Effective energy management in households is critical to achieving overall energy efficiency and sustainability goals. This study introduces a novel approach to predicting short-term energy consumption for households using federated learning (FL) models. The approach achieves short-term energy consumption predictions (i.e. for the next 10 minutes) by analyzing local data, such as the current watt consumption and activated devices. The key innovation in this approach is the use of privacy-preserving machine learning techniques, ensuring that personal data is never shared during the training process. The models are used to predict potential spikes in energy demand, allowing for proactive management. In addition, a local Large Language Model (LLM) is integrated to generate personalized recommendations for users, aimed at avoiding predicted consumption spikes and promoting energy-efficient behavior. This approach not only preserves user privacy but also enhances user engagement by providing actionable insights based on local consumption patterns.

With the ongoing integration of machine learning models into critical infrastructure, the resilience of these systems against adversarial attacks is important for all domains. This paper introduces an adversarial attack generator framework against a network dataset that is part of OCPP Dataset using CI-CFlowMeter parser. We conduct a comprehensive evaluation of various prominent adversarial attacks, including FGSMA, JSMA, PGD, C&W, and more to assess their efficacy on the OCCP dataset. The Adversarial Generator is meticulously evaluated, demonstrating a significant impact in the models performance to detect potential perturbations. The results showcased the impact of the different type of adversarial attacks, contributing to a critical advancement in future defense strategies that need to be utilised in order to protect industrial control systems.

Modern technologies have enabled illicit firearms trafficking to partially merge with cybercrime, while also allowing its off-line aspects to become increasingly complex. The online trade of firearms, their components, 3D blueprints and illicit substances carried out by criminals on both the surface Web and dark Web is increasingly difficult to address as a consequence of the exponential growth in the amount of information disseminated on the Internet. On the other hand, law enforcement agencies are confronted with significant challenges that require the development of sophisticated technological solutions capable of processing large volumes of data, identifying relevant information in a timely manner and creating networks of connections between potential criminal groups. This article presents a real-world practical system, namely the CEASEFIRE one, powered by advanced artificial intelligence technologies that can assist law enforcement personnel in addressing the above described challenges.

The rise of financial crime that has been observed in recent years has created an increasing concern around the topic and many people, organizations and governments are more and more frequently trying to combat it. Despite the increase of interest in this area, there is a lack of specialized datasets that can be used to train and evaluate works that try to tackle those problems. This article proposes a new micro-benchmark dataset for algorithms and models that identify individuals and organizations, and their multiple writings, in news articles, and presents an approach that assists in its creation. Experimental efforts are also reported, using this dataset, to identify individuals and organizations in financial-crime-related articles using various low-billion parameter Large Language Models (LLMs). For these experiments, standard metrics (Accuracy, Precision, Recall, F1 Score) are reported and various prompt variants comprising the best practices of prompt engineering are tested. In addition, to address the problem of ambiguous entity mentions, a simple, yet effective LLM-based disambiguation method is proposed, ensuring that the evaluation aligns with reality. Finally, the proposed approach is compared against a widely used stateof-the-art open-source baseline, showing the superiority of the proposed method.

This paper introduces the Biometrics Data Space framework, which is a secure ecosystem built on Data Spaces technology and it is designed to address the challenges of suspect identification during cross-border crime investigation. Apart from Data Spaces technology, the proposed framework innovates by leveraging also Privacy Enhancing Technologies (PETs) and blockchain to enable secure, trustworthy, and sovereign data exchange between Law Enforcement Agencies (LEAs) across borders. Specifically, it utilizes advanced PETs, including Large-Scale Biometric Data Indexing based on deep hashing techniques and Homomorphic Encryption to allow for suspect identification without disclosing sensitive information of personal biometric data. Thus, it enables LEAs to securely compare and exchange encrypted sensitive biometric data, including facial images, fingerprints and voiceprints, while maintaining data privacy and data sovereignty. LEAs define the usage rules for the biometic data they own and these rules are enforced to and respected by the other LEAs participating in the Biometrics Data Space. The proposed architecture is designed to be scalable, allowing the incorporation of additional biometric modalitiies and the easy expansion and integration with new participant LEAs.

Cross-border collaboration among Law Enforcement Agencies is essential for effective and timely suspect identification, especially when the availability of biometric data varies between agencies. This paper presents a scalable and secure approach for multimodal biometric identification across multiple jurisdictions. Our approach allows Law Enforcement Agencies to combine biometric modalities -facial images, fingerprints, and voice samples- and compare them with collaborating agencies, improving the overall accuracy and effectiveness of suspect identification. By leveraging deep learning models for indexing and comparison, efficient data retrieval was achieved without compromising privacy or security. To ensure the protection of sensitive biometric data, our approach incorporates advanced encryption mechanisms, including Homomorphic Encryption for secure computations and Advanced Encryption Standard (AES encryption) for safeguarding biometric information. Its decentralised architecture allows each Law Enforcement Agency to maintain independent instances of the Deep Learning Indexer and Comparator, minimising risks associated with centralising sensitive data and supporting seamless collaboration between agencies. This approach not only improves the accuracy of suspect identification but also enhances operational efficiency by allowing Law Enforcement Agencies to query and share biometric data securely across borders.

In recent years, the protection of so-called "soft targets", has become an increasingly important and challenging issue. The complexity and seriousness of this security threat have been growing exponentially, particularly with the advent of advanced technologies such as Artificial Intelligence (AI), Autonomous Vehicles (AVs), and 3D printing, especially in the context of large-scale, popular, and diverse public spaces. In this paper, a novel Digital Twin-as-a-Security-Service (DTaaSS) architecture is introduced for holistically and significantly enhancing the protection of public spaces (e.g. metro stations, leisure sites, urban squares, etc.). The proposed framework combines a Digital Twin (DT) conceptualization with additional cutting-edge technologies, including Internet of Things (IoT), cloud computing, Big Data analytics and AI. In particular, DTaaSS comprises a holistic, real-time, large-scale, comprehensive and data-driven security solution for the efficient/robust protection of public spaces, supporting: a) data collection and analytics, b) area monitoring/control and proactive threat detection, c) incident/attack prediction, and d) quantitative and data-driven vulnerability assessment. Overall, the designed architecture exhibits increased potential in handling complex, hybrid and combined threats over large, critical and popular soft-targets. The applicability and robustness of DTaaSS is discussed in detail against representative and diverse real-world application scenarios, including complex attacks to: a) a metro station, b) a leisure site, and c) a cathedral square.

Automated visual firearms classification from RGB images is an important real-world task with applications in public space security, intelligence gathering and law enforcement investigations. When applied to images massively crawled from the World Wide Web (including social media and dark Web sites), it can serve as an important component of systems that attempt to identify criminal firearms trafficking networks, by analyzing Big Data from open-source intelligence. Deep Neural Networks (DNN) are the state-of-the-art methodology for achieving this, with Convolutional Neural Networks (CNN) being typically employed. The common transfer learning approach consists of pretraining on a large-scale, generic annotated dataset for whole-image classification, such as ImageNet-1k, and then finetuning the DNN on a smaller, annotated, task-specific, downstream dataset for visual firearms classification. Neither Visual Transformer (ViT) neural architectures nor Self-Supervised Learning (SSL) approaches have been so far evaluated on this critical task. SSL essentially consists of replacing the traditional supervised pretraining objective with an unsupervised pretext task that does not require ground-truth labels. This paper evaluates a common CNN and a typical ViT architecture in combination with different SSL methods, comparing them against each other and against supervised pretraining in terms of downstream classification accuracy. Additionally, “CrawledFirearmsRGB” is introduced as a new, challenging image dataset for visual classification of firearms and other concepts related to on-line criminal networks. Finally, a new mixed pretraining objective is formulated that combines SSL and whole-image classification, under a multitask learning setting. The experimental results, indicate the superiority of certain SSL pretraining methods that cooperate well with the ViT architecture, even when the pretraining dataset is of a scale similar or identical to that of CrawledFirearmsRGB, despite the fact that no ground-truth labels are exploited.

Automated detection of contraband items in X-ray images can significantly increase public safety, by enhancing the productivity and alleviating the mental load of security officers in airports, subways, customs/post offices, etc. The large volume and high throughput of passengers, mailed parcels, etc., during rush hours practically make it a Big Data problem. Modern computer vision algorithms relying on Deep Neural Networks (DNNs) have proven capable of undertaking this task even under resource-constrained and embedded execution scenarios, e.g., as is the case with fast, single-stage object detectors. However, no comparative experimental assessment of the various relevant DNN components/methods has been performed under a common evaluation protocol, which means that reliable cross-method comparisons are missing. This paper presents exactly such a comparative assessment, utilizing a public relevant dataset and a well-defined methodology for selecting the specific DNN components/modules that are being evaluated. The results indicate the superiority of Transformer detectors, the obsolete nature of auxiliary neural modules that have been developed in the past few years for security applications and the efficiency of the CSP-DarkNet backbone CNN.

This paper addresses the growing issue of terrorists utilizing the Internet, and particularly the Dark Web market places, with the purpose of fundraising for their illegal activities. It proposes the Visual Analytics (VA) system, an advanced AI-powered tool, in an effort to combat cross-border financing associated with terrorism. The tools focus on semantic concept detection and large-scale visual data indexing, and the ultimate goal is to familiarize end-users, practitioners, and law enforcement investigators with these technologies. This paper discusses prior works concerning the presentation and visualization of Deep Learning results to users, outlines the main objectives, and provides fundamental usage instructions for the VA system.

In this article, an approach to semantic image analysis is presented. Under the proposed approach, ontologies are used to capture general, spatial, and contextual knowledge of a domain, and a genetic algorithm is applied to realize the final annotation. The employed domain knowledge considers high-level information in terms of the concepts of interest of the examined domain, contextual information in the form of fuzzy ontological relations, as well as low-level information in terms of prototypical low-level visual descriptors. To account for the inherent ambiguity in visual information, uncertainty has been introduced in the spatial relations definition. First, an initial hypothesis set of graded annotations is produced for each image region, and then context is exploited to update appropriately the estimated degrees of confidence. Finally, a genetic algorithm is applied to decide the most plausible annotation by utilizing the visual and the spatial concepts definitions included in the domain ontology. Experiments with a collection of photographs belonging to two different domains demonstrate the performance of the proposed approach.