@inproceedings{brideau-duquette_forensic_2025,

title = {Forensic Psychiatry and Big Data: Towards a Cyberphysical System in Service of Clinic, Research and Cybersecurity},

author = {M. Brideau-Duquette and S. S. -P. Côté and T. Boukhalfi and P. Renaud},

editor = {Boulay B. and Di Mascio T. and Tovar E. and Meinel C.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105003621528&doi=10.5220%2f0013496100003932&partnerID=40&md5=ee442c9113f1104973b40ef698bc5d7d},

doi = {10.5220/0013496100003932},

isbn = {21845026 (ISSN); 978-989758746-7 (ISBN)},

year  = {2025},

date = {2025-01-01},

booktitle = {International Conference on Computer Supported Education, CSEDU - Proceedings},

volume = {1},

pages = {856–864},

publisher = {Science and Technology Publications, Lda},

abstract = {The advent of big data and artificial intelligence has led to the elaboration of computational psychiatry. In parallel, great progress has been made with extended reality (XR) technologies. In this article, we propose to build a forensic cyberphysical system (CPS) that, with a data lake as its computational and data repository core, will support clinical and research efforts in forensic psychiatry, this in both intramural and extramural settings. The proposed CPS requires offender's data (notably clinical, behavioural and physiological), but also emphasises the collection of such data in various XR contexts. The same data would be used to train machine and deep learning, artificial intelligence, algorithms. Beyond the direct feedback these algorithms could give to forensic specialists, they could help build forensic digital twins. They could also serve in the fine tuning of XR usage with offenders. This paper concludes with human-centered cybersecurity concerns and opportunities the same CPS would imply. The proximity between a forensic and XR-supported CPS and social engineering will be addressed, and special consideration will be given to the opportunity for situational awareness training with offenders. We conclude by sketching ethical and implementation challenges that would require future inquiring. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.},

note = {Journal Abbreviation: International Conference on Computer Supported Education, CSEDU - Proceedings},

keywords = {Artificial intelligence, Artificial life, Behavioral research, Computer forensics, Cyber security, Cyber-physical systems, Cyberphysical System, Cybersecurity, Data Lake, Data repositories, Extended Reality, Forensic engineering, Forensic psychiatry, Intelligent computing, prediction, Prevention, Research efforts, Social Engineering, Sociology, System in services},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{lapointe_field_2025,

title = {Field Trials of an AI-AR-Based System for Remote Bridge Inspection by Drone},

author = {J. -F. Lapointe and M. S. Allili and N. Hammouche},

editor = {Harris D. and Li W.-C. and Krömker H.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85213387549&doi=10.1007%2f978-3-031-76824-8_20&partnerID=40&md5=565ae5dded9cfdf27632e79e702c7718},

doi = {10.1007/978-3-031-76824-8_20},

isbn = {03029743 (ISSN); 978-303176823-1 (ISBN)},

year  = {2025},

date = {2025-01-01},

booktitle = {Lect. Notes Comput. Sci.},

volume = {15381 LNCS},

pages = {278–287},

publisher = {Springer Science and Business Media Deutschland GmbH},

abstract = {Bridge inspections are important to ensure the safety of users of these critical transportation infrastructures and avoid tragedies that could be caused by the collapse of these infrastructures. This paper describes the results of field trials of an advanced system for remotely guided inspection of bridges by a drone, which relies on artificial intelligence and augmented reality to achieve it. Results indicate that a high speed network link is critical to achieve good performance. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.},

note = {Journal Abbreviation: Lect. Notes Comput. Sci.},

keywords = {Advanced systems, Air navigation, Artificial intelligence, artificial intelligence (AI), augmented reality, augmented reality (AR), Bridge inspection, Concrete bridges, Drone, Drones, Field trial, HIgh speed networks, High-speed Networks, Network links, Performance, Remote guidance, Transportation infrastructures, UAV},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{nosrati_ai_2025,

title = {The AI Complacency Model: Integrating Bounded Rationality and Information Processing},

author = {S. Nosrati and H. Motaghi},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105025349267&partnerID=40&md5=14d676bedd3ea18921ed24a830889a8b},

isbn = {979-833132774-3 (ISBN)},

year  = {2025},

date = {2025-01-01},

booktitle = {Am. Conf. Inf. Syst., AMCIS},

volume = {7},

pages = {4590–4599},

publisher = {Association for Information Systems},

abstract = {This study addresses a critical gap in understanding why users exhibit reduced oversight when interacting with generative AI systems despite their known limitations. While existing research documents AI errors across domains, theoretical frameworks explaining the underlying psychological mechanisms remain underdeveloped. We propose a comprehensive model of AI complacency that extends beyond traditional automation complacency theories by integrating bounded rationality constraints with dual-process information processing models. Our framework demonstrates how Perceived AI Reliability triggers a shift from systematic to heuristic information processing, subsequently reducing vigilance and impairing task performance. The relationship between Perceived AI Reliability and information processing is moderated by three key factors derived from bounded rationality theory: knowledge limitations, cognitive processing capabilities, and time constraints. The proposed conceptual model of this study contributes to the literature by identifying unique psychological mechanisms underlying AI complacency, explaining the processing shifts in human-AI interaction, positioning vigilance as a critical mediating mechanism, and introducing the Vigilance-Reliability Matrix as a diagnostic tool for identifying different interaction patterns. This framework offers both theoretical insights and practical guidance for maintaining appropriate human oversight as AI systems become increasingly sophisticated and widespread across domains. Copyright © 2025 by Association for Information Systems (AIS). All rights reserved.},

keywords = {AI complacency, AI systems, Artificial intelligence, Behavioral research, Bounded rationality, Cognitive bias, cognitive biases, decision-making, Decisions makings, Heuristic processing, Human computer interaction, Human-AI interaction, information processing, Information systems, Information use, Perceived AI reliability, Reliability theory, Systematic processing, Vigilance},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{boudraLightweightIoCExtraction2025,

title = {Toward Lightweight IoC Extraction in IoT: The Role of Small Language Models},

author = {N. Boudra and A. Elhajjout and H. Moudoud and M. Oujaoura and Z. Jarir and Z. A. El Houda},

url = {https://www.scopus.com/pages/publications/105037622873?origin=resultslist},

doi = {10.1109/SmartAgriSuSY68475.2025.11466843},

isbn = {979-833157801-5 (ISBN)},

year  = {2025},

date = {2025-01-01},

booktitle = {Int. Congr. Smart Agric. Sustain. Syst., SmartAgri SuSY},

publisher = {Institute of Electrical and Electronics Engineers Inc.},

abstract = {The proliferation of cyber threats necessitates rapid and accurate extraction of Indicators of Compromise (IoCs) from diverse security data sources. While Large Language Models (LLMs) have demonstrated exceptional capabilities in natural language processing and information extraction tasks, their deployment on resource-constrained IoT devices remains challenging due to computational and memory requirements. This paper investigates whether Small Language Models (SLMs) can serve as effective alternatives to LLMs for IoC extraction in IoT environments with limited resources. We present a novel direct LLM-based IoC extraction system leveraging context memory mechanisms for document-wide semantic understanding, specifically designed for deployment on edge computing infrastructure with consumer-grade hardware. Our experimental setup utilizes an RTX 4080 GPU and Ryzen 7 7700X processor running the Ollama framework with GPT-OSS:20B model to evaluate the feasibility of using smaller models instead of resource-intensive LLMs for security tasks. Evaluated on 9 diverse threat intelligence reports spanning different malware families and attack campaigns, the system achieved an average F1 score of 0.62, precision of 0.54, recall of 0.79, and accuracy of 0.85, demonstrating that SLMs can achieve acceptable accuracy levels for IoC extraction while operating within the computational constraints typical of IoT edge deployments. The results suggest that smaller models may provide viable alternatives to large language models for distributed threat intelligence processing in resource-limited environments. © 2025 IEEE.},

note = {Journal Abbreviation: Int. Congr. Smart Agric. Sustain. Syst., SmartAgri SuSY},

keywords = {Artificial intelligence, Cyber threats, Data-source, Edge Computing, extraction, Indicator of compromize, Indicators of Compromise, Information retrieval, Information Security, IoT Security, Language model, Malware, Natural languages, Network security, Program processors, Resource Constraint, Resource Constraints, Semantics, Small language model, Small Language Models, Threat Intelligence},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{brideau-duquetteForensicPsychiatryBig2025a,

title = {Forensic Psychiatry and Big Data: Towards a Cyberphysical System in Service of Clinic, Research and Cybersecurity},

author = {M. Brideau-Duquette and S. S. -P. Côté and T. Boukhalfi and P. Renaud},

editor = {Boulay B. and Di Mascio T. and Tovar E. and Meinel C.},

url = {https://www.scopus.com/pages/publications/105003621528?origin=resultslist},

doi = {10.5220/0013496100003932},

isbn = {21845026 (ISSN); 978-989758746-7 (ISBN)},

year  = {2025},

date = {2025-01-01},

booktitle = {International Conference on Computer Supported Education, CSEDU - Proceedings},

volume = {1},

pages = {856–864},

publisher = {Science and Technology Publications, Lda},

abstract = {The advent of big data and artificial intelligence has led to the elaboration of computational psychiatry. In parallel, great progress has been made with extended reality (XR) technologies. In this article, we propose to build a forensic cyberphysical system (CPS) that, with a data lake as its computational and data repository core, will support clinical and research efforts in forensic psychiatry, this in both intramural and extramural settings. The proposed CPS requires offender's data (notably clinical, behavioural and physiological), but also emphasises the collection of such data in various XR contexts. The same data would be used to train machine and deep learning, artificial intelligence, algorithms. Beyond the direct feedback these algorithms could give to forensic specialists, they could help build forensic digital twins. They could also serve in the fine tuning of XR usage with offenders. This paper concludes with human-centered cybersecurity concerns and opportunities the same CPS would imply. The proximity between a forensic and XR-supported CPS and social engineering will be addressed, and special consideration will be given to the opportunity for situational awareness training with offenders. We conclude by sketching ethical and implementation challenges that would require future inquiring. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.},

note = {Journal Abbreviation: International Conference on Computer Supported Education, CSEDU - Proceedings},

keywords = {Artificial intelligence, Artificial life, Behavioral research, Computer forensics, Cyber security, Cyber-physical systems, Cyberphysical System, Cybersecurity, Data Lake, Data repositories, Extended Reality, Forensic engineering, Forensic psychiatry, Intelligent computing, prediction, Prevention, Research efforts, Social Engineering, Sociology, System in services},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{lapointeFieldTrialsAIARBased2025a,

title = {Field Trials of an AI-AR-Based System for Remote Bridge Inspection by Drone},

author = {J. -F. Lapointe and M. S. Allili and N. Hammouche},

editor = {Harris D. and Li W.-C. and Krömker H.},

url = {https://www.scopus.com/pages/publications/85213387549?origin=resultslist},

doi = {10.1007/978-3-031-76824-8_20},

isbn = {03029743 (ISSN); 978-303176823-1 (ISBN)},

year  = {2025},

date = {2025-01-01},

booktitle = {Lect. Notes Comput. Sci.},

volume = {15381 LNCS},

pages = {278–287},

publisher = {Springer Science and Business Media Deutschland GmbH},

abstract = {Bridge inspections are important to ensure the safety of users of these critical transportation infrastructures and avoid tragedies that could be caused by the collapse of these infrastructures. This paper describes the results of field trials of an advanced system for remotely guided inspection of bridges by a drone, which relies on artificial intelligence and augmented reality to achieve it. Results indicate that a high speed network link is critical to achieve good performance. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.},

note = {Journal Abbreviation: Lect. Notes Comput. Sci.},

keywords = {Advanced systems, Air navigation, Artificial intelligence, artificial intelligence (AI), augmented reality, augmented reality (AR), Bridge inspection, Concrete bridges, Drone, Drones, Field trial, HIgh speed networks, High-speed Networks, Network links, Performance, Remote guidance, Transportation infrastructures, UAV},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{temmarPhoneticAnalysisReal2025,

title = {Phonetic Analysis of Real and Synthetic Speech Using HuBERT Embeddings: Perspectives for Deepfake Detection},

author = {D. E. Temmar and A. Hamadene and V. Nallaguntla and A. Fursule and M. S. Allili and S. Kshirsagar and A. R. Avila},

url = {https://www.scopus.com/pages/publications/105033145913?origin=resultslist},

doi = {10.1109/SMC58881.2025.11343334},

isbn = {1062922X (ISSN); 979-833153358-8 (ISBN)},

year  = {2025},

date = {2025-01-01},

booktitle = {Conf. Proc. IEEE Int. Conf. Syst. Man Cybern.},

pages = {86–91},

publisher = {Institute of Electrical and Electronics Engineers Inc.},

abstract = {The growing sophistication of speech generated by Artificial Intelligence (AI) has introduced new challenges in audio deepfake detection. Text-to-speech (TTS) and voice conversion (VC) technologies can now produce convincing synthetic speech with high quality and intelligibility. This poses a serious threat to voice biometric security systems, such as automatic speaker recognition. It also increases the risks associated to the spread of spoken disinformation, where synthetic voices can be used to disseminate malicious content. In this study, we conduct an analysis of real and synthetic speech at phonetic and word levels. For that, a parallel dataset comprising real and synthetic speech signals were developed based on a subset of the LibriSpeech ASR corpus. Synthetic speech samples were generated using two TTS and one VC systems: Coqui TTS, VITS TTS, and StarGANv2 VC. We adopted HuBERT, a self-supervised speech model, to extract speech embeddings. The motivation for using this model stems from its ability to recognize sound units corresponding to the so-called pseudo phonemes. Our analysis is based on the KL divergence (KLD) between the distributions of synthetic and real phonemes, which allowed us to rank synthetic phonemes based on their alignment with their real counterpart. We also trained several classifiers per phoneme to distinguish between real and synthetic samples. We then compute the correlations between KLD and accuracies per phoneme. Besides showing a list of phonemes that are more discriminative, our findings suggest that vowels correlate better with the classifiers' performance, suggesting that the KLD can be an indicator of the most distinguishable phonemes for deepfake detection. © 2025 IEEE.},

note = {Journal Abbreviation: Conf. Proc. IEEE Int. Conf. Syst. Man Cybern.},

keywords = {Artificial intelligence, Audio acoustics, Audio DeepFake Detection, Audio signal processing, Embeddings, Hu-BERT, KL-divergence, Linguistics, Phoneme and word Embedding, Phonetic analysis, Security systems, Self-Supervised Speech Representation, Speech analysis, Speech communication, Speech processing, Speech synthesis, Synthetic speech, Text to speech, Voice conversion},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{moudoudSDNbasedAdaptiveEnsemble2025,

title = {An SDN-based Adaptive Ensemble Learning Framework for Intrusion Mitigation in Wireless Networks},

author = {H. Moudoud and Z. A. El Houda and L. Khoukhi and H. T. Mouftah},

editor = {Valenti M. and Reed D. and Torres M.},

url = {https://www.scopus.com/pages/publications/105018460686?origin=resultslist},

doi = {10.1109/ICC52391.2025.11161745},

isbn = {15503607 (ISSN); 979-833150521-9 (ISBN)},

year  = {2025},

date = {2025-01-01},

booktitle = {IEEE Int Conf Commun},

pages = {554–559},

publisher = {Institute of Electrical and Electronics Engineers Inc.},

abstract = {Jamming attacks are among the most critical security threats to Wireless Sensor Networks (WSNs), as they can severely disrupt normal network operations, leading to data loss, network downtime, and reduced system performance. Intrusion Detection Systems (IDSs) have therefore become essential to protect WSNs. However, conventional IDSs often struggle to detect zero-day attacks, creating a significant security gap. To address this, Artificial Intelligence (AI)-based IDSs have been introduced, offering improved detection capabilities but frequently encountering high bias or variance issues, which reduce their reliability. Recently, ensemble learning (EL) has emerged as a promising approach to build more adaptable and data-resilient models by combining multiple learning algorithms. In this context, we propose AdaptiveBoost, an SDN-based Adaptive Ensemble Learning Framework, specifically designed for effective jamming attack detection in WSNs. The SDN integration allows AdaptiveBoost to optimize network traffic flow, identify anomalies in real-time, and adaptively fine-tune detection mechanisms based on current network conditions. We conduct several experiments to evaluate AdaptiveBoost using real-world WSN attacks; using the well-known public network security dataset, WSN-DS, show that AdaptiveBoost outperforms AI-based algorithms in terms of accuracy, precision, recall, and F1 score, while achieving a remarkable reduction in training time by a factor of 235, making it an efficient, scalable solution for securing WSNs against jamming attacks. © 2025 IEEE.},

note = {Journal Abbreviation: IEEE Int Conf Commun},

keywords = {Aerial vehicle, Antennas, Artificial intelligence, Computer crime, Ensemble learning, Intrusion Detection, Intrusion Detection Systems, Jamming, Jamming Attacks, Learning algorithms, Learning frameworks, Network intrusion, Network operations, Radio communication, Security systems, Security threats, Sensors network, Unmanned aerial vehicle, Unmanned Aerial Vehicles, Unmanned aerial vehicles (UAV), Wireless networks, Wireless sensor, Wireless Sensor Networks, Zero-day attack},

pubstate = {published},

tppubtype = {inproceedings}

}