A Survey on Network Intrusion Detection using Convolutional Neural Network

Kaissar, Antanios; Nassif, Ali Bou; Injadat, MohammadNoor

doi:10.1051/itmconf/20224301003

“…Integrating hybrid ML algorithms into big data processing will increase the percentage of accurate results and reveal the hidden knowledge within the data. Compared to manual or statistically-based approaches, ML and DL techniques can reduce the false recognition rate of traditional NIDS [112,113]. By reducing False Negatives, the detection rate will be improved.…”

mentioning

confidence: 99%

A Review of Digital Twins and Their Application in Cybersecurity Based on Artificial Intelligence

Homaei,

Mogollón Gutiérrez,

Sancho Núñez

et al. 2023

Preprint

View full text Add to dashboard Cite

The potential of digital twin technology is yet to be fully realised due to its diversity and untapped potential. Digital twins enable systems’ analysis, design, optimisation, and evolution to be performed digitally or in conjunction with a cyber-physical approach to improve speed, accuracy, and efficiency over traditional engineering methods. Industry 4.0, factories of the future, and digital twins continue to benefit from the technology and provide enhanced efficiency within existing systems. Due to the lack of information and security standards associated with the transition to cyber digitisation, cybercriminals have been able to take advantage of the situation. Access to a digital twin of a product or service is equivalent to threatening the entire collection. There is a robust interaction between digital twins and artificial intelligence tools, which leads to strong interaction between these technologies, so it can be used to improve the cybersecurity of these digital platforms based on their integration with these technologies. This study aims to investigate the role of artificial intelligence in providing cybersecurity for digital twin versions of various industries, as well as the risks associated with these versions. In addition, this research serves as a road map for researchers and others interested in cybersecurity and digital security.

show abstract

A review of digital twins and their application in cybersecurity based on artificial intelligence

Homaei,

Mogollón-Gutiérrez,

Sancho

et al. 2024

Artif Intell Rev

2

0

View full text Add to dashboard Cite

The potential of digital twin technology is yet to be fully realised due to its diversity and untapped potential. Digital twins enable systems’ analysis, design, optimisation, and evolution to be performed digitally or in conjunction with a cyber-physical approach to improve speed, accuracy, and efficiency over traditional engineering methods. Industry 4.0, factories of the future, and digital twins continue to benefit from the technology and provide enhanced efficiency within existing systems. Due to the lack of information and security standards associated with the transition to cyber digitisation, cybercriminals have been able to take advantage of the situation. Access to a digital twin of a product or service is equivalent to threatening the entire collection. There is a robust interaction between digital twins and artificial intelligence tools, which leads to strong interaction between these technologies, so it can be used to improve the cybersecurity of these digital platforms based on their integration with these technologies. This study aims to investigate the role of artificial intelligence in providing cybersecurity for digital twin versions of various industries, as well as the risks associated with these versions. In addition, this research serves as a road map for researchers and others interested in cybersecurity and digital security.

show abstract

A Novel Lightweight NIDS Framework for Detecting Anomalous Data Traffic in Contemporary Networks

Kumar,

Subba

2024

J CIRCUIT SYST COMP

0

View full text Add to dashboard Cite

Network Intrusion Detection Systems (NIDSs) have been proposed in the literature as security tools for detecting anomalous and intrusive network data traffic. However, the existing NIDS frameworks are computation-intensive, thereby making them unsuitable for deployment in resource-constrained networks with limited computational capabilities. This paper aims to address this issue by proposing computationally efficient NIDS framework for detecting anomalous data traffic in resource-constrained networks. The proposed NIDS framework uses an ensemble-based classifier model comprising multiple classifiers, which enables it to achieve high accuracy and detection rate across a wide range of low-footprint and stealth network attacks. The proposed framework also uses feature scaling and dimensionality reduction techniques to minimize the overall computational overhead. The proposed framework consists of two stages. In the first stage, four distinct base-level classifiers are utilized. The classification probabilities of the first stage are used in the modified meta-level classifier. The modified meta-level classifier is trained on the class probabilities of the base-level classifiers combined using a novel proposed probability function. The performance of the proposed NIDS framework is evaluated on a proprietary testbed dataset and two benchmark datasets namely CICIDS-2017 and UNSW-NB15. The results reveal that the proposed NIDS framework provides better performance than the existing NIDS frameworks in terms of false positive rate, despite using a significantly lower number of input features for its analysis.

show abstract

A Survey on Network Intrusion Detection using Convolutional Neural Network

Cited by 4 publications

References 81 publications

A Review of Digital Twins and Their Application in Cybersecurity Based on Artificial Intelligence

A Review of Digital Twins and Their Application in Cybersecurity Based on Artificial Intelligence

A review of digital twins and their application in cybersecurity based on artificial intelligence

A Novel Lightweight NIDS Framework for Detecting Anomalous Data Traffic in Contemporary Networks

Contact Info

Product

Resources

About