Federated Learning (FL) has recently arisen as a revolutionary approach to collaborative training Machine Learning models. According to this novel framework, multiple participants train a global model collaboratively, coordinating with a central aggregator without sharing their local data. As FL gains popularity in diverse domains, security, and privacy concerns arise due to the distributed nature of this solution. Therefore, integrating this strategy with Blockchain technology has been consolidated as a preferred choice to ensure the privacy and security of participants. This paper explores the research efforts carried out by the scientific community to define privacy solutions in scenarios adopting Blockchain-Enabled FL. It comprehensively summarizes the background related to FL and Blockchain, evaluates existing architectures for their integration, and the primary attacks and possible countermeasures to guarantee privacy in this setting. Finally, it reviews the main application scenarios where Blockchain-Enabled FL approaches have been proficiently applied. This survey can help academia and industry practitioners understand which theories and techniques exist to improve the performance of FL through Blockchain to preserve privacy and which are the main challenges and future directions in this novel and still under-explored context. We believe this work provides a novel contribution concerning the previous surveys and is a valuable tool to explore the current landscape, understand perspectives, and pave the way for advancements or improvements in this amalgamation of Blockchain and Federated Learning.

With the increasing complexity and frequency of cyber attacks, organizations recognize the need for a proactive and targeted approach to safeguard their digital assets and operations. Every industry faces a distinct array of threats shaped by factors such as its industrial objective, geographic footprint, workforce size, revenue, partnerships, and the extent of its digital assets. This results in a wide heterogeneity in threat landscapes, which necessitates tailored threat intelligence sources. While some security practitioners may gravitate towards extensive sources, relying solely on volume-based solutions often leads to “alert fatigue”. For this reason, organization-specific threat intelligence has acquired a growing importance in cybersecurity defense. This work presents a complete and novel framework called OSTIS (Organization-Specific Threat Intelligence System) for generating and managing organization-specific Cyber Threat Intelligence (CTI) data. Our approach identifies reliable security blogs from which we gather CTI data through a custom and focused Web Crawler. Relevant content from such sources is, then, identified and extracted using automated deep-learning models. Moreover, our AI-driven solution maps CTI data to specific domain scenarios, such as education, finance, government, healthcare, industrial control systems, and IoT. To validate and gain insights from the trained models, we also include an explainable AI (XAI, for short) task carried out by leveraging the SHapley Additive exPlanations (SHAP) tool. This allows us to interpret the prediction process and discern influential content from data. The last step of our framework consists of the generation of an Organization Specific Threat Intelligence Knowledge Graph (OSTIKG), empowering organizations to identify and visualize attack patterns and incidents, promptly. To create this graph, we develop and adapt several techniques to extract diverse entities, including malware groups, campaigns, attack types, malware types, software tools, and so forth, and to identify relationships among them. Finally, through an extensive experimental campaign, we certify the validity and performance of all the components of our framework, which shows a 0.84 F1-score in the identification of relevant content, a 0.93 F1-score for the domain classification, and a 0.95 and 0.89 F1-score in the identification of entities and relations to build our OSTIKG graph.

Cyber attackers leverage malware to infiltrate systems, steal sensitive data, and extort victims, posing a significant cybersecurity threat. Security experts address this challenge by employing machine learning and deep learning approaches to detect malware precisely, using static, dynamic, or hybrid methodologies. They visualize malware to identify patterns, behaviors, and common features across different malware families. Various methods and tools are used for malware visualization to represent different aspects of malware behavior, characteristics, and relationships. This article evaluates the effectiveness of visualization techniques in detecting and classifying malware. We methodically categorize studies based on their approach to information retrieval, visualization, feature extraction, classification, and evaluation, allowing for an in-depth review of cutting-edge methods. This analysis identifies key challenges in visualization-based techniques and sheds light on the field's progress and future possibilities. Our thorough analysis can provide valuable insights to researchers, helping them establish optimal practices for selecting suitable visualizations based on the specific characteristics of the analyzed malware.