Noise-Robust Multilayer Perceptron Architecture for Distributed Denial of Service Attack Detection

Maranhão, João Paulo A.; Costa, João Paulo; Freitas, Edison Pignaton de; Javidi, Elnaz; Sousa, Rafael T. de

doi:10.1109/lcomm.2020.3032170

Cited by 13 publications

(2 citation statements)

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“…Also, differing from jamming attacks that directly disrupt the signal, DDoS attacks inundate the network with excessive requests, overwhelming the system and impeding legitimate communications. Several studies have explored various aspects of DDoS attacks in V2X environments [101]- [104]. A notable approach in detecting and locating DDoS attacks in LTE-based vehicular networks involves the application of Machine Learning techniques designed to handle the dynamic scenarios of moving vehicles and heterogeneous network entities such as Femtocells/Femto Access Points (FAPs) [105].…”

Section: Dos and Ddos Attacks In V2x Communication Scenariosmentioning

confidence: 99%

Safeguarding the V2X Pathways: Exploring the Cybersecurity Landscape Through Systematic Review

Herman Muraro Gularte,

Alfredo Ruiz Vargas,

Paulo Javidi da Costa

et al. 2024

IEEE Access

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Vehicle-to-Everything (V2X) communication, essential for enhancing road safety, driving efficiency, and traffic management, must be robust against cybersecurity threats for successful deployment and acceptance. This survey comprehensively explores V2X security challenges, focusing on prevalent cybersecurity threats such as jamming, spoofing, Distributed Denial of Service (DDoS), and eavesdropping attacks. These threats were selected due to their prevalence and ability to compromise the integrity and reliability of V2X systems. Jamming can disrupt communications, spoofing can lead to data and identity manipulation, DDoS attacks can saturate system resources, and eavesdropping can compromise user privacy and information confidentiality. Addressing these major threats ensures that V2X systems are robust and secure for successful deployment and widespread acceptance. An extensive review uncovered a global landscape of V2X cybersecurity research. We highlight contributions from China in scientific publications and the United States in patent innovations, with notable advancements from leading corporations such as Qualcomm, LG, Huawei, Intel, Apple, and Samsung. This work educates and informs on the current state of V2X cybersecurity and identifies emerging trends and future research directions based on a year-byyear analysis of the literature and patents. The findings underscore the evolving cybersecurity landscape in V2X systems and the importance of continued innovation and research in this critical field. The survey navigates the complexities of securing V2X communications, emphasizing the necessity for advanced security protocols and technologies, and highlights innovative approaches within the global scientific and patent research context. By providing a panoramic view of the field, this survey sets the stage for future advancements in V2X cybersecurity INDEX TERMS Vehicle-to-everything, connected and automated vehicle, intellectual property in V2X cybersecurity, cyberattacks and attacks, V2X security.

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Section: Dos and Ddos Attacks In V2x Communication Scenariosmentioning

confidence: 99%

Safeguarding the V2X Pathways: Exploring the Cybersecurity Landscape Through Systematic Review

Herman Muraro Gularte,

Alfredo Ruiz Vargas,

Paulo Javidi da Costa

et al. 2024

IEEE Access

Self Cite

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“…In order to explain network traffic, wavelet analysis is utilized to extract spectral components and distinguish anomalous events from typical network activity. Techniques for detecting intrusions recently developed [5], [6] that are based on machine learning (ML) algorithms [7].…”

Section: Introductionmentioning

confidence: 99%

Fast and accurate classifying model for denial-of-service attacks by using machine learning

Kareem

Jasim

2022

Bulletin EEI

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A denial of service (DoS) attack is one of the dangerous threats to networks that Internet resources and services will be less available, as they are easily operated and difficult to detect. As a result, identifying these intrusions is a hot issue in cybersecurity. Intrusion detection systems that use classic machine learning algorithms have a long testing period and high computational complexity. Therefore, it is critical to develop or improve techniques for detecting such an attack as quickly as possible to reduce the impact of the attack. As a result, we evaluate the effectiveness of rapid machine learning methods for model testing and generation in communication networks to identify denial of service attacks. In WEKA tools, the CICIDS2017 dataset is used to train and test multiple machine learning algorithms. The wide learning system and its expansions and the REP tree (REPT), random tree (RT), random forest (RF), decision stump (DS), and J48 were all evaluated. Experiments have shown that J48 takes less testing time and performs better, whereases it is performed by using 4-8 features. An accuracy result of 99.51% and 99.96% was achieved using 4 and 8 features, respectively.

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Multi-domain collaborative two-level DDoS detection via hybrid deep learning

Feng,

Zhang,

Liu

et al. 2024

Computer Networks

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Noise-Robust Multilayer Perceptron Architecture for Distributed Denial of Service Attack Detection

Cited by 13 publications

References 10 publications

Safeguarding the V2X Pathways: Exploring the Cybersecurity Landscape Through Systematic Review

Safeguarding the V2X Pathways: Exploring the Cybersecurity Landscape Through Systematic Review

Fast and accurate classifying model for denial-of-service attacks by using machine learning

Multi-domain collaborative two-level DDoS detection via hybrid deep learning

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