The last decade has been a critical one regarding cybersecurity, with studies predicting the worldwide cost of global cybercrime damage to hit 0.5 trillion dollars annually by 2025. Detecting, analysing, and defending against threats in (near) real-time conditions is impossible without employing machine learning techniques and big data infrastructures. This gives rise to cyberthreat intelligence and analytic solutions, such as (informed) machine learning on big data and open-source intelligence, to perceive, reason, learn, and act against cyber adversary techniques and actions. Moreover, organisations’ security analysts have to manage and protect systems and deal with the privacy and security of all personal and institutional data under their control. This workshop aims to provide researchers with a forum to exchange and discuss scientific contributions, open challenges and recent achievements in machine learning and their role in developing secure systems. If it will be considered, as in the previous years, we would join an LNCS proceedings volume.

Cybersecurity is of the utmost importance for computing systems. The ethics guidelines for trustworthy artificial intelligence authored by the European Commission’s Independent High Level Expert Group on Artificial Intelligence on April 2019 have highlighted that machine learning-based artificial intelligence developments in various fields, including cybersecurity, are improving the quality of our lives every day, that AI systems should be resilient to attacks and security, and that they should consider security-by-design principles.

Due to the scale and complexity of current systems, it is a permanent and growing concern in industry and academia. On the one hand, the volume and diversity of functional and non-functional data, including open source information, along with increasingly dynamic operating environments, create additional obstacles to the security of systems and the privacy and security of data. On the other hand, it creates an information-rich environment that, leveraged by techniques in the crossing of modern machine learning, data science and visualisation fields, will contribute to improving systems and data security and privacy. This poses significant, industry-relevant challenges to the machine learning and cybersecurity communities, as the main problems arise in contexts of dynamic operating environments and unexpected operating conditions, motivating the demand for production-ready systems able to improve and adaptively maintain the security of computing systems as well as the security and privacy of data.

Based on the recent history, we plan to organise this workshop as a European forum for cybersecurity researchers and practitioners who wish to discuss the recent developments of machine learning for developing cybersecurity, by paying special attention to solutions rooted in adversarial learning, pattern mining, neural networks and deep learning, probabilistic inference, anomaly detection, stream learning and mining, and big data analytics.

The last decade has been a critical one regarding cybersecurity, with studies predicting that the worldwide cost of global cybercrime damage will hit 0.5 trillion dollars annually by 2025. Cyberthreats have increased dramatically, exposing sensitive personal and business information, disrupting critical operations and imposing high economic costs. The number, frequency, and sophistication of threats will only increase and will become more targeted in nature. Furthermore, today’s computing systems operate under increasing scales and dynamic environments, ingesting and generating more and more functional and non-functional data. Detecting, analysing, and defending against threats in (near) real-time conditions is impossible without employing machine learning techniques and big data infrastructure. This gives rise to cyber threat intelligence and analytic solutions, such as (informed) machine learning on big data and open-source intelligence, to perceive, reason, learn, and act against cyber adversary techniques and actions. Moreover, organisations’ security analysts must manage and protect these systems and deal with the privacy and security of all personal and institutional data under their control. This calls for tools and solutions combining the latest advances in data science, visualisation, and machine learning. We strongly believe that the significant advance of state-of-the-art machine learning over the last few years has not been fully exploited to harness the potential of available data for the benefit of systems, data security, and privacy. While machine learning algorithms have already proven beneficial for the cybersecurity industry, they have also highlighted several shortcomings that impact their reliability and safety. As proved multiple times in the literature, machine learning models are vulnerable to adversarial machine learning attacks, which cause target algorithms to misbehave, provide unethical answers to users’ prompts, or reveal sensitive information about their inner workings. As machine learning-based capabilities become incorporated more frequently into cyber assets, the urgency of understanding these threats' impact and mitigations rises exponentially. On the other hand, the community must not ignore that it is still challenging to mine, interpret and extract knowledge from security-related data harvested from multiple sources and encoded in different formats. Hence, the need for learning techniques that can provide accurate predictions while withstanding adversarial attacks is of paramount importance. On the other side, although the priority of machine learning methods today is to perform accurate detection strengthening their robustness to adversarial attacks, explainability of security systems has recently emerged as a very active research field. Explaining the effect of certain data features on security decisions can contribute to allowing security systems to benefit better from the trust of security stakeholders.

The workshop follows the success of the four previous editions( MLCS 2019, MLCS 2020, MLCS 2021, MLCS 2022, MLCS 2023, and MLCS 2024) co-located with ECML-PKDD. In all the previous editions, the workshop gained strong interest, with attendance of between 20 and 30 participants, lively post-presentation discussions, amazing invited talks in all the editions and a vibrant panel discussion in the 2019 and 2021 editions. We strive to provide researchers with a forum to exchange and discuss scientific contributions and open challenges, both theoretical and practical, related to using machine-learning approaches in cybersecurity. We want to foster joint work and knowledge exchange in the cybersecurity community by increasing the connection between researchers and practitioners from not only the machine learning area but also experts from the big data, data science, and visualisation areas. The workshop shall provide a forum for discussing novel trends and achievements in machine learning and their role in developing secure systems. We want to highlight the latest research trends in machine learning, privacy of data, big data, deep learning, incremental and stream learning, and adversarial machine learning. In particular, the goal of this workshop is to promote the application of these emerging machine learning techniques to cybersecurity by measuring how they improve the field with their predictive capabilities.

The workshop shall contribute to identifying new application areas as well as open and future research problems related to the application of machine learning in the cybersecurity field.

MLCS welcomes both research papers reporting results from mature work, recently published work, as well as more speculative papers describing new ideas or preliminary exploratory work. Papers reporting industry experiences and case studies will also be encouraged. However, it should be noticed that papers based on recently published work will not be considered for publication in the proceedings.

All topics related to the contribution of machine learning approaches to the security of organisations’ systems and data are welcome. These include, but are not limited to:

• Machine learning for:
• the security and dependability of networks, systems, and software
• open-source threat intelligence and cybersecurity situational awareness
• data security and privacy
• cybersecurity forensic analysis
• the development of smarter security control
• the fight against (cyber)crime, e.g., biometrics, audio/image/video analytics
• vulnerability analysis
• the analysis of distributed ledgers
• malware, anomaly, intrusion and spam detection

• Adversarial machine learning and the robustness of AI models against malicious actions
• Interpretability and Explainability of machine learning models in cybersecurity
• Privacy preserving machine learning
• Trusted machine learning
• Data-centric security
• Scalable / big data approaches for cybersecurity
• Deep learning for automated recognition of novel threats
• Graph representation learning in cybersecurity
• Continuous and one-shot learning
• Informed machine learning for cybersecurity
• User and entity behavior modeling and analysis