A protective mechanism for the access control system in the virtual domain

Shen, Jinan; Zou, Deqing; Jin, Hai; Yang, Kai; Yuan, Bin

doi:10.1109/cc.2016.7781724

Cited by 1 publication

(2 citation statements)

References 9 publications

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“…However, hybrid techniques are able to detect new types of vulnerabilities [11]. In recent times, hardware and software vendors have introduced many new techniques such as data execution protection, space layouts randomization, structured exception handler overwriting protection [12] and mandatory integrity control [13]. We claim that current evasion techniques can be easily bypassed and vendors are still in a developing phase in order to handle severe sophisticated attacks.…”

Section: Introductionmentioning

confidence: 99%

“…In applications such as banking, healthcare, and defense, not only the issues related to privacy is a serious concern, but also there are legal concerns that need to be considered as mentioned in the Health Insurance Portability and Accountability (HIPAA) act [15]. Within the current technological scenario, there is a well-known discipline called privacy-preserving' [13] and statistical disclosure control (SDC) [16]. When data is distributed, preserving privacy becomes more challenging.…”

Section: Introductionmentioning

confidence: 99%

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Applications in Security and Evasions in Machine Learning: A Survey

2020

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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.

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Section: Introductionmentioning

confidence: 99%