Machine Learning for Cybersecurity (MLC)

Keynote Speakers

Title: TBD
Qiang Hu, Tianjin University
Abstract: TBD Dr. He is an Associate Professor at Tianjin University. Previously, he was a Postdoctoral Researcher at the University of Tokyo, working with Prof. Lei Ma. He received his Ph.D. degree from the University of Luxembourg, advised by Prof. Yves Le Traon, Prof. Mike Papadakis, and Prof. Maxime Cordy. Before that, he received his Master’s degree from Kyushu University, advised by Prof. Jianjun Zhao.
Title: TBD
Yaowen Zheng, Chinese Academy of Sciences
Abstract: TBD bio
Title: TBD
Imtiaz Karim, The University of Texas at Dallas
Abstract: . Dr. Imtiaz is an Assistant Professor in the Department of Computer Science at the University of Texas at Dallas. Previously, he was a Postdoctoral Researcher in the Department of Computer Science at Purdue University. He completed his Ph.D. from the same department in Spring 2023. He leads the System and Network Security (SysNetS) lab at UTD. His research lies in the general area of systems and network security. More specifically, his focus is on ensuring the security and privacy of wireless communication protocols (e.g., cellular networks—4G/5G, Bluetooth, VoWiFi, vehicular, WiFi, and IoT) with respect to their design and implementation. His research aims to develop tools that systematically analyze real-world systems and widely used protocols using AI (ML and NLP), formal verification, program analysis, and software testing techniques. Furthermore, with the advent of the next generation of networks (6G and beyond), his future goal is to ensure the resilience (reliability, adaptability, and security) of future network generations and to develop protocols and systems that are robust and secure by design.

Call For Paper

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:

Security and privacy of Data Mining
Data mining and AI applications for cybersecurity
Data mining approaches to enhance security and resiliency of cyber systems
Human behavior models with application to cybersecurity
Data mining for cybersecurity software verification and validation
AI tools and techniques to enhance resilience in cybersecurity
AI-enabled automation of cybersecurity tasks
Data-driven cybersecurity Analytics
Uncertainty-based decision making in cybersecurity
Modeling and simulation of cyber systems and system components

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:

Adversarial machine learning
Cryptography
Large language models
Interpretable deep learning
Multi-view deep learning paradigms
Real-time analytics and deep learning on stream data
Deep Transfer learning
Deep reinforcement learning
Bayesian deep learning
Graph convolutional networks and graph attention networks

Application areas of interest include, but are not limited to:

Malware evasion and detection
Cyber threat detection and modeling
Intrusion detection
Internet of Things (IoT) analysis
AI-enabled security information sharing and automation across multiple data sources
Smart and large-scale vulnerability assessment
Intelligent security systems with human-in-the-loop
Vulnerability detection
Anomaly detection
Dark Web Analytics for CTI applications
IP reputation analysis

Important Dates

Paper Submission Deadline: September 5, 2025
Acceptance Notification: September 18, 2025
Camera-ready Submission: September 25, 2025
Workshop Date: November 14, 2025 (Massachusetts Room)

Paper Submission

By the ICDM tradition, All accepted workshop papers will be published in the ICDMW proceedings published by the IEEE Computer Society Press, and will be accessible in the IEEE Computer Society Digital Library (CSDL) and the IEEE Xplore, and indexed by EI.
Submission Format: Paper submissions should be limited to max 8 pages plus 2 extra pages (for references, appendix, etc.) and follow the IEEE ICDM format. All submissions will be triple-blind reviewed by the Program Committee based on technical quality, relevance to scope of the conference, originality, significance, and clarity. The following sections give further information for authors. Please refer to the ICDM 2024 call for papers .
Submission website: Please submit your papers via the Submission Website
Presentation Format: Physical attendance is NOT mandatory this year due to potential visa issues. Remote presentations through Zoom are allowed. Meeting coordinates will be announced later to authors of accepted papers.
Registration is mandatory for accepted papers.

Organizers

Steering Chairs


Ali Babar University of Adelaide	Battista Biggio University of Cagliari	Elisa Bertino Purdue University

Hsinchun Chen University of Arizona	Yang Liu Nanyang Technological University	Xinming (Simon) Ou University of South Florida