This study examines metainnovations using deep learning-based IDS. Per the findings of the earlier tests, BiLSTMs are better for binary (regular/attacker) classification; however, sequential models (LSTM or BiLSTM) are better for detecting some brutal attacks in multiclass classifiers. According to experts, deep learning-based intrusion detection systems can now recognize and select the best structure for each category. However, specific difficulties will need to be solved in the future. Two topics should be studied further in future attempts. One of the researchers’ concerns is the impact of various data processing techniques, such as artificial intelligence or metamethods, on IDS. The BiLSTM approach has chosen the safest instances with the highest accuracy among the models. According to the findings, the most reliable and suitable solution for evaluating DDoS attacks in IoT is the BiLSTM design.

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Section: Wireless Communications and Mobile Computingmentioning

confidence: 99%

ML-DDoSnet: IoT Intrusion Detection Based on Denial-of-Service Attacks Using Machine Learning Methods and NSL-KDD

Esmaeili

Goki

Masjidi

et al. 2022

Wireless Communications and Mobile Computing

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show abstract

“…When DRDoS attacks occur, network traffic patterns are analyzed by fuzzy association rules with path restriction. The DRDoS attacks' defensive architecture based on multi-agent (DAMA) is set up to realize the detection, orientation, and defensive function [27]. DAMA is validated using network simulator 2 (NS-2) platform to quickly detect the attack source, screen the attack source, and stop transmitting attack traffic.…”

Section: Ddos Detection-based Aimentioning

confidence: 99%

Intelligent Application Protection Mechanism for Transportation in V2C Environment

et al. 2020

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The emergence of vehicle-to-cloud (V2C) technology is changing cloud computing and transportation ecosystems. V2C technology enables the development of smart services, such as driving assistance and vehicle maintenance, that transmit information to the driver. Recent studies have primarily focused on services. To date, there has not been sufficient research on security functions to detect abnormal behaviors on virtual application. If an abnormal behavior occurs in the application, the service not only notices the wrong information to driver, but also affects the transportation system around the vehicle. To defend against distributed denial of service (DDoS) attacks, system resources should be monitored constantly. However, continuous monitoring is difficult because V2C services change dynamically according to the service environment. In addition, rule-based or supervised monitoring is impossible for each of the numerous services provided by a cloud computing center. In this paper, we propose an intelligent application protection mechanism for smart vehicle services in a V2C environment that detects abnormal behavior through image-based system resource monitoring using artificial intelligence (ISRM-AI) to improve cloud vehicle service security. The ISRM-AI generates images about system information such as CPU, network, memory on V2C cloud services. Also, the mechanism analyzes the status using a convolutional neural network (CNN) to detect abnormal behavior of the each services. We constructed a service environment to test the performance of the proposed mechanism. In addition, we simulated the proposed mechanism's ability to detect DDoS attacks using real attack data. The proposed mechanism guarantees the reliability of a smart service by enhancing the security of the V2C environment.

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AF-FDS: An Accurate, Fast, and Fine-Grained Detection Scheme for DDoS Attacks in High-Speed Networks With Asymmetric Routing

Shao

Chen

Cheng

et al. 2023

IEEE Trans. Netw. Serv. Manage.

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A DRDoS Detection and Defense Method Based on Deep Forest in the Big Data Environment

Cited by 3 publications

References 14 publications

ML-DDoSnet: IoT Intrusion Detection Based on Denial-of-Service Attacks Using Machine Learning Methods and NSL-KDD

ML-DDoSnet: IoT Intrusion Detection Based on Denial-of-Service Attacks Using Machine Learning Methods and NSL-KDD

Intelligent Application Protection Mechanism for Transportation in V2C Environment

AF-FDS: An Accurate, Fast, and Fine-Grained Detection Scheme for DDoS Attacks in High-Speed Networks With Asymmetric Routing

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