taxonomy of attacks, defences..1.1P Basic Linux Security SIT719 Security and Privacy Issues in...

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taxonomy of attacks, defences..1.1P Basic Linux Security     SIT719 Security and Privacy Issues in Analytics      Pass Task 7.1: Taxonomy of Attacks, Defenses, and  Consequences in Adversarial Machine Learning    Overview    The Information Technology Laboratory (ITL) at the National Institute of Standards and Technology  (NIST) promotes the U.S. economy and public welfare by providing technical leadership for the  Nation’s measurement and standards infrastructure. Recently NIST has published an internal  report on “A Taxonomy and Terminology of Adversarial Machine Learning” (link below). This NIST  Interagency/Internal Report (NISTIR) is intended as a step toward securing applications of Artificial  Intelligence (AI), especially against adversarial manipulations of Machine Learning (ML), by  developing a taxonomy and terminology of Adversarial Machine Learning (AML).      Link: https://nvlpubs.nist.gov/nistpubs/ir/2019/NIST.IR.8269-draft.pdf    Please see the details of the task in the Task Description section.    This is a Pass task, so you MUST complete the task and submit the evidence of your work to  Ontrack.     Task Description    Suppose you are working in an organization who are developing a report on the vulnerabilities of  machine learning models due to adversarial attacks. Your manager has asked you to provide a  600 word report to submit within the next week. His expectation is that the 600 word report will  cover the attack taxonomies, defense mechanisms and consequences.     Instructions:    1. Read the NIST article from the below link:     https://nvlpubs.nist.gov/nistpubs/ir/2019/NIST.IR.8269-draft.pdf    2. Identify five important attack types. Summarize in approx. 300 words.  https://nvlpubs.nist.gov/nistpubs/ir/2019/NIST.IR.8269-draft.pdf https://nvlpubs.nist.gov/nistpubs/ir/2019/NIST.IR.8269-draft.pdf       Hint: The above figure demonstrates the attack categories. It has been obtained from Figure  2 of the report.      2. Summarize the defense mechanisms for the attack types you identified in step 1. (Approx.  200 words)      Submit the report PDF to the OnTrack system.   	Overview 	Task Description

Neha · Accepted Answer

Attacks
When an Oracle attack occurs, an advisory uses an interface for the application programming to present the model which includes the inputs and to observe the output of the model. When the adversary has no direct knowledge about the model then the input output pairing can be obtained from the Oracle attack. It can be used to train a similar model which operates like the targeted model using the transferable property exhibited by multiple model architectures. 
The data extraction is mainly related with the input extraction and also known as the model in version for stock in this attack attacker find out the details about the data corpus on which a machine learning model was trained. The research performed in the deep learning mainly focuses on the model for the explosion of the data and the data is very crucial to train the behaviour of the system. 
The model extraction is a type of extraction attack on the model itself install when an attacker targets machine learning system which is not complete white box attempt the opening of the box and copy the parameters or behaviour of system.

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1.1P Basic Linux Security SIT719 Security and Privacy Issues in Analytics Pass Task 7.1: Taxonomy of Attacks, Defenses, and Consequences in Adversarial Machine Learning Overview The Information...

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