Recognition of Abnormal Traffic Using Deep Neural Networks and Fuzzy Logic

Amosov, O.S.; Ivanov, Yuriy; Amosova, S.G.

doi:10.1109/fareastcon.2019.8934327

Cited by 7 publications

(2 citation statements)

References 9 publications

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“…Addressing research questions in denial of service (DoS) attacks, the authors in [95] employed dual deep learning neural network architecture based on convolution layers. Their research novelty comes in the form of the reinforcement of the input vector with its cluster evaluation.…”

Section: No Areas Examplesmentioning

confidence: 99%

An In-Depth Examination of Artificial Intelligence-Enhanced Cybersecurity in Robotics, Autonomous Systems, and Critical Infrastructures

Santoso,

Finn

2024

IEEE Trans. Serv. Comput.

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Recent developments in cutting-edge robotics have been constantly faced with increased cyber-threats, not only in terms of the quantity or the frequency of attacks, but also when it comes to the quality and the severity of the intrusions. This paper provides a systematic overview and critical assessment of state-of-the-art scientific developments in the security aspects of robotics, autonomous systems, and critical infrastructures. Our review highlights open research questions addressing significant research gaps and/or new conceptual frameworks, considering recent advancements in artificial intelligence (AI) and machine learning. Thus the contributions of this paper can be summarised as follows. We first compare and contrast the benefits of multiple cutting-edge AI-based learning algorithms (e.g., fuzzy logic and neural networks) relative to traditional model-based systems (e.g. distributed control and filtering). Subsequently, we point out some specific benefits of AI algorithms to quickly learn and adapt the dynamics of non-linear systems in the absence of complex mathematical models. We also present some potential future research directions (open challenges) in the field. Lastly, this review also delivers an open message to encourage collaborations among experts from multiple disciplines. The implementation of multiple AI algorithms to tackle current security issues in robotics will transform and create novel, hybrid knowledge for intelligent cybersecurity at the application level.

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Section: No Areas Examplesmentioning

confidence: 99%

An In-Depth Examination of Artificial Intelligence-Enhanced Cybersecurity in Robotics, Autonomous Systems, and Critical Infrastructures

Santoso,

Finn

2024

IEEE Trans. Serv. Comput.

View full text Add to dashboard Cite

show abstract

“…The most common supervised learning algorithm are decision tree, logistic regression, support vector machine, relevance vector machine, random forest, K-NN, bagging neural networks, linear regression and naïve Bayes [26].…”

Section: Supervised Learningmentioning

confidence: 99%