000 02299cam  2200409zi 4500
0019.881081
003CaOODSP
00520221107165940
006m     o  d f      
007cr |n|||||||||
008191024t20192018onc     ob   f000 0 eng d
040 |aCaOODSP|beng|erda|cCaOODSP
041 |aeng|bfre
043 |an-cn---
0861 |aD68-3/067-2019E-PDF
1001 |aMontuno, Delfin Y., |eauthor.
24510|aMachine learning in vulnerability assessment / |cDelfin Montuno.
264 1|aOttawa : |bDefence Research and Development Canada = Recherche et développement pour la défense Canada, |c2019.
264 4|c©2018
300 |a1 online resource (18 pages).
336 |atext|btxt|2rdacontent
337 |acomputer|bc|2rdamedia
338 |aonline resource|bcr|2rdacarrier
4901 |aContract report ; |vDRDC-RDDC-2019-C067
500 |a"Can unclassified."
500 |a"April 2019."
500 |aTitle from cover.
500 |a"PSPC Contract Number: W7714-115274/001/SV."
504 |aIncludes bibliographical references (pages 11-15).
5203 |a"Machine Learning (ML) is increasingly being applied in vulnerability assessment and more generally in providing cyber security. We review ML applications in both those areas by commercial vendors. We also review recent results in adversarial learning; since ML requires training data to be effective, it is susceptible to adversarial attacks in which that data is poisoned to impair the ML’s functionality or allow attackers to bypass it. As a result of this adversarial nature of the problem, we conclude that the automated nature of ML-based solutions increases the need for accurate ground truth input data, and that more research is required to ensure the safety and effectiveness of these approaches with human-machine cooperation in mind"--Abstract, page i.
546 |aIncludes abstract in French.
69207|2gccst|aInformation technology
69207|2gccst|aMilitary technology
7101 |aCanada. |bDefence R&D Canada. |bOttawa Research Centre.
7102 |aSolan Networks.
830#0|aContract report (Defence R&D Canada)|vDRDC-RDDC-2019-C067.|w(CaOODSP)9.802312
85640|qPDF|s431 KB|uhttps://publications.gc.ca/collections/collection_2019/rddc-drdc/D68-3-067-2019-eng.pdf