In the past decades, cybersecurity threats have been among the most significant challenges for social development resulting in financial loss, violation of privacy, damages to infrastructures, etc. Organizations, governments, and cyber practitioners tend to leverage state-of-the-art Artificial Intelligence technologies to analyze, prevent, and protect their data and services against cyber threats and attacks. Due to the complexity and heterogeneity of security systems, cybersecurity researchers and practitioners have shown increasing interest in applying data mining methods to mitigate cyber risks in many security areas, such as malware detection and essential player identification in an underground forum. To protect the cyber world, we need more effective and efficient algorithms and tools capable of automatically and intelligently analyzing and classifying the massive amount of data in cybersecurity complex scenarios. This workshop will focus on empirical findings, methodological papers, and theoretical and conceptual insights related to data mining in the field of cybersecurity.
The workshop aims to bring together researchers from cybersecurity, data mining, and machine learning domains. We encourage a lively exchange of ideas and perceptions through the workshop, focused on cybersecurity and data mining. Topics of interest include, but are not limited to:
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Data mining and AI applications for cybersecurity
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Data-driven cybersecurity innovation
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Modelling and simulation of cyber systems and system components
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Data mining approaches to make cyber systems secure and resilient
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Human behaviour models with application to cybersecurity
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AI tools and techniques, mental resilience, and cybersecurity
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Data mining for cybersecurity software verification and validation
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Automation of heterogeneous security tools
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Decision making with uncertainty in cyber systems
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Security and privacy
We are interested in the new applications of data mining and AI for cybersecurity. Submitted papers will be evaluated based on criteria such as technical originality, creativity, and applicability. Methodological topics of interest include, but are not limited to:
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Graph convolution networks and graph attention networks
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Interpretable deep learning
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Real-time and/or streaming deep learning
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Multi-view deep learning paradigms
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Deep adversarial learning (e.g., generative adversarial networks)
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Deep Transfer learning
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Deep Bayesian learning
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Deep reinforcement learning
Application areas of interest include, but are not limited to:
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Malware evasion and detection
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IP reputation services
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Event correlation and anomaly detection
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Internet of Things (IoT) analysis (e.g., fingerprinting, network telescopes, etc.)
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Threat modelling (e.g., mapping exploits to MITRE ATT&CK)
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Security data fusion (e.g., event correlation) across multiple data sources
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Cybersecurity information sharing and automation
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Smart and large-scale vulnerability assessment and management systems
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Security Intelligence Augmentation (e.g., human-in-the-loop systems)
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Dark Web Analytics for CTI applications
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Vulnerability detection
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Intrusion detection
All dates are 11:59PM Pacific Daylight Time (PDT).
Submissions due: September 2, 2022
Notifications of Acceptance: September 23, 2022
Camera-ready paper due: October 1, 2022
Workshop day: December 1, 2022
Paper Submission
All accepted workshop papers will be published in a formal IEEE proceedings, in the IEEE Computer Society Digital Library (CSDL) and the IEEE Xplore, and indexed by EI. Paper submissions should be limited to max 8 pages plus 2 extra pages (for references, appendix, etc.) and follow the IEEE ICDM format. More detailed information is available in the IEEE ICDM 2022 Submission Guidelines . Please submit your papers via the submission link.
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原文始发于微信公众号（NTU网络安全实验室）：Call For Papers - ICDM MLC 2022
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