The rise of blockchain technology and cryptocurrencies such as Bitcoin and Ethereum has created new avenues for both lawful and illicit activities, including illegal firearm transactions. This study applies a combination of graph-based analysis, functional data techniques, and machine learning to detect and classify suspicious activities related to firearm trafficking on blockchain networks. A Random Forest model, achieving a precision of 0.907 and recall of 0.786, was used to identify illicit Bitcoin addresses, while a multi-target classifier categorized these addresses by specific types of illicit activity. For Ethereum, an XGBoost model achieved a precision of 0.9864 and an accuracy of 0.9901, demonstrating robust detection of suspicious accounts. Feature engineering and a rule-based system further enhanced model performance, though challenges remain in addressing misclassifications, particularly in distinguishing subtle transaction patterns. These findings underscore the potential of machine learning in blockchain forensics, providing critical insights for law enforcement efforts to combat illegal firearm trading.

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 Dark Web, known for its anonymity and illicit activities, presents considerable challenges for Law Enforcement Agencies (LEAs) due to the complexity and volume of data generated within it. Online marketplaces on the Dark Web are notorious for facilitating illegal activities such as drug trafficking, counterfeit goods, and weapons sales while using advanced obfuscation techniques to avoid detection. The unstructured nature of data on these platforms and their constantly evolving operations make manual extraction and analysis exceedingly difficult.This paper addresses the pressing need for structured information extraction from Dark Web marketplaces, with a specific focus on firearm-related listings. Traditional rule-based methods have proven inadequate due to their reliance on HTML tags and pattern recognition, necessitating more adaptive solutions. Thus, the application of Large Language Models (LLMs) and Prompt Engineering to tackle these challenges is explored. By leveraging the capabilities of LLMs, this study aims to transform the extraction process into a more efficient and accurate system. Various generative models and prompt formulations are tested, to determine the most effective approach for extracting detailed information such as product specifications, pricing, and seller details.The proposed pipeline involves feeding crawled marketplace pages into a generative model, which then identifies Product Details Pages (PDPs) and consequently extracts relevant information from them. The use of LLMs marks a significant advancement over traditional methods, enhancing the accuracy and comprehensiveness of data extraction. Additionally, this research highlights the effectiveness of prompt engineering in improving information retrieval.This work underscores the critical need for sophisticated tools to monitor and combat illegal activities on the Dark Web, particularly in the context of firearm trafficking. By refining techniques for automated data extraction and applying cutting-edge LLM and prompt engineering methods, this study aims to support LEAs in their efforts to disrupt and dismantle criminal networks and enhance public safety.

With the introduction of 3D printing technology, manufacturing has undergone a revolutionary change, offering unprecedented access to custom object production. However, this democratization has also led to significant public safety concerns, particularly regarding the creation and distribution of 3D-printed firearms. This paper outlines a vision for a comprehensive framework to monitor and analyze the lifecycle of 3D firearm models, employing advanced tools such as web crawling, Natural Language Understanding (NLU), 3D file analysis, and image classification. The framework aims to track the distribution of 3D files across online platforms, analyze the contents of 3D files to identify firearm components, and detect 3D-printed firearms in images. By leveraging these technologies, law enforcement agencies (LEAs) can gain actionable insights for reducing the risks posed by illicit 3D printing activities.

This research delves into the dual nature of cryptocurrencies, offering financial opportunities while addressing the surge in digital criminal activities, especially in illegal firearms trafficking. The decentralized nature of blockchain technology presents unique challenges for law enforcement, necessitating innovative approaches to uncover and prevent criminal transactions. The study utilizes advanced data mining, analytics techniques, and machine learning models to analyze transactional graphs of prominent cryptocurrencies, aiming to identify and thwart transactions linked to illegal firearms trafficking.Additionally, the paper provides an overview of the current state of blockchain technology research and introduces the ambitious Ceasefire project. This initiative outlines a systematic approach to combat illegal firearms trading within the cryptocurrency domain, leveraging cutting-edge techniques and strategic partnerships with leading blockchain analysis platforms.By proposing a novel method, this paper enhances the ability to detect illicit firearms trading in cryptocurrencies, specifically focusing on Bitcoin and Ethereum networks. The approach combines predictive modeling with rule-based matching to identify potentially suspicious addresses in both ecosystems. This empowers authorities to track individuals attempting to conceal their transactional activities by transitioning between Bitcoin and Ethereum, thus bolstering efforts to maintain the integrity of decentralized financial systems.

Online discussion boards have become a tool for traffickers to widen their reach in the illegal firearm trade. This provides opportunities for authorities to detect firearm trafficking networks, as well as interdict arms shipments arranged by such networks. The present work offers a fresh solution to the pressing problem of spotting dubious firearm transaction chats on online discussion boards. The suggested methodology integrates state-of-the-art capabilities, such as natural language understanding and unsupervised clustering methods, to support the creation of more efficient cybercrime solutions. The necessity to target and flag questionable discussions about the trading of firearms in Internet forums is the main issue this work addresses. The major claim in this study centers on the use of intent recognition sequence patterns within dialogues and unsupervised clustering of such sequences. This method groups conversations based on the general direction and subject. The research methodology involves several key steps: i) data collection and manual annotation from the subreddit r/GunDeals, chosen due to its proximity to the primary subject of illicit firearm tracking, ii) training an intent classification Transformer-based model to generate intent sequences for each conversation, iii) preprocessing the intent sequences for clustering and encoding them using a polynomial method. iv) implementing clustering techniques while tuning various hyperparameters to optimize the results. The proposed methodology effectively classifies conversations into meaningful clusters, providing actionable insights. This research contributes to the body of knowledge by presenting a novel approach to detecting suspicious firearm trade conversations online. The practical implications are significant, as this work can be leveraged by law enforcement agencies to enhance their Internet scanning capabilities. The ability to efficiently process large volumes of text conversations and flag specific content can aid in addressing illicit firearm trading more effectively.

Photonic circuits pave the way to ultrafast computing and real-time inference of applications with paramount importance, such as imaging flow cytometry (IFC). However, current implementations exhibit inherent restrictions that consequently diminish the neural networks (NNs) complexity that can be supported.

This work presents a comparative analysis of human-crafted and automated feature extraction approaches for the discrimination of hand-based activities among eating, drinking and smoking. In this scheme, accelerometer and gyroscope sensors were utilised to capture activity signals. For this reason, wearable devices that embed the aforementioned sensors were employed to collect activity data from 12 office workers. The two approaches that were developed for feature mapping were evaluated equally on the collected dataset. Both the proposed schemes achieved to classify the hand-based activities. However, based on the experimental process, this study shows that the human-crafted features that extracted valuable information from the time and frequency domain of the raw signal measurements outperformed the automated feature mapping that utilised deep learning advances. The relevant results prove that the human-crafted features can recognise hand-based activities with 0.9109 and, on the other hand, automated features with a 0.907 F1 weighted score over the dataset.