A review of generative adversarial networks and its application in cybersecurity

Yinka-Banjo, Chika; Ugot, Ogban-Asuquo

doi:10.1007/s10462-019-09717-4

Cited by 103 publications

(39 citation statements)

References 13 publications

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“…GAN's have become of interest in cybersecurity especially as DL continues to be a ML methodology of choice in recent years. Yinka-Banjo et al [97] posited that the application of GAN's in cybersecurity is a developing research field. They authors in [99] also stated that beyond the possibility of GANs to generate fake data to fool a security system, they can also be used to defend systems.…”

Section: B Generative Adversarial Network (Gan) For Resilient Cpsmentioning

confidence: 99%

Resilient Machine Learning for Networked Cyber Physical Systems: A Survey for Machine Learning Security to Securing Machine Learning for CPS

Olowononi

Rawat

Liu

2021

IEEE Commun. Surv. Tutorials

131

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Cyber Physical Systems (CPS) are characterized by their ability to integrate the physical and information or cyber worlds. Their deployment in critical infrastructure have demonstrated a potential to transform the world. However, harnessing this potential is limited by their critical nature and the far reaching effects of cyber attacks on human, infrastructure and the environment. An attraction for cyber concerns in CPS rises from the process of sending information from sensors to actuators over the wireless communication medium, thereby widening the attack surface. Traditionally, CPS security has been investigated from the perspective of preventing intruders from gaining access to the system using cryptography and other access control techniques. Most research work have therefore focused on the detection of attacks in CPS. However, in a world of increasing adversaries, it is becoming more difficult to totally prevent CPS from adversarial attacks, hence the need to focus on making CPS resilient. Resilient CPS are designed to withstand disruptions and remain functional despite the operation of adversaries. One of the dominant methodologies explored for building resilient CPS is dependent on machine learning (ML) algorithms. However, rising from recent research in adversarial ML, we posit that ML algorithms for securing CPS must themselves be resilient. This paper is therefore aimed at comprehensively surveying the interactions between resilient CPS using ML and resilient ML when applied in CPS. The paper concludes with a number of research trends and promising future research directions. Furthermore, with this paper, readers can have a thorough understanding of recent advances on ML-based security and securing ML for CPS and countermeasures, as well as research trends in this active research area.

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Section: B Generative Adversarial Network (Gan) For Resilient Cpsmentioning

confidence: 99%

Resilient Machine Learning for Networked Cyber Physical Systems: A Survey for Machine Learning Security to Securing Machine Learning for CPS

Olowononi

Rawat

Liu

2021

IEEE Commun. Surv. Tutorials

131

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“…This is accomplished through evaluating the probability of the data being in one category given certain features, p (category|feature). GANs are often used in cybersecurity and health diagnostics [48,49].…”

Section: Supervised Learning (Labeled Data)mentioning

confidence: 99%

Artificial Intelligence in Drug Design: Algorithms, Applications, Challenges and Ethics

Arabi

2021

Future Drug. Discov.

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The discovery paradigm of drugs is rapidly growing due to advances in machine learning (ML) and artificial intelligence (AI). This review covers myriad faces of AI and ML in drug design. There is a plethora of AI algorithms, the most common of which are summarized in this review. In addition, AI is fraught with challenges that are highlighted along with plausible solutions to them. Examples are provided to illustrate the use of AI and ML in drug discovery and in predicting drug properties such as binding affinities and interactions, solubility, toxicology, blood–brain barrier permeability and chemical properties. The review also includes examples depicting the implementation of AI and ML in tackling intractable diseases such as COVID-19, cancer and Alzheimer’s disease. Ethical considerations and future perspectives of AI are also covered in this review.

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“…In the context of image processing and computer vision, GANs have been applied to fake image generation, super resolution, image synthesis and manipulation, texture synthesis, object detection, and video applications. 25 The framework presented in this paper falls into the category of fake image generation.…”

Section: Generative Adversarial Networkmentioning

confidence: 99%

Privacy preservation for image data: A GAN‐based method

et al. 2021

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The importance of protecting personal information, like, a person's address or health history, is well known and commonly discussed. However, images also contain sensitive information that can compromise a person's privacy or be used for nefarious purposes. To date, most methods for preserving privacy with images have relied on obfuscation techniques, such as pixelation, blurring, or masking parts of the image. However,

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A review of generative adversarial networks and its application in cybersecurity

Cited by 103 publications

References 13 publications

Resilient Machine Learning for Networked Cyber Physical Systems: A Survey for Machine Learning Security to Securing Machine Learning for CPS

Resilient Machine Learning for Networked Cyber Physical Systems: A Survey for Machine Learning Security to Securing Machine Learning for CPS

Artificial Intelligence in Drug Design: Algorithms, Applications, Challenges and Ethics

Privacy preservation for image data: A GAN‐based method

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