Search Results (29)

The landscape of cybercrime has undergone significant transformations over the past decade. Present-day threats include AI-generated attacks, deep fakes, 5G network vulnerabilities, cryptojacking, and supply chain attacks, among others. To remain resilient against contemporary threats, it is essential to examine historical data to gain insights that can inform cybersecurity strategies, policy decisions, and public awareness campaigns. This paper presents a comprehensive analysis of the evolution of cyber trends in state-sponsored attacks over the past 20 years, based on the council on foreign relations state-sponsored cyber operations (2005–present). The study explores the key trends, patterns, and demographic shifts in cybercrime victims, the evolution of complaints and losses, and the most prevalent cyber threats over the years. It also investigates the geographical distribution, the gender disparity in victimization, the temporal peaks of specific scams, and the most frequently reported internet crimes. The findings reveal a traditional cyber landscape, with cyber threats becoming more sophisticated and monetized. Finally, the article proposes areas for further exploration through a comprehensive analysis. It provides a detailed chronicle of the trajectory of cybercrimes, offering insights into its past, present, and future. Full article

Every minute, vast amounts of video and image data are uploaded worldwide to the internet and social media platforms, creating a rich visual archive of human experiences—from weddings and family gatherings to significant historical events such as war crimes and humanitarian crises. When properly analyzed, this multimodal data holds immense potential for reconstructing important events and verifying information. However, challenges arise when images and videos lack complete annotations, making manual examination inefficient and time-consuming. To address this, we propose a novel event-based focal visual content text attention (EFVCTA) framework for automated past event retrieval using visual question answering (VQA) techniques. Our approach integrates a Long Short-Term Memory (LSTM) model with convolutional non-linearity and an adaptive attention mechanism to efficiently identify and retrieve relevant visual evidence alongside precise answers. The model is designed with robust weight initialization, regularization, and optimization strategies and is evaluated on the Common Objects in Context (COCO) dataset. The results demonstrate that EFVCTA achieves the highest performance across all metrics (88.7% accuracy, 86.5% F1-score, 84.9% mAP), outperforming state-of-the-art baselines. The EFVCTA framework demonstrates promising results for retrieving information about past events captured in images and videos and can be effectively applied to scenarios such as documenting training programs, workshops, conferences, and social gatherings in academic institutions Full article

Recently, video surveillance systems have evolved from expensive, human-operated monitoring systems that were only useful after the crime was committed to systems that monitor 24/7, in real time, and with less and less human involvement. This is partly due to the use of smart cameras, the improvement of the Internet, and AI-based algorithms that allow the classifying and tracking of objects in images and in some cases identifying them as threats. Threats are often associated with abnormal or unexpected situations such as the presence of unauthorized persons in a given place or time, the manifestation of a different behavior by one or more persons compared to the behavior of the majority, or simply an unexpected number of people in the place, which depends largely on the available information of their context, i.e., place, date, and time of capture. In this work, we propose a model to automatically contextualize video capture scenarios, generating data such as location, date, time, and flow of people in the scene. A strategy to measure the accuracy of the data generated for such contextualization is also proposed. The pre-trained YOLO11n algorithm and the Bot-SORT algorithm gave the best results in person detection and tracking, respectively. Full article

This study conducts a systematic literature review following the PRISMA framework and the guidelines of Kitchenham and Charters to analyze the application of Internet of Things (IoT) technologies and deep learning models in monitoring violent actions and criminal activities in smart cities. A total of 45 studies published between 2010 and 2024 were selected, revealing that most research, primarily from India and China, focuses on cybersecurity in IoT networks (76%), while fewer studies address the surveillance of physical violence and crime-related events (17%). Advanced neural network models, such as Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and hybrid approaches, have demonstrated high accuracy rates, averaging over 97.44%, in detecting suspicious behaviors. These models perform well in identifying anomalies in IoT security; however, they have primarily been tested in simulation environments (91% of analyzed studies), most of which incorporate real-world data. From a legal perspective, existing proposals mainly emphasize security and privacy. This study contributes to the development of smart cities by promoting IoT-based security methodologies that enhance surveillance and crime prevention in cities in developing countries. Full article

In recent years, there has been a sharp increase in the number of online fraud cases. However, research on crime geography has paid little attention to online crimes, especially to the influencing factors behind their spatial distributions. Online fraud is closely related to people’s daily internet use. The existing literature has explored the impact of internet use on online crimes based on small samples of individual interviews. There is a lack of large-scale studies from a community perspective. This study applies the routine activity theory to online activities to test the relationship between online fraud alert data and the usage durations of different types of mobile phone users’ applications (apps) for communities in ZG City. It builds negative binomial regression models for analyzing the impact of the usage of different types of apps on the spatial distribution of online fraud. The results reveal that the online fraud crime rate and the online time spent on a financial management app share the most similar spatial distribution. While financial management, online education, transportation, and search engine app usages have a significant positive association with online fraud, the use of a financial management app has the greatest impact. Additionally, time spent on social media, online shopping and entertainment, and mobile reading apps have a significant negative association with online fraud. As not all online activities lead to cybercrime, crime prevention efforts should target specific types of apps, such as financial management, online education, transportation, and search engines. Full article

The advancement of Internet of Things (IoT), robots, drones, and vehicles signifies ongoing progress, accompanied by increasing complexities and challenges in forensic investigations. Globally, investigators encounter obstacles when extracting evidence from these vast landscapes, which include diverse devices, networks, and cloud environments. Of particular concern is the process of evidence collection, especially regarding fingerprints and facial recognition within the realm of vehicle forensics. Moreover, ensuring the integrity of forensic evidence is a critical issue, as it is vulnerable to attacks targeting data centres and server farms. Mitigating these challenges, along with addressing evidence mobility, presents additional complexities. This paper introduces a groundbreaking infrastructure known as SpiderNet, which is based on cloud computing principles. We will illustrate how this architecture facilitates the identification of devices, secures the integrity of evidence both at its source and during transit, and enables investigations into individuals involved in criminal activities. Through case studies, we will demonstrate the potential of SpiderNet to assist law enforcement agencies in addressing crimes perpetrated within IoT environments. Full article

Object tracking and detection are fundamental and challenging tasks in various computer vision applications, spanning surveillance, vehicle navigation, and autonomous robot control. These tasks are particularly critical in the context of video monitoring within dynamic environments, where the detection and tracking of objects, such as people and automobiles, play a pivotal role. In today’s world, as we combat crime and terrorism, ensure public safety, and manage traffic effectively, advanced computer vision technology has become indispensable. Video monitoring in dynamic environments is at the forefront of this battle, providing crucial insights and real-time information for decision making. Object-tracking-based techniques emerge as a strong choice, especially for detecting stationary foreground objects. These methods exhibit robust performances when the camera remains stationary, even in scenarios in which the ambient lighting conditions gradually change. This stability makes them well suited for applications requiring consistent and reliable object detection. In the contemporary landscape, one of the most pressing concerns revolves around the recognition of objects and the real-time tracking of their locations. Achieving these objectives is paramount for enhancing security, safety, and efficiency across various domains. However, it is essential to acknowledge that, in some scenarios, such as remote or isolated locations with limited Internet connectivity, access to advanced object-tracking and detection technologies may be constrained. Therefore, addressing these challenges and developing robust, offline-capable solutions remains a critical area of research and development in computer vision. In conclusion, object tracking and detection are pivotal technologies in computer vision, with applications spanning from surveillance to traffic management. In dynamic environments, they play a crucial role in enhancing security and safety. However, addressing the challenges related to real-time tracking and detection in resource-constrained settings is an ongoing research endeavor. Full article

Driven by the rapid escalation of its utilization, as well as ramping commercialization, Internet of Things (IoT) devices increasingly face security threats. Apart from denial of service, privacy, and safety concerns, compromised devices can be used as enablers for committing a variety of crime and e-crime. Despite ongoing research and study, there remains a significant gap in the thorough analysis of security challenges, feasible solutions, and open secure problems for IoT. To bridge this gap, we provide a comprehensive overview of the state of the art in IoT security with a critical investigation-based approach. This includes a detailed analysis of vulnerabilities in IoT-based systems and potential attacks. We present a holistic review of the security properties required to be adopted by IoT devices, applications, and services to mitigate IoT vulnerabilities and, thus, successful attacks. Moreover, we identify challenges to the design of security protocols for IoT systems in which constituent devices vary markedly in capability (such as storage, computation speed, hardware architecture, and communication interfaces). Next, we review existing research and feasible solutions for IoT security. We highlight a set of open problems not yet addressed among existing security solutions. We provide a set of new perspectives for future research on such issues including secure service discovery, on-device credential security, and network anomaly detection. We also provide directions for designing a forensic investigation framework for IoT infrastructures to inspect relevant criminal cases, execute a cyber forensic process, and determine the facts about a given incident. This framework offers a means to better capture information on successful attacks as part of a feedback mechanism to thwart future vulnerabilities and threats. This systematic holistic review will both inform on current challenges in IoT security and ideally motivate their future resolution. Full article

Gun violence and mass shootings kill and injure people, create psychological trauma, damage properties, and cause economic loss. The loss from gun violence can be reduced if we can detect the gunshot early and notify the police as soon as possible. In this project, a novel gunshot detector device is developed that automatically detects indoor gunshot sound and sends the gunshot location to the nearby police station in real time using the Internet. The users of the device and the emergency responders also receive smartphone notifications whenever the shooting happens. This will help the emergency responders to quickly arrive at the crime scene, thus the shooter can be caught, injured people can be taken to the hospital quickly, and lives can be saved. The gunshot detector is an electronic device that can be placed in schools, shopping malls, offices, etc. The device also records the gunshot sounds for post-crime scene analysis. A deep learning model, based on a convolutional neural network (CNN), is trained to classify the gunshot sound from other sounds with 98% accuracy. A prototype of the gunshot detector device, the central server for the emergency responder’s station, and smartphone apps have been developed and tested successfully. Full article

Cybercrime presents a significant threat to global society. With the number of cybercrimes increasing year after year and the financial losses escalating, law enforcement must advance its capacity to identify cybercriminals, collect probative evidence, and bring cybercriminals before the courts. Arguably to date, the approach to combatting cybercrime has been technologically centric (e.g., anti-virus, anti-spyware). Cybercrimes, however, are the result of human activities based on human motives. It is, therefore, important that any comprehensive law enforcement strategy for combatting cybercrime includes a deeper understanding of the hackers that sit behind the keyboards. The purpose of this systematic review was to examine the state of the literature relating to the application of a human-centric investigative tool (i.e., profiling) to cybercrime by conducting a qualitative meta-synthesis. Adhering to the PRISMA 2020 guidelines, this systematic review focuses specifically on cybercrime where a computer is the target (e.g., hacking, DDoS, distribution of malware). Using a comprehensive search strategy, this review used the following search terms: “cybercrime”, “computer crime”, “internet crime”, “cybercriminal”, “hacker”, “black hat”, “profiling”, “criminal profiling”, “psychological profiling”, “offender profiling”, “criminal investigative analysis”, “behavioral profiling”, “behavioral analysis”, “personality profiling”, “investigative psychology”, and “behavioral evidence analysis” in all combinations to identify the relevant literature in the ACM Digital Library, EBSCOhost databases, IEEE Xplore, ProQuest, Scopus, PsychInfo, and Google Scholar. After applying the inclusion/exclusion criteria, a total of 72 articles were included in the review. This article utilizes a systematic review of the current literature on cyber profiling as a foundation for the development of a comprehensive framework for applying profiling techniques to cybercrime—described as cyber behavioral analysis (CBA). Despite decades of research, our understanding of cybercriminals remains limited. A lack of dedicated researchers, the paucity of research regarding human behavior mediated by technology, and limited access to datasets have hindered progress. The aim of this article was to advance the knowledge base in cyber behavioral sciences, and in doing so, inform future empirical research relating to the traits and characteristics of cybercriminals along with the application of profiling techniques and methodologies to cybercrime. Full article

Modern armed conflicts can cause serious humanitarian disasters, and remote sensing technology is critical in monitoring war crimes and assessing post-war damage. In this study, a constrained energy minimization algorithm incorporating the feature bands (IFB-CEM) is designed to detect urban burning areas in optical images. Due to the difficulty of obtaining the ground survey data of the battlefield, the dual-polarization normalized coherence index (DPNCI) is designed based on the multi-temporal synthetic aperture radar (SAR) image, and the quantitative inversion and evaluation of the destruction of urban architecture are combined with the public images on the Internet. The results show that the burning area is widely distributed in the armed conflict region, and the distribution is most concentrated around the Azovstal steel and iron works. The burning area reached its peak around 22 March, and its change is consistent with the conflict process in time and space. About 79.2% of the buildings in the city were severely damaged or completely destroyed, and there was a significant correlation with burning exposure. The results of this study show that publicly available medium-resolution remote sensing data and Internet information have the ability to respond quickly to the damage assessment of armed conflict and can provide preliminary reference information for dealing with humanitarian disasters. Full article

The rapid development of digital finance has changed all aspects of human life and has also had a deep impact on the social governance system. This paper constructs an unbalanced panel of data of the theft crime rates for 289 cities in China during 2014–2019 based on the theft criminal judgments published on China’s Judicial Documents website and explores the impact of digital financial inclusion on urban theft crime. It shows that there is a significantly negative correlation between digital financial inclusion and the urban theft crime rate, indicating that the development of digital financial inclusion can effectively reduce urban theft crime, which is also confirmed by instrumental variable analysis based on the spherical distance between cities and Hangzhou, and that digital financial inclusion mainly reduces theft crime committed by more serious and highly educated individuals. In addition, mechanism analysis shows that digital financial inclusion can reduce the expected benefits of theft by enhancing payment convenience and raise the opportunity cost by promoting employment. Therefore, in the Internet era, it is essential for China to continuously improve social governance tools that adapt to the development of new technologies to achieve high-quality urban development. Full article

Cybersecurity has become a prominent issue in regard to ensuring information privacy and integrity in the internet age particularly with the rise of interconnected devices. However, advanced persistent threats (APTs) pose a significant danger to the current contemporary way of life, and effective APT detection and defense are vital. Game theory is one of the most sought-after approaches adopted against APTs, providing a framework for understanding and analyzing the strategic interactions between attackers and defenders. However, what are the most recent developments in game theory frameworks against APTs, and what approaches and contexts are applied in game theory frameworks to address APTs? In this systematic literature review, 48 articles published between 2017 and 2022 in various journals were extracted and analyzed according to PRISMA procedures and our formulated research questions. This review found that game-theory approaches have been optimized for the defensive performance of security measures and implemented to anticipate and prepare for countermeasures. Many have been designed as part of incentive-compatible and welfare-maximizing contracts and then applied to cyber–physical systems, social networks, and transportation systems, among others. The trends indicate that game theory provides the means to analyze and understand complex security scenarios based on technological advances, changes in the threat landscape, and the emergence of new trends in cyber-crime. In this study, new opportunities and challenges against APTs are outlined, such as the ways in which tactics and techniques to bypass defenses are likely to evolve in order to evade detection, and we focused on specific industries and sectors of high interest or value (e.g., healthcare, finance, critical infrastructure, and the government). Full article

With the development and spread of information and communication technology, our society is experiencing side effects of digital culture while also benefiting from various digital cultures. Representative side effects have spread significantly, including Internet addiction, copyright infringement, personal information infringement, and digital sex crimes. Digital sex crimes are very serious crimes, and we must find their causes and strongly prevent and deal with them at the social level. In this study, the causes and routes of occurrence of digital sex crimes in Korea are analyzed using statistics on digital sex crimes at the national level over the past four years. The statistical analysis results are as follows. First, the main victims of digital sex crimes are women in their teens and twenties, though the number of male victims is steadily increasing. Second, illegal filming is the most common type of digital sex crime, but it is not statistically significant. In other words, various digital sex crimes are occurring evenly. Third, the relationship between the victim and the perpetrator demonstrates the most temporary relationship, and there is no significant correlation between direct and indirect recognition with respect to the route of crime recognition. Finally, deletion by a digital platform is the highest for adult sites compared to other platforms. Based on these analysis results, this study proposes educational countermeasures to digital sex crimes, such as the need for early education to prevent digital sex crimes and the diversification of crime-reporting methods via the establishment of an educational portal site. Full article

Online harassment, particularly cyberbullying and the non-consensual sharing of intimate images, is a widespread phenomenon among adolescents and young adults. Descriptive research was carried out to investigate any differences among Italian school classes in the perception of cybercrime through a real-case scenario. Following the Italian school system, the final sample of 1777 adolescents (Mage = 15.37, SD = 1.65; Male = 52%) was divided into three groups based on the school class attended: middle school (N = 562; Mage = 13.37, SD = 0.48); high school biennium (N = 728; Mage = 15.55, SD = 0.50), and triennium (N = 487, Mage = 17.40, SD = 0.71). Participants completed a self-report questionnaire investigating the use of the Internet and the perception of a real case scenario involving the non-consensual sharing of intimate images and cyberbullying received by the National Centre for Combating Child Pornography Online (NCPO). Results showed differences among the three groups’ perceptions of the event’s features, motivations underlying the offense, victim-blaming and harassment justification (e.g., cyberbullying, in particular non-consensual sharing of intimate images, is recognized as a crime as age increases). The findings provide significant insights for future research and age-specific factors to consider when developing prevention programs for online risks. Full article