Abstract
In recent years, machine learning (ML) has become an important part to yield security and privacy in various applications. ML is used to address serious issues such as real-time attack detection, data leakage vulnerability assessments and many more. ML extensively supports the demanding requirements of the current scenario of security and privacy across a range of areas such as real-time decision-making, big data processing, reduced cycle time for learning, cost-efficiency and error-free processing. Therefore, in this paper, we review the state of the art approaches where ML is applicable more effectively to fulfill current real-world requirements in security. We examine different security applications’ perspectives where ML models play an essential role and compare, with different possible dimensions, their accuracy results. By analyzing ML algorithms in security application it provides a blueprint for an interdisciplinary research area. Even with the use of current sophisticated technology and tools, attackers can evade the ML models by committing adversarial attacks. Therefore, requirements rise to assess the vulnerability in the ML models to cope up with the adversarial attacks at the time of development. Accordingly, as a supplement to this point, we also analyze the different types of adversarial attacks on the ML models. To give proper visualization of security properties, we have represented the threat model and defense strategies against adversarial attack methods. Moreover, we illustrate the adversarial attacks based on the attackers’ knowledge about the model and addressed the point of the model at which possible attacks may be committed. Finally, we also investigate different types of properties of the adversarial attacks.
Highlights
The present-day community accesses advanced technologies, both hardware, and software, at an unprecedented pace in possibly every imaginable field
While in this survey based on that circumstance this survey combines different security applications and studies and carries out comprehensive summery in terms of tables based on the various parameters
Searched in Metadata; 2 Search topics filtered by subjects: Computer Science; Security Applications; Privacy; Machine Learning; Adversarial attacks
Summary
The present-day community accesses advanced technologies, both hardware, and software, at an unprecedented pace in possibly every imaginable field. The fundamental difference between previous surveys which have been proposed by authors, most of them only involve only security threats, internal issues of the machine learning systems in terms of adversarial defense While in this survey based on that circumstance this survey combines different security applications and studies and carries out comprehensive summery in terms of tables based on the various parameters. We emphasize a detailed review of security application with its performance matrices comparison as well as data distribution drifting leads by adversarial samples and private information transgression problem and its defense with attack model. This survey, as a complete summary combines numerous references and provides a macro understanding and interrelationship of security applications and machine learning related fields. We review adversarial defense techniques for the different types of adversarial attacks that cover both reactive and proactive types of defense techniques
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