Search Results (2)

The rapid rise of AI-driven cybercrime and deepfake fraud poses complex organisational challenges for US law enforcement, particularly the Federal Bureau of Investigation (FBI). Applying Maguire’s (2003) police organisation theory, this qualitative single-case study analyses the FBI’s structure, culture, technological integration, and inter-agency collaboration. Findings underscore the organisational strengths of the FBI, including a specialised Cyber Division, advanced detection tools, and partnerships with agencies such as the Cybersecurity and Infrastructure Security Agency (CISA). However, constraints, such as resource limitations, detection inaccuracies, inter-agency rivalries, and ethical concerns, including privacy risks associated with AI surveillance, hinder operational effectiveness. Fragmented global legal frameworks, diverse national capacities, and inconsistent detection of advanced deepfakes further complicate responses to this issue. This study proposes the establishment of agile task forces, public–private partnerships, international cooperation protocols, and ethical AI frameworks to counter evolving threats, offering scalable policy and technological solutions for global law enforcement. 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