Machine learning has witnessed tremendous growth in its adoption and advancement in the last decade. The evolution of machine learning from traditional algorithms to modern deep learning architectures has shaped the way today's technology functions. Its unprecedented ability to discover knowledge/patterns from unstructured data and automate the decision-making process led to its application in wide domains. High flying machine learning arena has been recently pegged back by the introduction of adversarial attacks. Adversaries are able to modify data, maximizing the classification error of the models. The discovery of blind spots in machine learning models has been exploited by adversarial attackers by generating subtle intentional perturbations in test samples. Increasing dependency on data has paved the blueprint for ever-high incentives to camouflage machine learning models. To cope with probable catastrophic consequences in the future, continuous research is required to find vulnerabilities in form of adversarial and design remedies in systems. This survey aims at providing the encyclopedic introduction to adversarial attacks that are carried out against malware detection systems. The paper will introduce various machine learning techniques used to generate adversarial and explain the structure of target files. The survey will also model the threat posed by the adversary and followed by brief descriptions of widely accepted adversarial algorithms. Work will provide a taxonomy of adversarial evasion attacks on the basis of attack domain and adversarial generation techniques. Adversarial evasion attacks carried out against malware detectors will be discussed briefly under each taxonomical headings and compared with concomitant researches. Analyzing the current research challenges in an adversarial generation, the survey will conclude by pinpointing the open future research directions.
翻译:在过去十年里,机器学习的采用和进步有了巨大的增长。机器学习过程从传统算法演变成现代深层次学习结构的演变决定了今天的技术功能。机器学习从传统算法演变成现代深层次学习结构,其从无结构的数据中发现知识/模式的空前能力,并使决策过程自动化,从而将其应用于广泛的领域。高飞行机学习舞台最近因引入对抗性攻击而倒退。对立体能够修改数据,使模型的分类错误最大化。在机器学习模型中发现盲点,被敌对攻击者利用,在试样中产生微妙的故意扰动。对数据的日益依赖为日益高的模拟机器学习模型的激励铺平了蓝图。为了应对未来可能发生的灾难性后果,需要进行持续研究,以找出系统对抗性攻击和设计补救办法等形式的弱点。本调查的目的是为对抗性攻击的对抗性攻击提供百科入门介绍,将采用各种公开的机器学习技术来产生对抗性对抗性和解释目标文件的结构。调查还将在对冲和敌对性攻击的每次反向性研究中,通过广泛接受的对冲性研究,对冲和对冲性攻击的对冲性研究,将得出对冲性攻击的对冲性攻击的对冲性攻击的对冲性研究,对冲性研究将用对冲性攻击的对冲性攻击的对冲性攻击的对冲性攻击的对冲性攻击的对冲性攻击的对冲性研究将作出最后的对冲性研究,通过。