The last decade has been a critical one regarding cybersecurity, with studies estimating the cost of cybercrime to be up to 1 percent of the global GDP in 2020. The capability to detect, analyze, and defend against threats in (near) real-time conditions is not possible 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. The aim of this workshop is to provide researchers with a forum to exchange and discuss scientific contributions, open challenges and recent achievements in machine learning and their role in the development of secure systems.

The last decade has been a critical one regarding cybersecurity, with studies estimating the cost of cybercrime to be up to 1 percent of the global GDP in 2020. The capability to detect, analyze, and defend against threats in (near) real-time conditions is not possible 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. The aim of this workshop is to provide researchers with a forum to exchange and discuss scientific contributions, open challenges and recent achievements in machine learning and their role in the development of secure systems.

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, cybersecurity 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 dynamical operating environments, create additional obstacles to the security of systems and to 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 visualization fields, will contribute to improve 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 organize this workshop as a European forum for cybersecurity researchers and practitioners that 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.

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, cybersecurity 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 dynamical operating environments, create additional obstacles to the security of systems and to 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 visualization fields, will contribute to improve 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 organize this workshop as a European forum for cybersecurity researchers and practitioners that 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 estimating the cost of cybercrime to be up to 1 percent of the global GDP in 2020. Cyberthreats have increased dramatically, exposing sensitive personal and business information, disrupting critical operations and imposing high costs on the economy. 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. The capability to detect, analyze, and defend against threats in (near) real-time conditions is not possible 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 have to 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 areas such as data science, visualization, and machine learning. We strongly believe that the significant advance of the state-of-the-art in machine learning over the last years has not been fully exploited to harness the potential of available data, for the benefit of systems-and-data security and privacy. In fact, while machine learning algorithms have been already proven beneficial for the cybersecurity industry, they have also highlighted a number of shortcomings. Traditional machine algorithms are often vulnerable to attacks, known as adversarial learning attacks, which can cause the algorithms to misbehave or reveal information about their inner workings. As machine learning-based capabilities become incorporated into cyber assets, the need to understand adversarial learning and address it becomes clear. On the other hand, when a significant amount of data is collected from or generated by different security monitoring solutions, big-data analytical techniques are necessary to mine, interpret and extract knowledge of these big data.

The last decade has been a critical one regarding cybersecurity, with studies estimating the cost of cybercrime to be up to 1 percent of the global GDP in 2020. Cyberthreats have increased dramatically, exposing sensitive personal and business information, disrupting critical operations and imposing high costs on the economy. 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. The capability to detect, analyze, and defend against threats in (near) real-time conditions is not possible 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 have to 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 areas such as data science, visualization, and machine learning. We strongly believe that the significant advance of the state-of-the-art in machine learning over the last years has not been fully exploited to harness the potential of available data, for the benefit of systems-and-data security and privacy. In fact, while machine learning algorithms have been already proven beneficial for the cybersecurity industry, they have also highlighted a number of shortcomings. Traditional machine algorithms are often vulnerable to attacks, known as adversarial learning attacks, which can cause the algorithms to misbehave or reveal information about their inner workings. As machine learning-based capabilities become incorporated into cyber assets, the need to understand adversarial learning and address it becomes clear. On the other hand, when a significant amount of data is collected from or generated by different security monitoring solutions, big-data analytical techniques are necessary to mine, interpret and extract knowledge of these big data.

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The workshop follows the success of the two previous editions (MLCS 2019 and MLCS 2020) co-located with ECML-PKDD 2019 and ECML-PKDD 2020 - in both editions the workshop gained strong interest, with an attendance between 30 and 40 participants, lively discussions after the talks, and a vibrant panel discussion in the 2019 edition. It aims at providing researchers with a forum to exchange and discuss scientific contributions and open challenges, both theoretical and practical, related to the use of machine-learning approaches in cybersecurity. We want to foster joint work and knowledge exchange between the cybersecurity community, and researchers and practitioners from the machine learning area, and its crossing with big data, data science, and visualization. The workshop shall provide a forum for discussing novel trends and achievements in machine learning and their role in the development of secure systems. It aims to highlight the latest research trends in machine learning, privacy of data, big data, deep learning, incremental and stream learning, and adversarial learning. In particular, it aims to promote the application of these emerging techniques to cybersecurity and measure the success of these less-traditional algorithms.

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

The workshop follows the success of the two previous editions (MLCS 2019 and MLCS 2020) co-located with ECML-PKDD 2019 and ECML-PKDD 2020 - in both editions the workshop gained strong interest, with an attendance between 30 and 40 participants, lively discussions after the talks, and a vibrant panel discussion in the 2019 edition. It aims at providing researchers with a forum to exchange and discuss scientific contributions and open challenges, both theoretical and practical, related to the use of machine-learning approaches in cybersecurity. We want to foster joint work and knowledge exchange between the cybersecurity community, and researchers and practitioners from the machine learning area, and its crossing with big data, data science, and visualization. The workshop shall provide a forum for discussing novel trends and achievements in machine learning and their role in the development of secure systems. It aims to highlight the latest research trends in machine learning, privacy of data, big data, deep learning, incremental and stream learning, and adversarial learning. In particular, it aims to promote the application of these emerging techniques to cybersecurity and measure the success of these less-traditional algorithms.

The workshop shall contribute to identify 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.

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:

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:

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• 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, and intrusion 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

• 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, and intrusion 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