SQL injection attack detection in network flow data

Crespo-Martínez, Ignacio Samuel; Campazas-Vega, Adrián; Guerrero‐Higueras, Ángel Manuel; Castillo, Virginia Riego Del; Álvarez-Aparicio, Claudia; Fernández‐Llamas, Camino

doi:10.1016/j.cose.2023.103093

Cited by 41 publications

(17 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Lu et al 12 Alotaibi et al 13 Patidar et al 14 Liu et al 15 Alghawazi et al 16 Saini et al 17 Sheth et al 18 Okesola et al 19 Labib et al 20 Marashdih et al 21 Mehta et al 22 Henry et al 23 Pruzinec et al 24 Irungu et al 25 Fratty et al 26 Logozzo et al 27 Singh et al 28 Crespo-Martínez et al 29 Fu et al 30 Philip et al 31 Barsellotti et al 32 Mallissery et al 33 Lu et al 12 Alotaibi et al 13 Guan et al 34 Nasrullayev et al 35 Muhammad et al 36 Brintha et al 37 Al Badri et al [36] Lu et al 12 (2023)…”

Section: Parse Tree Validation Policy Enforcement Isr Taint Tracking ...mentioning

confidence: 99%

Analysis of SQL injection attacks in the cloud and in WEB applications

Kumar,

Dutta,

Pranav

2024

Security and Privacy

View full text Add to dashboard Cite

Cloud computing has revolutionized the way IT industries work. Most modern‐day companies rely on cloud services to accomplish their day‐to‐day tasks. From hosting websites to developing platforms and storing resources, cloud computing has tremendous use in the modern information technology industry. Although an emerging technique, it has many security challenges. In structured query language injection attacks, the attacker modifies some parts of the user query to still sensitive user information. This type of attack is challenging to detect and prevent. In this article, we have reviewed 65 research articles that address the issue of its prevention and detection in cloud and Traditional Networks, of which 11 research articles are related to general cloud attacks, and the rest of the 54 research articles are specifically on web security. Our result shows that Random Forest has an accuracy of 99.8% and a Precision rate of 99.9%, and the worst‐performing model is Multi‐Layer Perceptron (MLP) in the SQLIA Model. For recall value, Random Forest performs best while TensorFlow Linear Classifier performs worst. F1 score is best in Random Forest, while MLP is the most diminutive performer.

show abstract

Section: Parse Tree Validation Policy Enforcement Isr Taint Tracking ...mentioning

confidence: 99%

Analysis of SQL injection attacks in the cloud and in WEB applications

Kumar,

Dutta,

Pranav

2024

Security and Privacy

View full text Add to dashboard Cite

show abstract

“…SQL injection is an application layer attack through which hackers steal sensitive data by inserting malicious SQL statements into the input field of an application for execution such as the input of SQL query to dump the database contents to the attacker. In this type of attack, the aim of the attacker is the usage of malicious SQL code for backend database manipulation to access information that is not supposed to be displayed [5] such as private customer details, company data, user's list, and so on.…”

Section: Sql Injection Attackmentioning

confidence: 99%

An Investigation into the Performances of the State-of-the-art Machine Learning Approaches for Various Cyber-attack Detection: A Survey

Ige,

Kiekintveld,

Piplai

2024

Preprint

View full text Add to dashboard Cite

To secure computers and information systems from attackers taking advantage of vulnerabilities in the system to commit cybercrime, several methods have been proposed for real-time detection of vulnerabilities to improve security around information systems. Of all the proposed methods, machine learning had been the most effective method in securing a system with capabilities ranging from early detection of software vulnerabilities to real-time detection of ongoing compromise in a system. As there are different types of cyberattacks, each of the existing state-of-the-art machine learning models depends on different algorithms for training which also impact their suitability for detection of a particular type of cyberattack. In this research, we analyzed each of the current state-of-theart machine learning models for different types of cyberattack detection from the past 10 years with a major emphasis on the most recent works for comparative study to identify the knowledge gap where work is still needed to be done with regard to detection of each category of cyberattack

show abstract

“…SQL attack is a method where the attacker uses malicious cod to gain access to information by backend database manipulation [56]. Information attackers target may be details of the targeted organization, details of the company's customers or users of the company's systems and networks which results to illegitimate viewership of sensitive information, deletion or modification [57], [58], [59].…”

Section: Structured Query Language (Sql) Injectionmentioning

confidence: 99%

Assessment of existing cyber-attack detection models for web-based systems

Awuor¹

2023

Global J. Eng. Technol. Adv.

View full text Add to dashboard Cite

In the current technological environment, different entities engage in intricate cyber security approaches in order to counter damages and disruptions in web-based systems. The design of the security protocols relies on the guarantee that attacks are prevented in the web-based systems. Prevention and detection using techniques such as access control tools, encryption and firewalls present limitations in the full protection of web-based systems. Furthermore, despite the sophistication of current systems, there are still shortfalls in high false positive and false negative threat detection rates, which is attributed to poor adaptation by systems and networks to the changing threats and behavior of cyber-criminals. In this perspective, this survey paper discusses the existing cyber-attack detection models, and recommends the cyber-attack detection models and techniques that are appropriate for web-based systems. It is evident that deep learning techniques offer better performance and robustness compared to traditional machine learning techniques and other non-artificial intelligence-based techniques. Deep learning techniques learn and extract features automatically without human intervention and can also handle big and multidimensional data more conventionally than the other techniques.

show abstract

SQL injection attack detection in network flow data

Cited by 41 publications

References 14 publications

Analysis of SQL injection attacks in the cloud and in WEB applications

Analysis of SQL injection attacks in the cloud and in WEB applications

An Investigation into the Performances of the State-of-the-art Machine Learning Approaches for Various Cyber-attack Detection: A Survey

Assessment of existing cyber-attack detection models for web-based systems

Contact Info

Product

Resources

About