Machine Learning for Cybersecurity (MLC)

Keynote Speakers

Trustworthy and Privacy-Preserving Machine Learning Inference Services
Thang Hoang, Virginia Tech
Abstract: TBD Dr. Thang Hoang is an Assistant Professor in the Department of Computer Science at Virginia Tech and a CCI Researcher. Prior to joining Virginia Tech, Thang was a Postdoctoral Fellow at Carnegie Mellon University (CMU) hosted by Prof. Elaine Shi and a Research Associate at the University of South Florida (USF) hosted by Prof. Attila A. Yavuz. He received a PhD degree from USF in August 2020. Thang's research spans the domains of cybersecurity and applied cryptography, with interests in privacy, secure and verifiable computation, zero-knowledge proofs, fuzzy cryptography, and trustworthy machine learning..
Towards Robustness Analysis of AIGC Systems
Kailong Wang, Huazhong University of Science and Technology
Abstract: The rapid advancement of AI technologies in user-oriented software systems has introduced novel challenges in ensuring system robustness. In this talk, I will first introduce Drowzee, our innovative approach that combines logic programming and metamorphic testing to detect fact-conflicting hallucinations in LLMs. Drowzee constructs factual knowledge bases, represents facts as logical predicates, and applies reasoning rules to generate logically sound question-answer pairs for testing LLMs, using semantic-aware metamorphic oracles to identify potential hallucinations. Next, I will discuss our research on "glitch tokens", anomalous tokens produced by tokenizers that can compromise LLM response quality. We categorized glitch tokens, observed LLM symptoms when interacting with them, and developed GlitchHunter, an iterative clustering-based detection technique that outperformed three baselines on eight open-source LLMs, offering insights into mitigating tokenization-related errors. Dr. Kailong Wang is currently an associate professor (with tenure) in the School of CSE at Huazhong University of Science and Technology (HUST). He is broadly interested in AI+Security, Secure and Private Software Engineering. He has published in various top-tier conferences and journals such as OOPSLA, NDSS, MobiCom, TSE, TOSEM, FSE, ASE, ISSTA and WWW.
Jieshan Chen, CSIRO's Data61
Abstract: User interfaces (UI) are vital in shaping user interactions with software, but they can also introduce significant cybersecurity risks. Deceptive design patterns, or "dark patterns," and phishing tactics exploit UI to manipulate users into actions that compromise their data security. This talk explores our initial attempts to detect these deceptive practices in mobile apps, using machine learning to analyse design patterns that facilitate scamming, phishing, and privacy violations. We will also discuss future directions for improving the detection and prevention of these threats in apps. Dr. Jieshan Chen is currently a research scientist and UI intelligence team lead at CSIRO’s Data61, Australia. She received her Ph.D. degree in computer science at Australian National University. She works at the interaction of software engineering and human computer interaction, using human-centred technique to examine and ensure responsible software development by design. She is currently working on dark pattern detection, data visualisation, UI design search and generation, and mobile application accessibility enhancement. She has published in various top-tier conferences and journals such as ICSE, ASE, FSE, UIST, CHI, TOSEM and USENIX Security.

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

Workshop papers submission: ~~September 10~~, September 22, 2024
Notification of workshop papers acceptance to authors: ~~October 7~~ October 15, 2024
Camera-ready deadline and copyright form: ~~October 11~~ October 20, 2024
Workshops date: December 9, 2024

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