International Workshop on Machine Learning for Web Services Security (MLWSS)
Event submitted on Friday, July 24th 2020, approved by Content Team ✓
This event has been tagged as follows:
This physical event (“in-person”), based in Dubai, UAE, focuses on Web Services Security (WSS) and Machine Learning and cyber threats related to it. Machine-Learning continues to grow exponentially and so does the necessity to secure these AI/ML systems. We wish the organizers the best of luck with this event.
Conference Event Summary
The following description was either submitted by the Conference Organizer on Friday, July 24th 2020, or created by us.
The cybersecurity domain is pivotal for the world today due to its significance for the computer-centric social and business world. The domain poses numerous challenges, such as threat detection, privacy presentation, intrusion detection, etc.
With the rapid growth of the cyber world and the subsequent development of adversarial techniques, current cyber-threats are becoming more and more complicated and complex. For instance, for web services available over the internet via the World Wide Web and through other internet-based systems and protocols. For the improved and efficient usage of the cyber technologies, Web Services Security (WSS) is an important area for the cyber-security researchers in order to define security measures that prevent them from cyberattacks. However, it is challenging to achieve in the spectrum of paradigms in technologies providing our services through complex systems. Therefore, the explosion of web-based services has created unprecedented opportunities and thus essential security challenges.
The aim of this workshop is to provide a premier international platform for the wide range of experts including practitioners and academicians an essential linkage to web services, services science, and service orientation in most current IT-driven collaborations.
This workshop will focus on overfitting issues, such as architecture cost, design, algorithms, and methodologies to solicit original research work with a particular emphasis on the challenges and future trends in cybersecurity, particularly for web-based services security, using machine learning applications. The machine learning approach has proven to be suitable for the WSS because it can help learn information and behavior from the online and offline data sources in an automatic routine and reduce the workload of security threat analysis through human experts. In connection to this, emerging approaches, such as reinforcement learning, adaptive learning, deep learning, etc can be used for efficiently detecting any type of cyberthreat to WSS.