Workshop

Illinois Institute of Technology, United States

Workshop Chair: Dr. Marwan Omar, Associate Professor in Illinois Institute of Technology

As a satellite event of the CONF-SPML 2024 | International Conference on Signal Processing and Machine Learning, the offline Workshop organized by Illinois Institute of Technology (Chicago, United States) was held on January 15th, 2024. With the topic of Natural Language Processing (NLP), Dr. Marwan Omar, Associate Professor in Illinois Institute of Technology, chaired the Workshop, aiming to achieve the following three goals: (1) Introduce participants to the intersection of NLP and cybersecurity; (2) Provide a foundation in NLP concepts and techniques; (3) Explore NLP applications in cybersecurity. The participants had spirited debates and in-depth discussions about subjects like "Natural Language Processing (NLP), Cybersecurity, Machine learning, Feature engineering, Text classification, Sentiment analysis". Please check the Workshop's official website for further details.

Torrens University, Australia

Workshop Chair: Dr. Ammar Alazab, Senior Lecturer in Torrens University Australia

The CONF-SPML 2024 workshop, held on January 8th, 2024, brought together 25 students eager to delve into the realms of machine learning and its applications in advancing cybersecurity. The workshop, titled "Harnessing the Power of Machine Learning to Advance Cybersecurity," served as a dynamic platform for participants to explore the intersection of these two rapidly evolving fields.

Led by Dr. Amma Alazab, the workshop covered diverse aspects of machine learning relevant to cybersecurity, aiming to equip attendees with practical skills and theoretical knowledge. Topics included anomaly detection, threat intelligence, and the integration of machine learning algorithms into security frameworks. Through hands-on exercises and interactive sessions, participants gained valuable insights into leveraging machine learning techniques to bolster defense mechanisms against evolving cyber threats.

The workshop fostered a collaborative learning environment, allowing students to exchange ideas, troubleshoot challenges, and build a network within the cybersecurity and machine learning communities. As the participants delved into real-world case studies and emerging trends, they left with a deeper understanding of the role machine learning plays in fortifying cybersecurity measures. CONF-SPML 2024 provided a comprehensive and engaging experience, empowering the next generation of cybersecurity professionals with the tools needed to navigate the complex landscape of digital security.

University of Portsmouth, United Kingdom

Workshop Chair: Dr. Stavros Shiaeles, Reader in University of Portsmouth

A collaborative Intrusion Detection System (IDS) based on blockchain technology represents a groundbreaking approach in cybersecurity. This system leverages the inherent strengths of blockchain—decentralization, immutability, and transparency—to enhance the security and reliability of intrusion detection across various nodes in a network.

In this setup, each participating node contributes to and accesses a shared ledger, where data regarding potential security threats and anomalies are recorded. Thanks to blockchain's immutable nature, once information is logged, it cannot be altered retroactively. This ensures the integrity of the data, making it trustworthy for all participants. The decentralized structure of the blockchain means there's no single point of failure, significantly increasing the system's resilience to attacks and manipulation.

Blockchain's consensus mechanisms, such as Proof of Work or Proof of Stake, ensure that all data additions to the ledger are validated collectively, preventing false data injection and enhancing the overall accuracy of the IDS. Moreover, the transparent nature of blockchain allows participants to trace and verify each record's origins, fostering trust among the nodes.

Smart contracts on the blockchain can automate responses to detected threats, improving the system's responsiveness and efficiency. While offering transparency, blockchain-based IDS can also incorporate privacy-preserving techniques, such as zero-knowledge proofs, to protect sensitive data. This innovative amalgamation of blockchain with IDS paves the way for a more secure, collaborative approach to cyber defense.

China Agricultural University, China

Workshop Chair: Dr. Xinqing Xiao, Associate Professor in China Agricultural University

The workshop Machine Learning Based Smart Sensing and Applications was successfully held on Jan. 5, 2024 at the College of Engineering, China Agricultural University chaired by Assoc. Professor Xinqing Xiao. The 10 students attending the workshop were mainly doctor, master and international students from the discipline of mechanical and electronic engineering and agriculture engineering, accounting at China Agricultural University. The workshop showcased some of the latest research conducted in the area of machine learning and smart sensing technologies and application in agriculture as well as overlaps between the two subjects. Some of the topics discussed including examination of machine learning and smart sensing technologies, the application in food or fruit monitoring and the development trends in future. The workshop also has discussed the possible application in animals breeding such as the aquaculture, live sheep breeding and transportation etc. by using machine learning and smart sensing technologies.

Publications

Journals

We call for excellent papers and review them according to the requirements of each journal. Qualified submissions will be directly recommended for publication in the following journals or corresponding Special Issues. The journals listed below have been indexed by Science Citation Index (SCI)/Science Citation Index Expanded (SCI-E) and/or Social Science Citation Index (SSCI):

• JCRQ2 - Sensors:[Impact Factor: 3.9; CiteScore: 6.8]
• JCRQ2 - Electronics[Impact Factor: 2.9; CiteScore: 4.7]
• JCRQ2 - Machines[Impact Factor: 2.6; CiteScore: 2.1]

Conference proceedings

Accepted papers of CONF-SPML 2024 have been published in SPIE Conference Proceedings (print ISSN 0277-786X) or Applied and Computational Engineering (Print ISSN 2755-2721), and have been submitted to Ei Compendex, Conference Proceedings Citation Index (CPCI), Crossref, Portico, Inspec, Google Scholar, CNKI, and other databases for indexing.

Title: SPIE Conference Proceedings
Press: SPIE, United States
ISSN: 0277-786X (print) 1996-756X (electronic)
Title: Applied and Computational Engineering (ACE)
Press: EWA Publishing, United Kingdom
ISSN: 2755-2721 (Print) 2755-273X (electronic)