Treffer: Design and implementation of an intelligent and secure vulnerability assessment framework.

The decreasing cost of distributed computing technology, network connectivity, and sensors has led to the development of smart applications that link providers and consumers, offering remote monitoring and control. All of the key aspects of our life, including our homes, wellbeing, cities, farms, grid lines, transportation, and production, are about to undergo significant change due to the Internet of Things. The public has become aware of an increase in cyber-attacks in recent years due to an unprecedently high number of IoT devices. However, in comparison to our increasing reliance on smart applications, the methods for protecting them are developing quite slowly. Furthermore, these applications' weaknesses are so obvious that they may be used in a chain to penetrate further into the smart network and have much more negative effects. In order to reap the benefits of smart services, it is imperative to evaluate and eliminate these possible hazards. Conventional security systems fail to detect novel threat variants and guarantee data integrity in uncertain smart environments. Therefore, developing and investigating alternate approaches for sustainable IoT is imperative. The best technique to identify these risks has always been machine learning (ML), given its potential. However, general machine learning (ML)-based methods have been falling short in identifying unknown threats in real-world applications because of changes in domains, different distributions, the requirement for labelled data, and lengthy training cycles. Transfer learning, or TL, has become a successful remedy for the aforementioned issues. [ABSTRACT FROM AUTHOR]

Copyright of AIP Conference Proceedings is the property of American Institute of Physics and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)