The application of data encryption technology in computer network security

Wang, Liya

doi:10.1109/icnetic59568.2023.00030

2023 International Conference on Networking, Informatics and Computing (ICNETIC) 2023

DOI: 10.1109/icnetic59568.2023.00030

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The application of data encryption technology in computer network security

Liya Wang

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2024

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Cited by 2 publications

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Research on neural networks in computer network security evaluation and prediction methods

Wei,

Zhao,

Shi

2024

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Anomaly detection in networks to identify intrusions is a common and successful security measure used in many different types of network infrastructure. Network data traffic has increased due to the proliferation of viruses and other forms of cyber-attacks as network technology and applications have developed quickly. The limitations of classical intrusion detection, such as poor detection accuracy, high false negatives, and dependence on dimensionality reduction methods, become more apparent in the face of massive traffic volumes and characteristic information. That’s why IoT infrastructures often use Software-Defined Networking (SDN), allowing for better network adaptability and control. Hence, this paper’s convolutional neural network-based Security Evaluation Model (CNN-SEM) is proposed to secure the source SDN controller from traffic degradation and protect the source network from DDoS assaults. The proposed CNN-SEM system might defend against DDoS assaults once discovered by applying and testing a Convolutional Neural Network (CNN). The model can automatically extract the useful aspects of incursion samples, allowing for precise classification of such data. The detection and mitigation modules evaluate the proposed SDN security system’s performance, and the findings showed promise against next-generation DDoS assaults. The experimental results show the CNN-SEM achieves a high accuracy ratio of 96.6%, a detection ratio of 97.1%, precision ratio of 97.2%, a performance ratio of 95.1% and an enhanced security rate of 98.1% compared to other methods.

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Research on neural networks in computer network security evaluation and prediction methods

Wei,

Zhao,

Shi

2024

KES

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Employing Cryptographic Techniques for Data Security and Privacy Preservation

Liu

2024

TCSISR

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This scholarly discourse delineates the foundational principles and prevalent algorithms underpinning information encryption technology, illuminating its critical role in fortifying information security through the lenses of data protection and privacy safeguarding. Encryption technology serves as a stalwart guardian of data confidentiality, integrity, and availability, undergirding the secure and robust functioning of digital networks and systems. As the landscape of cyber threats undergoes relentless transformation, the mandate for the advancement of encryption methodologies becomes ever more pressing. The perpetual refinement and evolution of cryptographic practices are imperative to surmount the increasingly intricate challenges posed by sophisticated cyber adversaries. In an era where data breaches and privacy incursions are rampant, encryption acts as the sine qua non of digital security strategies. It not only obfuscates sensitive information from unauthorized viewers but also ensures that even if data is intercepted, it remains an indecipherable enigma to the interloper. Furthermore, encryption technologies underpin trust in the burgeoning realms of e-commerce and online communication, where they authenticate transactional exchanges and preserve the sanctity of digital interactions.

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The application of data encryption technology in computer network security

Cited by 2 publications

References 8 publications

Research on neural networks in computer network security evaluation and prediction methods

Research on neural networks in computer network security evaluation and prediction methods

Employing Cryptographic Techniques for Data Security and Privacy Preservation

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