Federated Learning (FL) has emerged as a promising approach for collaborative medical image analysis while preserving data privacy, making it particularly suitable for radiomics tasks. This paper presents a systematic meta-analysis of recent surveys on Federated Learning in Medical Imaging (FL-MI), published in reputable venues over the past five years. We adopt the PRISMA methodology, categorizing and analyzing the existing body of research in FL-MI. Our analysis identifies common trends, challenges, and emerging strategies for implementing FL in medical imaging, including handling data heterogeneity, privacy concerns, and model performance in non-IID settings. The paper also highlights the most widely used datasets and a comparison of adopted machine learning models. Moreover, we examine FL frameworks in FL-MI applications, such as tumor detection, organ segmentation, and disease classification. We identify several research gaps, including the need for more robust privacy protection. Our findings provide a comprehensive overview of the current state of FL-MI and offer valuable directions for future research and development in this rapidly evolving field.

The task of identifying malicious activities in logs and predicting threats is crucial nowadays in industrial sector. In this paper, we focus on the identification of past malicious activities and in the prediction of future threats by proposing a novel technique based on the combination of Marked Temporal Point Processes (MTTP) and Neural Networks. Differently from the traditional formulation of Temporal Point Processes, our method does not make any prior assumptions on the functional form of the conditional intensity function and on the distribution of the events. Our approach is based the adoption of Neural Networks with the goal of improving the capabilities of learning arbitrary and unknown event distributions by taking advantage of the Deep Learning theory. We conduct a series of experiments using industrial data coming from gas pipelines, showing that our framework is able to represent in a convenient way the information gathered from the logs and predict future menaces in an unsupervised way, as well as classifying the past ones. The results of the experimental evaluation, showing outstanding values for precision and recall, confirm the effectiveness of our approach.

Binary obfuscation techniques are commonly employed to protect code against reverse engineering and piracy. Unfortunately, besides being used for legitimate purposes, virus writers also resort to obfuscation to evade antivirus detection mechanisms based on signature scanning. Consequently, the detection of obfuscated code in executables may be a precious resource to prevent the execution of malicious programs. Detecting obfuscation is a task fraught with difficulties owing to the wide range of possible obfuscation transformations and the indistinguishability of obfuscated code. In this paper, we venture into the not-so-explored world of obfuscation detection, gaining a deeper comprehension of what happens - from a statistical perspective - to a binary program when obfuscation transformations are applied to it. We accomplish this goal by leveraging eXplainable Artificial Intelligence, which allows us to discern the altered features from the invariant ones, which in turn can then be used for obfuscation-resilient malware detection. The present study has been carried out utilizing diverse datasets, not only to examine the detection of obfuscation but also to classify the specific obfuscating transformations employed. The investigation encompasses binaries compiled for various architectures, and we propose an effective methodology for identifying both the existence of obfuscation and isolating invariant patterns that can facilitate the creation of obfuscation-resistant signatures for antivirus detection.