Search Results - Dataset SQL injection Anomaly detection

Superviz25-SQL: high-quality dataset to empower unsupervised SQL injection detection systems

Authors: Quetel, Grégor Alata, Eric Gimenez, Pierre-François et al.

Contributors: Quetel, Grégor Alata, Eric Gimenez, Pierre-François et al.

Subject Terms: SQL injection, Dataset SQL injection Anomaly detection, Anomaly detection, [INFO] Computer Science [cs], SQL Injection Detection Dataset, Dataset

File Description: application/pdf

Access URL: https://hal.science/hal-05314211v1

Comprehensive Study of SQL Injection Attacks Mitigation Methods and Future Directions.

Authors: Al-olaqi, Mohammed Al-gailani, Ahmed Rahman, M. M. Hafizur

Source: Journal of Cyber Security & Risk Auditing; 2025, Vol. 2025 Issue 4, p347-365, 19p

Subject Terms: SQL, CONVOLUTIONAL neural networks, INTRUSION detection systems (Computer security), DATA analysis, ACCURACY

SecuProbe: Intelligent Detection of Cross-Site Scripting, SQL Injection, & No SQL Attacks with Real-Time Alert.

Authors: Prasad, Y. Shyam Simhadri, B. Karthikram, A.

Source: International Research Journal of Innovations in Engineering & Technology; 2025 Special Issue, Vol. 9, p228-244, 7p

Subject Terms: SECURITY systems, ANOMALY detection (Computer security), SQL, WEB design, CYBERTERRORISM

Parameter optimization of one-class support vector machine for detecting SQL injection attacks.

Authors: Al-Araji, Zainab H. Alyasiri, Hasanen

Source: AIP Conference Proceedings. 2025, Vol. 3318 Issue 1, p1-13. 13p.

Subject Terms: *SUPPORT vector machines, *ANOMALY detection (Computer security), *DETECTION algorithms, *MACHINE learning, *INTERNET security, *OPTIMIZATION algorithms

A Thirty-Day Dataset of Malicious HTTP Requests Blocked by OWASP ModSecurity on a Production Web Server.

Authors: Lucz, Geza Forstner, Bertalan

Source: Data (2306-5729); Nov2025, Vol. 10 Issue 11, p186, 11p

Subject Terms: INTRUSION detection systems (Computer security), INTERNET security, DATA security failures, COMPUTER network security

Enhancing Database Security in NoSQL Systems Using Machine Learning-Based Anomaly Detection

Authors: Al Rusheidi , Abdullah Nasser Nasar, Mohammad

Source: مجلة الشرق للعلوم الإنسانية; مجلد 1 عدد 5 (2025): عدد خاص لمؤتمر ICETMF25، كلية مزون، سلطنة عمان; 58-67 ; East Journal of Human Science; Vol. 1 No. 5 (2025): Special Issue for ICETMF25, Mazoon College, Oman; 58-67 ; 3079-9384

Subject Terms: NoSQL databases, Machine Learning, Machine Learning Anomaly Detection, Database Security, SQL Injection, Big Data, Internet of Things (IoT)

File Description: application/pdf

Relation: https://eastpublication.com/index.php/ejhs/article/view/156/68; https://eastpublication.com/index.php/ejhs/article/view/156

Availability: https://eastpublication.com/index.php/ejhs/article/view/156
https://doi.org/10.63496/ejhs.Vol1.Iss5.156

Enhancing Large-Scale Network Security with a VGG-Net-Based DCNN: A Deep Learning Approach to Anomaly Detection.

Authors: Siale, Adnan Yousif Dawod Hassan, Qasim Mustafa Zainel Kadekle, Mohammed Fakhrulddin Abdulqader Sedeeq et al.

Source: Journal of Robotics & Control (JRC); 2025, Vol. 6 Issue 3, p1316-1331, 16p

Subject Terms: ANOMALY detection (Computer security), DEEP learning, CONVOLUTIONAL neural networks, CYBERTERRORISM, INTERNET of things, COMPUTER network security

Machine Learning and Deep Learning Techniques for Internet of Things Network Anomaly Detection—Current Research Trends.

Authors: Rafique, Saida Hafsa Abdallah, Amira Musa, Nura Shifa et al.

Source: Sensors (14248220); 3/15/2024, Vol. 24 Issue 6, p1-32, 32p, 1 Diagram, 5 Charts, 2 Graphs

Enhancing Web Application Performance and Security Using AI-Driven Anomaly Detection and Optimization Techniques.

Authors: Thangaraju, Vigneshwaran

Source: International Research Journal of Innovations in Engineering & Technology; Mar2025, Vol. 9 Issue 3, p205-212, 8p

Subject Terms: WEB-based user interfaces, ARTIFICIAL intelligence, ANOMALY detection (Computer security), INTERNET security, CYBERTERRORISM, MACHINE learning, RESOURCE allocation

AN UNSUPERVISED APPROACH FOR DETECTION OF ENCRYPTED IOT ANOMALIES USING VARIATIONAL AUTOENCODER AND ISOLATION FOREST TECHNIQUES

Authors: Sukanya N Raja S

Source: ICTACT Journal on Communication Technology. 16:3677-3685

Anomaly Detection in IoT Networks: Machine Learning Approaches for Intrusion Detection.

Authors: Atassi, Reem

Source: Fusion: Practice & Applications; 2023, Vol. 13 Issue 1, p126-134, 9p

Subject Terms: LEARNING curve, RECEIVER operating characteristic curves, ANOMALY detection (Computer security), INTERNET of things, MACHINE learning, INTRUSION detection systems (Computer security)

Comprehensive Study of SQL Injection Attacks Mitigation Methods and Future Directions

Authors: Mohammed Al-olaqi Ahmed Al-gailani M M Hafizur Rahman

Source: Journal of Cyber Security and Risk Auditing. 2025:347-365

Application of machine learning and artificial intelligence in detecting SQL injection attacks

Authors: Md Sultan, Abu Bakar Agiliga, Nwabudike Augustine Osman, Mohd Hafeez Bin et al.

Index Terms: Article, PeerReviewed

URL: http://psasir.upm.edu.my/id/eprint/118126/1/118126.pdf
http://psasir.upm.edu.my/id/eprint/118126/
https://joiv.org/index.php/joiv/article/view/3631
http://psasir.upm.edu.my/id/eprint/118126
https://joiv.org/index.php/joiv/article/view/3631
10.62527/joiv.8.4.3631

SQL Injection Detection using Machine Learning: A Review.

Authors: Oudah, Mohammed A. M. Marhusin, Mohd Fadzli

Source: Malaysian Journal of Science, Health & Technology; 2024, Vol. 10 Issue 1, p39-49, 11p

Application of Artificial Intelligence in Detecting SQL Injection Attacks

Authors: Nwabudike Augustine Abu Bakar Md. Sultan Mohd Hafeez Osman et al.

Source: JOIV: International Journal on Informatics Visualization, Vol 8, Iss 4, Pp 2131-2138 (2024)

Subject Terms: sql injection, machine learning, artificial intelligence, cybersecurity, sql injection attack, Computer software, QA76.75-76.765

File Description: electronic resource

Relation: https://joiv.org/index.php/joiv/article/view/3631; https://doaj.org/toc/2549-9610; https://doaj.org/toc/2549-9904

Access URL: https://doaj.org/article/ea8b0492532c4e28b09f4d93258bf071

Advanced modelling techniques for anomaly detection: A proactive approach to database breach mitigation

Authors: null Chinedu Jude Nzekwe null Christopher J Ozurumba

Source: International Journal of Science and Research Archive. 13:2839-2909

Anomaly-based Web Attack Detection: The Application of Deep Neural Network Seq2Seq With Attention Mechanism.

Authors: Mohammadi, Shahriar Namadchian, Amin

Source: ISeCure; Winter/Spring 2020, Vol. 12 Issue 1, p44-54, 11p

Subject Terms: ANOMALY detection (Computer security), SQL, UNIFORM Resource Locators, MACHINE learning, WEBSITES

Profiling Database Application to Detect SQL Injection Attacks

Authors: Bertino, Elisa Kamra, Ashish Early, James

Source: 2007 IEEE International Performance, Computing, and Communications Conference. :449-458

Subject Terms: SQL, validity, query, databases, applications, Life Sciences, 02 engineering and technology, anomaly detection, Engineering, Medicine and Health Sciences, Physical Sciences and Mathematics, 0202 electrical engineering, electronic engineering, information engineering, malicious behaviour

Access URL: http://doi.ieeecomputersociety.org/10.1109/PCCC.2007.358926
https://docs.lib.purdue.edu/ccpubs/287/
https://dblp.uni-trier.de/db/conf/ipccc/ipccc2007.html#BertinoKE07
https://ci.nii.ac.jp/naid/10028098425/
http://yadda.icm.edu.pl/yadda/element/bwmeta1.element.ieee-000004197962
https://ieeexplore.ieee.org/document/4197962/

Theoretical Exploration of a Graph-Based Framework for Automated Cyberattack Classification in Client-Server Models with Emphasis on Computational Cost Optimization.

Authors: Jawad, Ali Muayad¹ ameerfayz2532@gmail.com

Source: Journal of Computational Analysis & Applications. 2025, Vol. 34 Issue 8, p329-342. 14p.

Subject Terms: *CLIENT/SERVER computing, *GRAPH neural networks, *SUBGRAPHS, *CYBERTERRORISM, *BIPARTITE graphs, *INTRUSION detection systems (Computer security), *COST functions

DEEP LEARNING MODEL OPTIMIZATION FOR ANOMALY DETECTION IN IOT HEALTHCARE SYSTEMS.

Authors: SHARMA, MONIKA BINDAL, AMIT SHUKLA, SEEMA et al.

Source: TPM: Testing, Psychometrics, Methodology in Applied Psychology; 2025 Supplement, Vol. 32, p356-370, 15p