Predicting and preventing adversaries in machine learning (Record no. 237113)
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| 041 ## - LANGUAGE CODE | |
| Language code of text/sound track or separate title | English |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Nisha, D. |
| 245 #0 - TITLE STATEMENT | |
| Title | Predicting and preventing adversaries in machine learning |
| Remainder of title | Sivaraman, E. |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. | |
| Date of publication, distribution, etc. | Jan-May 2019 |
| Name of publisher, distributor, etc. | PES University |
| Place of publication, distribution, etc. | Bengaluru |
| 510 ## - CITATION/REFERENCES NOTE | |
| Name of source | Machine learning is a major area in artificial intelligence, which enables computer to learn itself explicitly without programming. As machine learning is widely used in making decision automatically, attackers have strong intention to manipulate the prediction generated by machine learning model. In this paper we study about the different types of attacks and its countermeasures on machine learning model. By research we found that there are many security threats in various algorithms such as K-Nearest-Neighbors (KNN) classifier, Random Forest, AdaBoost, Support Vector Machine (SVM), Decision Tree, we revisit existing security threads and check what are the possible countermeasures during the training and prediction phase of machine learning model In any machine learning model, there is 2 phase one is the training phase and the other one is the testing phase. So, the attacker tries to attack the training phase that is called as causative or poisoning attack and the attack on |
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| Koha item type | Compact Disks |
| Date last seen | Total checkouts | Accession number | Price effective from | Category | Lost status | Damaged status | Withdrawn status | Home library | Current library | Entry date | Grants | Currency symbol | Conversion rate | Discount |
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| 26/01/2024 | CD7240 | 26/01/2024 | Compact Disks | Central Library | Central Library | CS/PG | RS | 1 |