Search results
Results from the WOW.Com Content Network
In data sanitization, HTML sanitization is the process of examining an HTML document and producing a new HTML document that preserves only whatever tags and attributes are designated "safe" and desired. HTML sanitization can be used to protect against attacks such as cross-site scripting (XSS) by sanitizing any HTML code submitted by a user.
All of the client supplied input must be checked/sanitized of any characters that may result in malicious behavior. The input validation should verify the input by checking for the presence of special characters that are a part of the LDAP query language, known data types, legal values, etc. [ 2 ] White list input validation can also be used to ...
Data validation is intended to provide certain well-defined guarantees for fitness and consistency of data in an application or automated system. Data validation rules can be defined and designed using various methodologies, and be deployed in various contexts. [1]
Validating or "sanitizing" input, such as whitelisting known good values. This can be done on the client side, which is prone to modification by malicious users, or on the server side, which is more secure. Encoding input or escaping dangerous characters.
In general, data sanitization techniques use algorithms to detect anomalies and remove any suspicious points that may be poisoned data or sensitive information. Furthermore, data sanitization methods may remove useful, non-sensitive information, which then renders the sanitized dataset less useful and altered from the original.
User input validation: User input (gathered by any peripheral such as a keyboard, bio-metric sensor, etc.) is validated by checking if the input provided by the software operators or users meets the domain rules and constraints (such as data type, range, and format).
Following the flow of data between all the components of an application or group of applications allows validation of required calls to dedicated procedures for sanitization and that proper actions are taken to taint data in specific pieces of code. [12] [13]
Data cleaning differs from data validation in that validation almost invariably means data is rejected from the system at entry and is performed at the time of entry, rather than on batches of data. The actual process of data cleansing may involve removing typographical errors or validating and correcting values against a known list of entities.