Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
4
datasets available to search
ShareScore release 0.9.0
Dataset results
4 results for “sql injection”
SQL Injection Test (D3)
<p>This dataset has SQL injection attacks as malicious flow data. The attacks carried out are SQL injection for Union Query and Blind SQL injection. To perform the attacks, the SQLmap tool has been used.</p>
SQL Injection Attack Netflow
<p><strong>Introduction</strong></p> <p>This datasets have SQL injection attacks (SLQIA) as malicious Netflow data. The attacks carried out are SQL injection for Union Query and Blind SQL injection. To perform the attacks, the SQLMAP tool has been used.</p> <p>NetFlow traffic has generated using DOROTHEA (DOcker-based fRamework fOr gaTHering nEtflow trAffic). NetFlow is a network protocol developed by Cisco for the collection and monitoring of network traffic flow data generated. A flow is defined as a unidirectional sequence of packets with some common properties that pass through a network device.</p> <p><strong>Datasets</strong></p> <p>The firts dataset was colleted to train the detection models (<strong>D1</strong>) and other collected using different attacks than those used in training to test the models and ensure their generalization (<strong>D2</strong>).</p> <p>The datasets contain both benign and malicious traffic. All collected datasets are balanced.</p> <p>The version of NetFlow used to build the datasets is 5.</p> <table> <thead> <tr> <th scope="col">Dataset</th> <th scope="col">Aim</th> <th scope="col">Samples</th> <th scope="col">Benign-malicious<br> traffic ratio</th> </tr> </thead> <tbody> <tr> <td>D1</td> <td>Training</td> <td>400,003</td> <td>50%</td> </tr> <tr> <td>D2</td> <td>Test</td> <td>57,239</td> <td>50%</td> </tr> </tbody> </table> <p> </p> <p><strong>Infrastructure and implementation</strong></p> <p>Two sets of flow data were collected with <a href="https://niebla.unileon.es/cybersecurity/dorothea">DOROTHEA</a>. DOROTHEA is a Docker-based framework for NetFlow data collection. It allows you to build interconnected virtual networks to generate and collect flow data using the NetFlow protocol. In DOROTHEA, network traffic packets are sent to a NetFlow generator that has a sensor<a href="https://github.com/aabc/ipt-netflow"><em> ipt_netflow</em></a> installed. The sensor consists of a module for the Linux kernel using Iptables, which processes the packets and converts them to NetFlow flows.</p> <p>DOROTHEA is configured to use Netflow V5 and export the flow after it is inactive for 15 seconds or after the flow is active for 1800 seconds (30 minutes)</p> <p>Benign traffic generation nodes simulate network traffic generated by real users, performing tasks such as searching in web browsers, sending emails, or establishing Secure Shell (SSH) connections. Such tasks run as Python scripts. Users may customize them or even incorporate their own. The network traffic is managed by a gateway that performs two main tasks. On the one hand, it routes packets to the Internet. On the other hand, it sends it to a NetFlow data generation node (this process is carried out similarly to packets received from the Internet).</p> <p>The malicious traffic collected (SQLI attacks) was performed using <a href="https://sqlmap.org/">SQLMAP</a>. SQLMAP is a penetration tool used to automate the process of detecting and exploiting SQL injection vulnerabilities.</p> <p>The attacks were executed on 16 nodes and launch SQLMAP with the parameters of the following table.</p> <table> <thead> <tr> <th scope="col">Parameters</th> <th scope="col">Description</th> </tr> </thead> <tbody> <tr> <td>'--banner','--current-user','--current-db','--hostname','--is-dba','--users','--passwords','--privileges','--roles','--dbs','--tables','--columns','--schema','--count','--dump','--comments', --schema'</td> <td>Enumerate users, password hashes, privileges, roles, databases, tables and columns</td> </tr> <tr> <td>--level=5</td> <td>Increase the probability of a false positive identification</td> </tr> <tr> <td>--risk=3</td> <td>Increase the probability of extracting data</td> </tr> <tr> <td>--random-agent</td> <td>Select the User-Agent randomly</td> </tr> <tr> <td>--batch</td> <td>Never ask for user input, use the default behavior</td> </tr> <tr> <td>--answers="follow=Y"</td> <td>Predefined answers to yes</td> </tr> </tbody> </table> <p>Every node executed SQLIA on 200 victim nodes. The victim nodes had deployed a web form vulnerable to Union-type injection attacks, which was connected to the <a href="https://www.mysql.com/">MYSQL </a>or <a href="https://www.microsoft.com/es-es/sql-server/sql-server-2019">SQLServer </a>database engines (50% of the victim nodes deployed MySQL and the other 50% deployed SQLServer).</p> <p>The web service was accessible from ports 443 and 80, which are the ports typically used to deploy web services. The IP address space was 182.168.1.1/24 for the benign and malicious traffic-generating nodes. For victim nodes, the address space was 126.52.30.0/24.<br> The malicious traffic in the test sets was collected under different conditions. For <strong>D1</strong>, SQLIA was performed using Union attacks on the MySQL and SQLServer databases.</p> <p>However, for <strong>D2</strong>, BlindSQL SQLIAs were performed against the web form connected to a <a href="https://www.postgresql.org/">PostgreSQL </a>database. The IP address spaces of the networks were also different from those of <strong>D1</strong>. In <strong>D2</strong>, the IP address space was 152.148.48.1/24 for benign and malicious traffic generating nodes and 140.30.20.1/24 for victim nodes.</p> <p>To run the MySQL server we ran <a href="https://mariadb.org/">MariaDB</a> version 10.4.12.<br> Microsoft SQL Server 2017 Express and PostgreSQL version 13 were used.</p>
METHODS FOR PREVENTING SQL INJECTION IN IDENTITY AND ACCESS MANAGEMENT (IAM) SYSTEMS
<p>This paper discusses methods for preventing SQL (Structured Query Language) injections in identity and access control (IAM) systems. SQL injections represent one of the most serious threats to web security, allowing attackers to gain unauthorized access to and modify data. The main security methods include filtering input data, using prepared statements and parameterization, implementing stored procedures, restricting access rights, and regularly updating software. Effective privilege management and database activity monitoring also play a key role in preventing attacks. The introduction of these measures helps protect confidential information, ensures reliable authentication and authorization, and maintains data integrity. The paper highlights the importance of an integrated approach to database security in the face of growing cyber threats.</p>
Appendix to Combinatorial Methods for Dynamic Gray-Box SQL Injection Testing
<p>This appendix contains the detailed results of our case study performed in the context of our article "Combinatorial Methods for Dynamic Gray-Box SQL Injection Testing", published in the Journal of Software Testing, Verification and Reliability.</p> <p><br> For each web application investigated, one table exists, provided as a separate PDF file.<br> In each of these tables, the results are grouped by endpoint. For each investigated approach, we list the total number of submitted vectors, the number of injected vectors and the injection rate, the amount of executed vectors and the execution rate, and the total time required to submit the attack vectors and evaluate the responses.</p> <p>Please note that no false positive endpoints are listed for WAVSEP. Furthermore, some entries for the execution rate may be displayed as 0.00 despite some vectors having been executed; this is merely a consequence of rounding.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.