RISK DETECTION AND AVOIDANCE FOR THE SELECTION AND DEVELOPMENT OF AN EXTRACT TRANSFORM LOAD TOOL

Abstract

Detecting malware has emerged as one of the most important challenges in the field of computer security. As Time bomb scripts (Techniques) are capable of working as well as run automatically on a predefine date and time during data operations, or during the Extract Transform Load (ETL) process in a data warehouse, a malicious script is being uploaded in which time bomb scripts will have the ability to work. The malware data oriented is hidden in the data warehouse by the hacklers and can be run on the hacker's chosen date and time which can lead to huge losses and huge financial losses, so there should be a solution to filter the data uploaded in the data warehouse Whether it's a plain or clean file because today's communications infrastructure is subject to intrusion by various malware infection tactics and attacks. One of the most difficult tasks in the field of computer security is detecting malware. This vulnerability will lead to the intrusion (and attack) of a successful computer system, on the other hand, leading to the success of more sophisticated attacks such as the Distributed Daniel of Service (DDoS) attack. Data mining methods can be used to eliminate the limitations of signature-based techniques in detecting zero-day malware. This study discusses in detail the malware and malware detection systems that use existing methods, such as data mining techniques, to identify known and undetected malware patterns.

Keywords (ETL, Data warehouse, Harmful Scripts, Cyber Security, Detection, Avoidance)