Search Results (2)

Ransomware operations have evolved from isolated malware incidents into organized ransomware-as-a-service (RaaS) ecosystems that employ coordinated tactics, techniques, and procedures and increasingly rely on automation and artificial intelligence to scale intrusions. However, most assessments remain artifact-centric, focusing on malware signatures or aggregate victim counts, which provide limited visibility into differences in actor-level behavior and operational capability. This study introduces the AI-Amplification Indicator (AIAI), an interpretable actor-level scoring framework that transforms publicly observable leak-site disclosures and verifiable open-source evidence into quantitative behavioral profiles. Using continuous monitoring of dark web leak portals, we construct a standardized dataset of ransomware disclosures for 2025 with temporal, geographic, and sector metadata. AIAI measures four complementary dimensions: GenAI-enabled social engineering, operational tempo and orchestration, targeting breadth and diversification, and temporal scaling dynamics. Indicators are computed for all observed actors, while comparative profiling focuses on the ten most active actors to ensure stable behavioral estimation. The analysis reveals substantial heterogeneity in posting cadence, targeting strategies, and scaling dynamics, as well as limited but measurable evidence of automated or AI-assisted deception. These differences are not captured by victim counts alone. The proposed framework provides a transparent and reproducible approach for actor-level ransomware intelligence, enabling systematic comparison of operational styles and supporting data-driven defensive prioritization. Full article

Technological achievement and cybercriminal methodology are two parallel growing paths; protocols such as Tor and i2p (designed to offer confidentiality and anonymity) are being utilised to run ransomware companies operating under a Ransomware as a Service (RaaS) model. RaaS enables criminals with a limited technical ability to launch ransomware attacks. Several recent high-profile cases, such as the Colonial Pipeline attack and JBS Foods, involved forcing companies to pay enormous amounts of ransom money, indicating the difficulty for organisations of recovering from these attacks using traditional means, such as restoring backup systems. Hence, this is the benefit of intelligent early ransomware detection and eradication. This study offers a critical review of the literature on how we can use state-of-the-art machine learning (ML) models to detect ransomware. However, the results uncovered a tendency of previous works to report precision while overlooking the importance of other values in the confusion matrices, such as false negatives. Therefore, we also contribute a critical evaluation of ML models using a dataset of 730 malware and 735 benign samples to evaluate their suitability to mitigate ransomware at different stages of a detection system architecture and what that means in terms of cost. For example, the results have shown that an Artificial Neural Network (ANN) model will be the most suitable as it achieves the highest precision of 98.65%, a Youden’s index of 0.94, and a net benefit of 76.27%, however, the Random Forest model (lower precision of 92.73%) offered the benefit of having the lowest false-negative rate (0.00%). The risk of a false negative in this type of system is comparable to the unpredictable but typically large cost of ransomware infection, in comparison with the more predictable cost of the resources needed to filter false positives. Full article