BI-Validator Usecase - Stress Test PlanDatagaps Inc
BI-Validator's Stress Test Plan can simulate user load with out the need for writing any scripts/code. With multiple settings and options, it is easy to replicate real-time user behavior and see how the BI Tool will perform.
This document contains some basic scenarios which should be tested while testing any BI (Business Intelligence) reports.
Prepared By:
Rakesh Hansalia
in.linkedin.com/in/rakeshhansalia/
ETL Validator Usecase - Validating Measures, Counts with VarianceDatagaps Inc
ETL Validator gives quick and easy way to create test cases for comparing counts and measures of source & target data sources. A variance can be specified too. Here, we will create a Checksum test case that will compare measures and counts. The same functionality is also implemented in Component test case using 'Measure Validation'.
Constraints are the rules enforced on the data columns of a table. These are used to limit the type of data that can go into a table. This ensures the accuracy and reliability of the data in the database.
Constraints can be divided into following two types:
Column level constraints : limits only column data
Table level constraints : limits whole table data
Aggregate Functions
There are no systems that are connected to the Internet that are completely safe. Cyber-attacks are the norm. Everyone with a web presence is attacked multiple times each week. To further complicate this scenario, government entities have been found to be weakening Web security protocols and compromising business systems in the interest of national security, and hyper-competitive companies have been caught engaging in cyber-espionage. Detection of these attacks in real-time is difficult due to a number of reasons. The primary ones being the dynamism and ingenuity of the attacker and the nature of contemporary real-time attack detection systems. In this talk, I will share insights on an alternative, i.e. quickly recognizing attacks in a short period of time after the incident using audit analysis.
Data Warehousing in Pharma: How to Find Bad Data while Meeting Regulatory Req...RTTS
In the U.S., pharmaceutical firms must meet electronic record-keeping regulations set by the Food and Drug Administration (FDA). The regulation is Title 21 CFR Part 11, commonly known as Part 11.
Part 11 requires regulated firms to implement controls for software and systems involved in processing many forms of data as part of business operations and product development.
Enterprise data warehouses are used by the pharmaceutical and medical device industries for storing data covered by Part 11. QuerySurge, the only test tool designed specifically for automating the testing of data warehouses and the ETL process, is the market leader in testing data warehouses used by Part 11-governed companies.
For more on QuerySurge and Pharma, please visit
http://www.querysurge.com/solutions/pharmaceutical-industry
Hello Guys,
This is the presentation I gave at the Test Tribe Meetup on 22nd of September 2018 at Andheri, Mumbai. The presentation is about using Owasp top 10 we will: Define the vulnerabilities, Demonstrate the vulnerabilities and how to protect against them.
This paper presents a comparative analysis of various machine learning classification models for
structured query language injection prevention. The objective is to identify the best-performing model in
terms of accuracy on a given dataset. The study utilizes popular classifiers such as Logistic Regression,
Naive Bayes, Decision Tree, Random Forest, K-Nearest Neighbors, and Support Vector Machine. Based on
the tests used to evaluate the performance of the classifiers, the Naïve Bayes gets the highest level of
accurate detection. The results show a 97.06% detection rate for the Naïve Bayes, followed by
LogisticRegression (0.9610), Support Vector Machine (0.9586), RandomForest (0.9530), DecisionTree
(0.9069), and K-Nearest Neighbor (0.6937). The code snippet provided demonstrates the implementation
and evaluation of these models.
BI-Validator Usecase - Stress Test PlanDatagaps Inc
BI-Validator's Stress Test Plan can simulate user load with out the need for writing any scripts/code. With multiple settings and options, it is easy to replicate real-time user behavior and see how the BI Tool will perform.
This document contains some basic scenarios which should be tested while testing any BI (Business Intelligence) reports.
Prepared By:
Rakesh Hansalia
in.linkedin.com/in/rakeshhansalia/
ETL Validator Usecase - Validating Measures, Counts with VarianceDatagaps Inc
ETL Validator gives quick and easy way to create test cases for comparing counts and measures of source & target data sources. A variance can be specified too. Here, we will create a Checksum test case that will compare measures and counts. The same functionality is also implemented in Component test case using 'Measure Validation'.
Constraints are the rules enforced on the data columns of a table. These are used to limit the type of data that can go into a table. This ensures the accuracy and reliability of the data in the database.
Constraints can be divided into following two types:
Column level constraints : limits only column data
Table level constraints : limits whole table data
Aggregate Functions
There are no systems that are connected to the Internet that are completely safe. Cyber-attacks are the norm. Everyone with a web presence is attacked multiple times each week. To further complicate this scenario, government entities have been found to be weakening Web security protocols and compromising business systems in the interest of national security, and hyper-competitive companies have been caught engaging in cyber-espionage. Detection of these attacks in real-time is difficult due to a number of reasons. The primary ones being the dynamism and ingenuity of the attacker and the nature of contemporary real-time attack detection systems. In this talk, I will share insights on an alternative, i.e. quickly recognizing attacks in a short period of time after the incident using audit analysis.
Data Warehousing in Pharma: How to Find Bad Data while Meeting Regulatory Req...RTTS
In the U.S., pharmaceutical firms must meet electronic record-keeping regulations set by the Food and Drug Administration (FDA). The regulation is Title 21 CFR Part 11, commonly known as Part 11.
Part 11 requires regulated firms to implement controls for software and systems involved in processing many forms of data as part of business operations and product development.
Enterprise data warehouses are used by the pharmaceutical and medical device industries for storing data covered by Part 11. QuerySurge, the only test tool designed specifically for automating the testing of data warehouses and the ETL process, is the market leader in testing data warehouses used by Part 11-governed companies.
For more on QuerySurge and Pharma, please visit
http://www.querysurge.com/solutions/pharmaceutical-industry
Hello Guys,
This is the presentation I gave at the Test Tribe Meetup on 22nd of September 2018 at Andheri, Mumbai. The presentation is about using Owasp top 10 we will: Define the vulnerabilities, Demonstrate the vulnerabilities and how to protect against them.
This paper presents a comparative analysis of various machine learning classification models for
structured query language injection prevention. The objective is to identify the best-performing model in
terms of accuracy on a given dataset. The study utilizes popular classifiers such as Logistic Regression,
Naive Bayes, Decision Tree, Random Forest, K-Nearest Neighbors, and Support Vector Machine. Based on
the tests used to evaluate the performance of the classifiers, the Naïve Bayes gets the highest level of
accurate detection. The results show a 97.06% detection rate for the Naïve Bayes, followed by
LogisticRegression (0.9610), Support Vector Machine (0.9586), RandomForest (0.9530), DecisionTree
(0.9069), and K-Nearest Neighbor (0.6937). The code snippet provided demonstrates the implementation
and evaluation of these models.
SQL injection is the major susceptible attack in today’s era of web application which attacks the database to gain unauthorized and illicit access. It works as an intermediate between web application and database. Most of the time, well-known people fire the SQL injection, who is previously working in the organisation on the present database. Today organisation has major concern is to stop SQL injection because it is the major vulnerable attack in the database. SQLI attacks target databases that are reachable through web front. SQLI prevention technique efficiently blocked all of the attacks without generating any false positive. In this paper we present different techniques and tools which can prevent various attacks.
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SQL Injection Attack Detection and Prevention Techniques to Secure Web-Siteijtsrd
Structured Query Language (SQL) Injection is a code injection technique that exploits security vulnerability occurring in database layer of web applications [8]. According to Open Web Application Security Projects (OWASP), SQL Injection is one of top 10 web based attacks [10]. This paper shows the basics of SQL Injection attack, types of SQL Injection Attack according to their classification. It also describes the survey of different SQL Injection attack detection and prevention. At the end of this paper, the comparison of different SQL Injection Attack detection and prevention is shown. Mr. Vishal Andodariya"SQL Injection Attack Detection and Prevention Techniques to Secure Web-Site" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-4 , June 2018, URL: http://www.ijtsrd.com/papers/ijtsrd13034.pdf http://www.ijtsrd.com/computer-science/computer-security/13034/sql-injection-attack-detection-and-prevention-techniques-to-secure-web-site/mr-vishal-andodariya
SQL injection attack is the most common and difficult to handle attacks now days. SQL injection attack is of five types. In these paper details of SQL injection is mentioned.
1. DATABASE DEVELOPMENT (IT203P)
WESTLAKE DATABASE
(2015)
Juanita M. McConnell
Computer Network Systems
ITT Technical Institute
Philadelphia PA 19106
Contact:
JMcConnell152@email.itt-tech.edu
2. WESTLAKE DATABASE PROJECT OBJECTIVES
1. Creating a table with a primary key
2. Defining a Primary Key
3. Types of Primary Keys
4. Creating a stored procedure to guard against
SQL injection attacks.
5. Executing queries to meet requirements.
3. CREATING A TABLE WITH A PRIMARY KEY
You can create a table using the CREATE TABLE command or by right-clicking the
Tables folder and running New Table. You can assign primary key functions to attributes
by selecting the golden key icon in column adjacent to Column Name.
4. PRIMARY KEYS
A primary key is an attribute that uniquely identifies an entity.
It should have a unique value, very uneasy to guess, not ever
change, and be previously non-existent or non-established.
5. TYPES OF PRIMARY KEYS
Five Types of Primary Keys
Composite Primary Key – a primary key that is made up of more than one attribute.
Surrogate Primary Key – a primary key that is computer generated and usually numeric.
Natural Key – a real world identifier such as an email address.
Candidate Key- a primary key with the capabilities to be a primary key.
Foreign Key – a primary key assigned to a second table as a secondary key formatting a
connection between the tables.
6. CREATING A STORED PROCEDURE TO
GUARD AGAINST SQL INJECTION ATTACKS
A SQL injection attack is the addition of malicious code to a
SQL statement with the intention to corrupt the database.
• To combat intrusions, data analysts can use stored procedures
to guard against SQL injection attacks.
• Stored procedures help protect against SQL injection because
queries will only accept values of the data type specified in the
parameter and multiple statements will not be executed as a
batch.
7. CREATING A STORED PROCEDURE TO
GUARD AGAINST SQL INJECTION ATTACKS
This a query statement designed to guard against a DELETE statement that
would execute all data from the Patients table, if the DELETE permission had
been compromised.
8. CREATING A STORED PROCEDURE TO
GUARD AGAINST SQL INJECTION ATTACKS
This a query statement designed to copy records from
RecordPatientSymptoms and insert them into the PatientVisitSymptoms table.
9. EXECUTING QUERIES TO MEET
REQUIREMENTS I
You can use a SELECT statement to retrieve data.
It supports the following clauses:
FROM—identify the table
ORDER BY—sort the data
WHERE – filter the data
LIKE—retrieve data that matches a pattern =, <, >—retrieve
data that meets a condition BETWEEN—retrieve data in a
range
JOIN—join two tables together
GROUP BY—group aggregated data
10. EXECUTING QUERIES TO MEET
REQUIREMENTS I
This is a query statement designed to retrieve the visit date and patient key for all patients that
reported a depression level over 5.
11. EXECUTING QUERIES TO MEET
REQUIREMENTS II
This is a query statement designed to retrieve the visit date and patient name, and depression
levels for all patients that reported a depression level over 5.
12. EXECUTING QUERIES TO MEET
REQUIREMENTS III
This is a query statement designed to retrieve the number of patient visits that occurred in which
the patient had a pulse over 45.
13. EXECUTING QUERIES TO MEET
REQUIREMENTS IV
This is a query statement designed to lists the visit date and patient name for each patient visit,
sorted by depression level from lowest to highest.
14. EXECUTING QUERIES TO MEET
REQUIREMENTS V
This is a query designed to display the patient’s last name, average pulse and average depression
level for each patient.
15.
16. DATABASE DEVELOPMENT (IT203P)
WESTLAKE DATABASE
(2015)
Author Note
Juanita M. McConnell, Computer Network Systems, ITT Technical Institute.
Juanita McConnell is a student at ITT Technical Institute studying Computer
Networking, Computer Infrastructure and Computer Programming.
Correspondence concerning this PowerPoint should be addressed to
Juanita McConnell ,
Computer Network Systems,
ITT Technical Institute, 105 South 7th St., Suite 100 Philadelphia, PA 19106
Contact: JMcConnell152@email.itt-tech.edu