Date: 30-10-2025
October 30-31, 2025
AISSLE-2025
International Workshop on Applied Intelligent Security Systems in Law Enforcement
Kharkiv National University of Internal Affairs
Kharkiv, Ukraine
GENERAL INFORMATION
International Workshop on Applied Intelligent Security Systems in Law Enforcement (AISSLE-2025) will be held in Vinnytsia, Ukraine,
from October 30–31, 2025. The Workshop is organized by Kharkiv National University of Internal Affairs
Kharkiv National University of Internal Affairs is a leading higher education institution in Ukraine with specific training conditions, which trains specialists in information and analytical support and cybersecurity of the National Police of Ukraine. For 10 years, since 2015, the annual International Scientific and Practical Conference “Security in the Cyberspace” has been held on its basis, in which more than 100 scientists, researchers, teachers, and graduate students from countries around the world participate annually. In 2025, this conference expands its activities at the international level by highlighting a workshop that presents leading research on the development and improvement of intelligent security systems for the cyber police and law enforcement agencies’ needs.
International Workshop on Applied Intelligent Security Systems in Law Enforcement (AISSLE-2025) aims to bring together specialists in the field of intelligent security systems for the cyber police and law enforcement agency’s needs, as well as discuss current methods for detecting and preventing cyber threats in real time. The event objectives include exchanging practical experience in the artificial intelligence and machine learning algorithms and Big Data technologies used for monitoring network traffic, anomalies in user behavior, and biometric authentication, as well as analyzing and optimizing the hardware and software systems operation through applied modeling and digital twins. Particular attention will be paid to interpretability methods (SHAP, residual analysis), adversarial stability, and optimization of neural networks for embedded devices. The discussion will cover a wide range of topics, from data mining and ontologies to generative AI and decision support systems, which will allow for a comprehensive understanding of the modern challenges and solutions in the field of intelligent security for law enforcement agencies.
Participation and publication are free of charge!
AISSLE-2025 workshop take the presentation form by invited keynote speakers plus presentations of peer-reviewed individual papers.
WORKSHOP TOPICS
Topics of interest include, but are not limited to:
1. Applied Intelligent Security Systems. Applied intelligent security systems use artificial intelligence, machine learning, and big data processing to automate and improve the protection of information, physical infrastructure, and critical facilities efficiency. This area includes recognizing anomalies and threats in network traffic, biometric authentication, intelligent video monitoring, and risk prediction based on user and device behavior analysis. In a corporate environment, such systems help identify and neutralize cyberattacks, data leaks, and internal incidents in real time, and in industry, they ensure the safety of technological processes and prevent emergency situations.
2. Artificial Intelligence and Machine Learning. Artificial intelligence (AI) and machine learning (ML) involve the algorithms and statistical methods used to analyze data, identify patterns, and make decisions without explicitly programming all the steps. This field covers technologies such as neural networks, decision trees, support vector machines, and deep learning, applicable to processing images, speech, text, and structured data. In law enforcement, they help improve the investigations efficiency, automate facial recognition and behavioral anomalies, and predict and prevent crime.
3. Applied Modeling in Security Systems. Applied modeling in security systems involves the mathematical, statistical, and computational models used for designing, analyzing, and optimizing the software operation and hardware-software systems. This area covers the digital twins construction, simulating data processing processes, simulating user scenarios, and assessing system performance under various loads.
4. Big Data and Data Science. Big Data is a technologies and methods set for processing and analyzing extremely large, diverse, and rapidly changing data sets to identify patterns and gain valuable insights. Data Science combines statistics, machine learning, and programming to extract knowledge from data and make informed decisions based on it.
5. Intelligent Information Systems, Data Mining and Ontology. Intelligent information systems are software and hardware systems capable of adaptively processing and analyzing data based on built-in artificial intelligence algorithms, automatically making decisions in complex dynamic environments. Data Mining (intellectual data analysis) uses statistical and machine learning methods to identify hidden patterns and patterns in information large amount, and ontologies provide a formal description of the subject area through concepts set and relations between them, serving as a foundation for semantic interpretation and integration of heterogeneous data.
6. Computational Intelligence for Data Acquisition Systems. Computational Intelligence for Data Acquisition Systems is a field that studies the computational intelligence methods (neural networks, evolutionary algorithms, fuzzy logic) application to automate the collection, filtering and pre-processing of signals from sensors in real time. Using these approaches allows adaptive noise suppression, distortion compensation and optimization of the computing resources use, increasing the accuracy and reliability of data acquisition systems under dynamic operating conditions.
7. Design and Testing of Advanced Computer Systems. Design and Testing of Advanced Computer Systems is a field dedicated to the architectural and software solutions development for high-performance computing platforms, including multiprocessor and distributed systems, followed by their functional correctness, performance, and reliability comprehensive testing. These studies use modeling methods, load testing, and automated verification analysis to ensure that systems meet specified requirements and identify bottlenecks.
8. Autoencoders and Generative Models. Autoencoders are neural networks that learn to compress input data into a compact latent representation (code) and reconstruct it back, which allows for anomaly detection and dimensionality reduction. Generative AI Models are algorithms that can synthesize new data (text, images, audio, etc.) by learning from real-world examples large amount and producing high-quality, semantically coherent results. Key technologies include transformers (GPT, BERT), variational autoencoders (VAE), and generative adversarial networks (GAN), which provide ample opportunities for creativity and automation of content generation.
9. Intelligent Software Systems and Tools. Intelligent software systems and tools combine artificial intelligence methods (machine learning, heuristic algorithms, ontologies) to automate and optimize the software development life cycle: from code generation and refactoring to quality analysis and testing. Such solutions are able to adaptively adjust to changing requirements, provide predictive error detection and improve the development team’s efficiency.
10. Text Mining. Text Mining applies natural language processing techniques and statistical algorithms to extract semantic patterns, key entities, and hidden knowledge from unstructured text sources.
11. Social Media Analytics. Social Media Analytics are methods set for collecting, processing and analyzing data from social networks (posts, comments, likes, reposts) in order to identify audience sentiment, key topics and influencers. NLP, network analysis and machine learning technologies are used to monitor reputation, detect trends and predict user behavior.
12. Multi-criteria Decision Analysis and Decision Support Systems. Multi-criteria decision analysis (MCDM) involves methods for constructing and comparing alternatives based on multiple, often conflicting, criteria to select the best option. Decision Support Systems integrate MCDM, databases, and analytical models, providing users with interactive tools to make informed choices and predict the decisions consequences.
13. Law enforcement intelligent systems. Law enforcement intelligent systems are software and hardware systems that use big data analysis, machine learning, and computer vision to automate investigations, recognize faces, and detect criminal patterns. Such solutions improve the police work efficiency through predictive policing, operational analysis of evidence, and real-time decision support.
14. Internet of Things. Internet of Things is a concept of connecting physical devices and sensors into a single network via the Internet, which allows collecting, transmitting and analyzing data in real time. The smart objects interaction provides processes automation, remote monitoring and control of various systems (from smart homes to industrial enterprises).
15. Risk-based analysis models in cybersecurity. Risk-based analysis models in cybersecurity assess the likelihood and potential damage of various threats by matching system vulnerabilities with the assets importance and their exploitation likelihood. This allows for the protection measures prioritization and resources allocation based on risk calculations, ensuring a balance between costs and security levels.
IMPORTANT DATES
Paper submission deadline:
before September 15, 2025 before October 05, 2025
Notification of acceptance: on October 17, 2025
Camera-ready submission deadline: before October 24, 2025
Workshop period: October 30–31, 2025
KEYNOTE SPEAKERS
Yevgeniy Bodyanskiy, D.Sc., Professor, Kharkiv National University of Radio Electronics. Title: "Multidimensional cascade bagging metamodel and its online learning"
Oksana Mulesa, D.Sc., Professor, University of Presov in Presov. Title: "Conflict-Aware Collaborative Decision Support for Critical Infrastructure"
Olga Cherednichenko, D.Sc., Professor, Bratislava University of Economics and Management. Title: "A Data-Centric Approach to Crowd Counting: Synthetic Dataset Creation and Evaluation"
INTERNATIONAL PROGRAM COMMITTE
Chair:
Valerii Sokurenko, Kharkiv National University of Internal Affairs, Ukraine
Co-Chairs:
Oleksandr Muzychuk, Kharkiv National University of Internal Affairs, Ukraine
Serhii Vladov, Kharkiv National University of Internal Affairs, Ukraine
Yevgeniy Bodyanskiy, Kharkiv National University of Radio Electronics, Ukraine
Igor Aizenberg, Manhattan University, USA
Vasyl Lytvyn, Lviv Polytechnic National University, Ukraine
Lyubomyr Chyrun, Ivan Franko National University of Lviv, Ukraine
Members of the international program committee:
WORKSHOP ORGANIZING COMMITTEE
Head:
Serhii Vladov, Kharkiv National University of Internal Affairs, Ukraine
Deputies:
Serhii Ablamskyi, Kharkiv National University of Internal Affairs, Ukraine
Liudmyla Nadopta, Kharkiv National University of Internal Affairs, Ukraine
Vitalii Naida, Kharkiv National University of Internal Affairs, Ukraine
Symon Serbenyuk, Kharkiv National University of Internal Affairs, Ukraine
SUBMISSION
Authors are invited to submit a regular paper (10 or more full pages) in English. One author (co-author) might submit a maximum of two papers. Short paper (5–9 full pages) are not accepted!
AISSLE-2025 uses CEUR-ART style (1 column) for the proceedings. Your submission should be prepared using CEUR-WS’s LibreOffice template .
Note that LibreOffice has its own “Save PDF” function, which produces good PDF files.
LibreOffice users need to make sure that the Libertinus font family is installed on the local computer. Instructions are in the ODT template.
Do not use cloud text editors such as Office-365 or GoogleDocs. They do not support Libertinus. And do not use Word/Microsoft365 since it tends to produce incompatible PDFs.
LibreOffice is free software and available on most platforms.
All papers for participation in the workshop and peer-reviewing (in PDF format) should be submitted to e-mail: [email protected] .
We solicit submission of the original paper, not previously published or submitted for publication elsewhere. Participation in the workshop is free of charge.
Author Agreement to Publish a Contribution as Open-Access on CEUR-WS.org (just filled in by hand and good quality scanned) is necessary.
To reviewer feedbacks for preparing a camera-ready paper you MUST take into account the following acceptance criteria for the CEUR-WS publications:
Paper Structure should generally include: Abstract; Keywords; Introduction; Related Works; Proposed methodology/model/technique; Results/Discussions; Conclusion; References.
The references list MUST NOT be very short or long without the need, the authors ARE OBLIGED to use the results of international research. Authors SHOULD NOT use references only to a narrow circle of authors from their educational institution or several institutions. Usually self-citation MUST NOT exceed 20%. References should be, mostly, modern, NOT OLDER than 5–8 years. One cascade (group) citation should consist of not more than 3 sources.
English MUST BE proofread.
The paper MUST follow the formatting rules. Please, pay your attention especially on font’s size and style used for paper title and section/subsection headers, figure and table captions. Please, double-check formatting style of the references list.
All references should be presented in English and if it is strongly necessary – with notation (in Ukrainian), etc.
The quality of all figures should be very high for good reproduction during CEUR publication. All illustrative figures should be provided by caption signatures. All abbreviations should be preliminary explained. The size of the letters and numbers in the formulas and in the formulas’ fragments in the text should be the same.
The title of paper should be not very long (until 13 words including THE, IN, FOR, etc.) and should be exactly corresponded to the research results which are discussed in the paper. Please, avoid using same words in the title of paper twice.
IMPORTANT
CEUR-WS supports the publication of computer-science workshops. Note that CEUR-WS demand that each paper has at least one author with at least 5 papers listed in DBLP ( DBLP.org ). Authors should not include ArXiv and other non peer-reviewed papers in the number of DBLP papers.
If this condition is not met, or we cannot verify it, the corresponding paper will not be submitted for peer-review and will be rejected by the International Program Committee.
PEER-REVIEWING
Submitted papers will be peer-reviewed by two scholars on the basis of technical quality, relevance, originality, significance, and clarity. If necessary, a third, additional reviewer will be involved. The International Program Committee will use these reviews to determine, which papers will be accepted for presentation at the workshop. The result of the reviewing will be announced to the submitting authors by email, along with reviewer comments, if any.
Authors of papers accepted for publication must take into account all the reviewers comments in their papers and upload the final versions to e-mail [email protected] in .pdf.
We require that authors of all accepted for publication papers (based on the review results) additionally send their papers to the workshop e-mail [email protected] in .ODT format (for LibreOffice) or LaTeX so that we can make corrections if necessary in accordance with CEUR-WS requests.
The accepted papers must be presented at the workshop at least by one of their authors. Proceedings shall be submitted to CEUR-WS.org for online publication.
CONTACT
E-mail: [email protected] (Serhii Vladov, doctor of technical sciences, leading researcher)
Telephone: +380987383162, WhatsApp (Serhii Vladov, doctor of technical sciences, leading researcher)
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