Comparative Study of Pre-trained Models on Cotton Plant Disease Detection Using Transfer Learning

Kumar, Satyam; Musharaf, Dayima; Sagar, Anil Kumar

doi:10.1007/978-981-19-2980-9_12

Cited by 2 publications

(1 citation statement)

References 14 publications

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“…The answer to that would be that the output of GAI is carefully calibrated combinations of the data fed to it to train its algorithm [20]. Due to the massive amount of data which goes into training the algorithm of GAI and the randomness with which it selects the elements of its output, its output appears to have creative and lifelike elements to it [21]. GAI models use advanced deep-learning techniques like Generative Artificial Networks, Variational Autoencoders, Transformers etc.…”

Section: An Overview Of Gaimentioning

confidence: 99%

Generative AI for Cyber Security: Analyzing the Potential of ChatGPT, DALL-E, and Other Models for Enhancing the Security Space

Sai,

Yashvardhan,

Chamola

et al. 2024

IEEE Access

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This research paper intends to provide real-life applications of Generative AI (GAI) in the cybersecurity domain. The frequency, sophistication and impact of cyber threats have continued to rise in today's world. This ever-evolving threat landscape poses challenges for organizations and security professionals who continue looking for better solutions to tackle these threats. GAI technology provides an effective way for them to address these issues in an automated manner with increasing efficiency. It enables them to work on more critical security aspects which require human intervention, while GAI systems deal with general threat situations. Further, GAI systems can better detect novel malware and threatening situations than humans. This feature of GAI, when leveraged, can lead to higher robustness of the security system. Many tech giants like Google, Microsoft etc., are motivated by this idea and are incorporating elements of GAI in their cybersecurity systems to make them more efficient in dealing with ever-evolving threats. Many cybersecurity tools like Google Cloud Security AI Workbench, Microsoft Security Copilot, SentinelOne Purple AI etc., have come into the picture, which leverage GAI to develop more straightforward and robust ways to deal with emerging cybersecurity perils. With the advent of GAI in the cybersecurity domain, one also needs to take into account the limitations and drawbacks that such systems have. This paper also provides some of the limitations of GAI, like periodically giving wrong results, costly training, the potential of GAI being used by malicious actors for illicit activities etc.

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Section: An Overview Of Gaimentioning

confidence: 99%