A survey on neural networks for (cyber-) security and (cyber-) security of neural networks

Pawlicki, Marek; Kozik, Rafał; Choraś, Michał

doi:10.1016/j.neucom.2022.06.002

Cited by 47 publications

(8 citation statements)

References 131 publications

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“…Several intriguing applications of neural networks are being employed to improve data security. Here are a few instances of data security solutions that have benefited from neural network advancements [31].…”

Section: Neural Network Can Provide Additional Security Layers To Dat...mentioning

confidence: 99%

Artificial Neural Networks: An Overview

Qamar,

Ali Zardari

2023

Mesopotamian Journal of Computer Science

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Neural networks, also known as artificial neural networks (ANNs) or artificially generated neural networks (SNNs) are a subset of machine learning that provide the foundation of deep learning techniques. Their name and form are inspired by the human brain, and they replicate the way real neurons communicate with one another. Artificial neural networks (ANNs) are massively parallel systems comprised of a huge number of interconnected basic processors. This paper discuss about the artificial neural network and its basic types. This article explains the ANN and its basic outlines the fundamental neuron and the artificial computer model. It describes network structures and learning methods, as well as some of the most popular ANNs.

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Section: Neural Network Can Provide Additional Security Layers To Dat...mentioning

confidence: 99%

Artificial Neural Networks: An Overview

Qamar,

Ali Zardari

2023

Mesopotamian Journal of Computer Science

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“…The concrete, measurable results of an AI-powered solution are still the most important, ultimate objective, but an increasing number of researchers agree that it is unadvisable to design any AI/ML solution without taking the explainability, interpretability and fairness into consideration (Choraś et al , 2020; Szczepański et al , 2020; Markus et al , 2021; Pawlicki et al , 2022). Thus, the principles of fairness, explainability and security of AI/ML-powered solutions must be embedded in any solution right from the very beginning.…”

Section: The “New” Human-related Aspects Of Cybersecuritymentioning

confidence: 99%

Human-driven and human-centred cybersecurity: policy-making implications

Pawlicka

Pawlicki

Kozik

et al. 2022

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Purpose The purpose of this paper is to challenge the prevailing, stereotypical approach of the human aspect of cybersecurity, i.e. treating people as weakness or threat. Instead, several reflections are presented, pertaining to the ways of making cybersecurity human-centred. Design/methodology/approach This paper bases on the authors’ own experiences, gathered whilst working in cybersecurity projects; the resulting comments and reflections have been enriched and backed up by the results of a targeted literature study. Findings The findings show that the way the human aspects of cybersecurity are understood is changing, and deviates from the stereotypical approach. Practical implications This paper provides a number of practical recommendations for policymakers, as well as cybersecurity managers on how to make the cybersecurity more human-centred; it also inspires further research directions. Originality/value This paper presents a fresh, positive approach to humans in cybersecurity and opens the doors to further discourse about new paradigms in the field.

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“…The strong adaptability is of high value. Combined with the generalisation ANNs provide as they are trained with an immense amount of datapoints, ANNs obtain high performance on test data [19][38][8] [42].…”

Section: Deep Neural Network and Hyperparameter Optimizationmentioning

confidence: 99%

Towards Deployment Shift Inhibition Through Transfer Learning in Network Intrusion Detection

Pawlicki

Kozik

Choraś

2022

Proceedings of the 17th International Conference on Availability, Reliability and Security

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Currently, machine learning sees growing adoption in numerous domains, including critical applications, like cybersecurity. However, to fully enjoy the benefits of artificial intelligence the end-user has some high barriers to entry to circumnavigate. The deployment of machine-learning-based Network Intrusion Detection Systems requires the collection of labelled data to train the intelligent components. This is an expensive and laborious process, which necessitates expert knowledge in cyberattacks and computer networks. Even when using data collected and labelled on premises, phenomena like concept drift can cause the model to underperform -a concept known as deployment shift. This paper evaluates the use of transfer learning techniques to curb the effects of deployment shift in machine-learning-based network intrusion detection.

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A survey on neural networks for (cyber-) security and (cyber-) security of neural networks

Cited by 47 publications

References 131 publications

Artificial Neural Networks: An Overview

Artificial Neural Networks: An Overview

Human-driven and human-centred cybersecurity: policy-making implications

Towards Deployment Shift Inhibition Through Transfer Learning in Network Intrusion Detection

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