Detection of SQL Injection Vulnerability in Embedded SQL

Jang, Young Su

doi:10.1587/transinf.2019edl8143

Cited by 8 publications

(2 citation statements)

References 14 publications

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“…On the other hand, while Ethereum also supports smart contracts and has been widely used in decentralized applications, it is a permissionless network, meaning that anyone can participate and access the data. This makes it less suitable for use in the context of intrusion detection, where privacy and security are of utmost importance [15]. Hyperledger Fabric's focus on security, privacy, and customization make it a more suitable choice for intrusion detection systems than Ethereum.…”

Section: Blockchain Technologymentioning

confidence: 99%

Hybrid Collaborative Intrusion Detection System Based on Blockchain & Machine Learning

Farah

Korbaa

2023

Preprint

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Intrusion Detection Systems (IDS) have traditionally been designed with a centralized structure, where a single device is responsible for monitoring the entire network. However, with the increasing complexity and scale of modern networks, this approach has become less effective. Centralized IDS can suffer from performance issues, limited scalability, and vulnerability to targeted attacks. To address these limitations, there is a growing need to develop collaborative IDS that can distribute the workload across multiple devices and better handle large-scale networks. Collaboration enables IDS to detect intrusions more effectively by combining and analyzing data from multiple sources. The adoption of blockchain technology is essential in achieving a collaborative IDS. Blockchain provides a secure, decentralized way to store and exchange information between different devices, which is critical for building trust and ensuring the integrity of the system. Furthermore, machine learning algorithms can be used to improve the performance of IDS by detecting new and emerging threats. Machine learning can help to identify patterns and anomalies in network traffic, enabling the system to detect and respond to attacks more effectively. By combining these approaches, a reliable and scalable detection system can be developed. The collaborative IDS using blockchain technology and machine learning algorithms can improve the accuracy and efficiency of detecting network intrusions while maintaining the security and integrity of the system.

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Section: Blockchain Technologymentioning

confidence: 99%

Hybrid Collaborative Intrusion Detection System Based on Blockchain & Machine Learning

Farah

Korbaa

2023

Preprint

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“…If the grammar rules are not met, an error will be reported. If the grammar rules are met, the compilation will pass [11].…”

Section: Syntax Analysis Modulementioning

confidence: 99%

Malicious SQL data injection detection

Yan

Chen

2022

Second International Symposium on Computer Technology and Information Science (ISCTIS 2022)

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SQL injection attack could obtain sensitive information in the database, tamper or delete illegally obtained information, etc., which causes immeasurable losses to the system. Aiming at SQL injection attack, this paper proposes a new SQL injection detection scheme that combines traditional detection methods with abstract syntax tree structure judgment based on semantic analysis. The solution includes modules such as data preprocessing, SQL statement pre-assembly, and semantic analysis. By assembling the user input content and the actual SQL template statement to form a complete SQL statement, the statement is subjected to structural judgment and semantic analysis to determine the request and precisely identify malicious injection attack.

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Cybersecurity Considerations in Blockchain-Based Solutions

McKay

Mashatan

2022

Blockchain in Life Sciences

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Detection of SQL Injection Vulnerability in Embedded SQL

Cited by 8 publications

References 14 publications

Hybrid Collaborative Intrusion Detection System Based on Blockchain & Machine Learning

Hybrid Collaborative Intrusion Detection System Based on Blockchain & Machine Learning

Malicious SQL data injection detection

Cybersecurity Considerations in Blockchain-Based Solutions

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Detection of SQL Injection Vulnerability in Embedded SQL

Cited by 8 publications

References 14 publications

Hybrid Collaborative Intrusion Detection System Based on Blockchain &amp; Machine Learning

Hybrid Collaborative Intrusion Detection System Based on Blockchain &amp; Machine Learning

Malicious SQL data injection detection

Cybersecurity Considerations in Blockchain-Based Solutions

Contact Info

Product

Resources

About

Hybrid Collaborative Intrusion Detection System Based on Blockchain & Machine Learning

Hybrid Collaborative Intrusion Detection System Based on Blockchain & Machine Learning