000			a
999			_c32468 _d32468
008			230831b xxu||||| |||| 00| 0 eng d
020			_a9783658404413
082			_a006.31 _bLAB
100			_aLabaca-Castro, Raphael
245			_aMachine learning under malware attack
260			_bSpringer, _c2023 _aWiesbaden :
300			_axxxiv, 116 p. ; _bill., _c21 cm
365			_b74.99 _cEUR _d94.90
504			_aIncludes bibliographical references.
520			_aMachine learning has become key in supporting decision-making processes across a wide array of applications, ranging from autonomous vehicles to malware detection. However, while highly accurate, these algorithms have been shown to exhibit vulnerabilities, in which they could be deceived to return preferred predictions. Therefore, carefully crafted adversarial objects may impact the trust of machine learning systems compromising the reliability of their predictions, irrespective of the field in which they are deployed. The goal of this book is to improve the understanding of adversarial attacks, particularly in the malware context, and leverage the knowledge to explore defenses against adaptive adversaries. Furthermore, to study systemic weaknesses that can improve the resilience of machine learning models.
650			_aMalware
650			_aComputer software
650			_aMachine learning
650			_aSafety measures
942			_2ddc _cBK