Search Results (10)

This study investigates the evolution of cybercrime in Greece by analyzing data from the Cyber Crime Division of the Hellenic Police. By combining 2023 statistics with earlier national and international data (e.g., Europol, FBI), this study presents a comprehensive 15-year view of cybercrime trends. Key findings highlight a persistent rise in cyber incidents, with financial fraud as the most common type. Other major threats include unauthorized system access, data breaches, and crimes targeting vulnerable populations. The study assesses national legislation aligned with EU directives and outlines stakeholder roles. It underscores the need for adaptive legal frameworks, inter-agency cooperation, and public awareness to mitigate Greece’s growing cybersecurity challenges. Full article

As more IoT devices become connected to the Internet, the attack surface for cybercrimes expands, leading to significant security concerns for these devices. Existing intrusion detection systems (IDSs) designed to address these concerns often suffer from high rates of false positives and missed threats due to the presence of redundant and irrelevant information for the IDSs. Furthermore, recent IDSs that utilize artificial intelligence are often presented as black boxes, offering no explanation of their internal operations. In this study, we develop a solution to the identified challenges by presenting a deep learning-based model that adapts to new attacks by selecting only the relevant information as inputs and providing transparent internal operations for easy understanding and adoption by cybersecurity personnel. Specifically, we employ a hybrid approach using statistical methods and a metaheuristic algorithm for feature selection to identify the most relevant features and limit the overall feature set while building an LSTM-based model for intrusion detection. To this end, we utilize two publicly available datasets, NF-BoT-IoT-v2 and IoTID20, for training and testing. The results demonstrate an accuracy of 98.42% and 89.54% for the NF-BoT-IoT-v2 and IoTID20 datasets, respectively. The performance of the proposed model is compared with that of other machine learning models and existing state-of-the-art models, demonstrating superior accuracy. To explain the proposed model’s predictions and increase trust in its outcomes, we applied two explainable artificial intelligence (XAI) tools: Local Interpretable Model-agnostic Explanations (LIME) and Shapley Additive Explanations (SHAP), providing valuable insights into the model’s behavior. Full article

The prevalence of cybercrime has emerged as a critical issue in contemporary society because of its far-reaching financial, social, and psychological implications. The negative effects of cyber-attacks extend beyond financial losses and disrupt people’s lives on social and psychological levels. Conventional practice involves cyber experts sourcing data from various outlets and applying personal discernment and rational inference to manually formulate cyber intelligence specific to a country. This traditional approach introduces personal bias towards the country-level cyber reports. However, this paper reports a novel approach where country-level cyber intelligence is automatically generated with artificial intelligence (AI), employing cyber-related social media posts and open-source cyber-attack statistics. Our innovative cyber threat intelligence solution examined 37,386 tweets from 30,706 users in 54 languages using sentiment analysis, translation, term frequency–inverse document frequency (TF-IDF), latent Dirichlet allocation (LDA), N-gram, and Porter stemming. Moreover, the presented study utilized 238,220 open-intelligence cyber-attack statistics from eight different web links, to create a historical cyber-attack dataset. Subsequently, AI-based algorithms, like convolutional neural network (CNN), and exponential smoothing were used for AI-driven insights. With the confluence of the voluminous Twitter-derived data and the array of open-intelligence cyber-attack statistics, orchestrated by the AI-driven algorithms, the presented approach generated seven-dimensional cyber intelligence for Australia and China in complete automation. Finally, the topic analysis on the cyber-related social media messages revealed seven main themes for both Australia and China. This methodology possesses the inherent capability to effortlessly engender cyber intelligence for any country, employing an autonomous modality within the realm of pervasive computational platforms. Full article

The growing prevalence of cybercrime, particularly among young adults, necessitates the promotion of digital citizenship to educate students about responsible online behavior and to equip them with the skills to mitigate cyber risks. The specific objective of this study was to investigate the effect of digital citizenship skills on the prevention of cybercrime among higher education students. A mixed-method approach, including surveys and interviews, was employed to collect data from 652 students in Saudi Arabia. This study found that digital citizenship generally has a significant impact on students’ awareness and prevention of cybercrime through the development of responsible online behavior. Knowledge of digital law came first, followed by beliefs about digital manners. Digital communication skills came third, followed by digital rights, knowledge, and duties in fourth place. Then, digital commerce skills and digital health beliefs came fifth and sixth, respectively. This was followed by digital access skills, then digital security, and finally digital culture. The results also revealed a negative statistical relationship between digital citizenship and cybercrimes’ various forms including national, financial, banking, social, immoral, insulting, slanderous, defaming, threatening, and harassment in virtual learning environments. These findings have significant implications for the understanding of how higher education institutions can promote digital citizenship and prevent cybercrime by integrating digital citizenship education into their curriculum, providing training for educators, and establishing clear policies and guidelines for responsible online behavior. Full article

Cybercrime is one of the most significant security challenges of the 21st century. However, official statistics do not provide insights into its prevalence and nature. Representative cross-sectional field studies may help fill this gap, focusing on differences between urban and rural technology users. We (a) investigated the association between the purpose of computers and other electronic device usage and perceived vulnerability, (b) compared the differences in the purpose of computers or other electronic device use and perceived vulnerability, and (c) compared the perceived cyber victimization between residents of rural and urban areas. We conducted a field study that resulted in a representative sample of the Republic of Slovenia in Europe. We found several significant differences in the purpose of technology use and perceived cyber victimization. Furthermore, the results indicate that the purpose of technology use is somehow associated with perceived vulnerability in cyberspace; however, such associations are different in cyberspace than in the material world. Full article

The anonymous system Tor uses an asymmetric algorithm to protect the content of communications, allowing criminals to conceal their identities and hide their tracks. This malicious usage brings serious security threats to public security and social stability. Statistical analysis of traffic flows can effectively identify and classify Tor flow. However, few features can be extracted from Tor traffic, which have a weak representational ability, making it challenging to combat cybercrime in real-time effectively. Extracting and utilizing more accurate features is the key point to improving the real-time detection performance of Tor traffic. In this paper, we design an efficient and real-time identification scheme for Tor traffic based on the time window method and bidirectional statistical characteristics. In this paper, we divide the network traffic by sliding the time window and then calculate the relative entropy of the flows in the time window to identify Tor traffic. We adopt a sequential pattern mining method to extract bidirectional statistical features and classify the application types in the Tor traffic. Finally, extensive experiments are carried out on the UNB public dataset (ISCXTor2016) to validate our proposal’s effectiveness and real-time property. The experiment results show that the proposed method can detect Tor flow and classify Tor flow types with an accuracy of 93.5% and 91%, respectively, and the speed of processing and classifying a single flow is 0.05 s, which is superior to the state-of-the-art methods. Full article

In the modern knowledge–information age, various information and communication technologies provide us with many benefits and at the same time, bring various side effects such as cybercrime. The number of cybercrimes is increasing gradually, and in particular, the number of digital sex crimes has been increasing recently. In addition, digital sex crimes are becoming increasingly violent, so national measures are needed. In this study, statistical data at the national level were used to investigate the overall characteristics of digital sex crimes in Korea. First, statistical analysis shows that the victims are mainly women in their teens and 20s. Typical types of digital sex crimes are distribution of illegal contents and illegal filming, the perpetrators are mainly unknown, and digital sex crimes were less often recognized by others and more often by the victims themselves. Based on these results, countermeasures against various digital sex crimes are suggested. Full article

Information exchange has become increasingly faster and efficient through the use of recent technological advances, such as instant messaging and social media platforms. Consequently, access to information has become easier. However, new types of cybersecurity threats that typically result in data loss and information misuse have emerged simultaneously. Therefore, maintaining data privacy in complex systems is important and necessary, particularly in organizations where the vast majority of individuals interacting with these systems is students. In most cases, students engage in data breaches and digital misconduct due to the lack of knowledge and awareness of cybersecurity and the consequences of cybercrime. The aim of this study was to investigate and evaluate the level of cybersecurity awareness and user compliance among undergraduate students at Majmaah University using a scientific questionnaire based on several safety factors for the use of the Internet. We quantitatively evaluated the knowledge of cybercrime and protection among students to show the need for user education, training, and awareness. In this study, we used a quantitative research methodology and conducted different statistical tests, such as ANOVA, Kaiser–Meyer–Olkin (KMO), and Bartlett’s tests, to evaluate and analyze the hypotheses. Safety concerns for electronic emails, computer viruses, phishing, forged ads, popup windows, and supplementary outbreaks on the Internet were well-examined in this study. Finally, we present recommendations based on the collected data to deal with this common problem. Full article

Steganalysis of adaptive multi-rate (AMR) speech is a hot topic for controlling cybercrimes grounded in steganography in related speech streams. In this paper, we first present a novel AMR steganalysis model, which utilizes extreme gradient boosting (XGBoost) as the classifier, instead of support vector machines (SVM) adopted in the previous schemes. Compared with the SVM-based model, this new model can facilitate the excavation of potential information from the high-dimensional features and can avoid overfitting. Moreover, to further strengthen the preceding features based on the statistical characteristics of pulse pairs, we present the convergence feature based on the Markov chain to reflect the global characterization of pulse pairs, which is essentially the final state of the Markov transition matrix. Combining the convergence feature with the preceding features, we propose an XGBoost-based steganalysis scheme for AMR speech streams. Finally, we conducted a series of experiments to assess our presented scheme and compared it with previous schemes. The experimental results demonstrate that the proposed scheme is feasible, and can provide better performance in terms of detecting the existing steganography methods based on AMR speech streams. Full article

With an increasing pervasiveness, prevalence and severity of cybercrimes, various metrics, measures and statistics have been developed and used to measure various aspects of this phenomenon. Cybercrime-related data, metrics, and information, however, pose important and difficult dilemmas regarding the issues of reliability, validity, comparability and practical utility. While many of the issues of the cybercrime economy are similar to other underground and underworld industries, this economy also has various unique aspects. For one thing, this industry also suffers from a problem partly rooted in the incredibly broad definition of the term “cybercrime”. This article seeks to provide insights and analysis into this phenomenon, which is expected to advance our understanding into cybercrime-related information. Full article