Silvia Gonzalez

As it is well known, some Intrusion Detection Systems (IDSs) suffer from high rates of false positives and negatives. A mutation technique is proposed in this study to test and evaluate the performance of a full range of classifier ensembles for Network Intrusion Detection when trying to recognize new attacks. The novel technique applies mutant operators that randomly modify the features of the captured network packets to generate situations that could not otherwise be provided to IDSs while learning. A comprehensive comparison of supervised classifiers and their ensembles is performed to assess their generalization capability. It is based on the idea of confronting brand new network attacks obtained by means of the mutation technique. Finally, an example application of the proposed testing model is specially applied to the identification of network scans and related mutations.

Cryopreservation‐enabled workflows decouple tissue acquisition from organoid generation, allowing archived biopsies and surgical explants to be revisited as renewable experimental models. This shift expands access to patient‐specific material, reduces the logistical and batch variability inherent in fresh‐tissue pipelines, and enables retrospective and longitudinal studies anchored in real‐world clinical cohorts. Proof‐of‐concept studies show that liver organoids can be derived from cryopreserved human tissues that retain viability and disease‐relevant phenotypes, but performance remains sensitive to the source material, culture lines, and protocol details. The field remains fragmented, lacking broadly adopted standard operating procedures, shared post‐thaw quality benchmarks, and interoperable data infrastructures linking organoid biobanks to clinical metadata.In this work, we argue that cryopreservation‐enabled organoid biobanking should be treated as foundational infrastructure for precision hepatology, and we outline a pragmatic roadmap for coordinated implementation over the next decade.

In this work, we introduce families of multimodal maps based on logistic map, i.e., families of m-modal maps are defined on an interval I ⊂ ℝ, which is partitioned into non-uniform subdomains, with m ∈ ℕ. Because the subdomains of the partition are not uniform, each subdomain contains a unimodal map, given by the logistic map, that can have different heights. Therefore, we give the necessary and sufficient conditions for these modal maps present a multimodal family of m-modal maps, i.e., a bifurcation parameter can set a unimodal map, a bimodal map, up to a m -modal map. Some numerical examples are given according to the developed theory. Some numerical examples are given in accordance with the developed theory.

In recent years, mobile devices such as smartphones, tablets and wearables have become the new paradigm of user-computer interaction. The increasing use and adoption of such devices is also leading to an increased number of potential security risks. The spread of mobile malware, particularly on popular and open platforms such as Android, has become a major concern. This paper focuses on the bad-intentioned Android apps by addressing the problem of selecting the key features of such software that support the characterization of such malware. The accurate detection and characterization of this software is still an open challenge, mainly due to its ever-changing nature and the open distribution channels of Android apps. Maximum relevance minimum redundancy and evolutionary algorithms guided by information correlation measures have been applied for feature selection on the well-known Android Malware Genome (Malgenome) dataset, attaining interesting results on the most informative features for the characterization of representative families of existing Android malware.

The Secure Shell Protocol (SSH) is a well-known standard protocol, mainly used for remotely accessing shell accounts on Unix-like operating systems to perform administrative tasks. As a result, the SSH service has been an appealing target for attackers, aiming to guess root passwords performing dictionary attacks or to directly exploit the service itself. To identify such situations, this article addresses the detection of SSH anomalous connections from an intrusion detection perspective. The main idea is to compare several strategies and approaches for a better detection of SSH-based attacks. To test the classification performance of different classifiers and combinations of them, SSH data coming from a real-world honeynet are gathered and analysed. For comparison purposes and to draw conclusions about data collection, both packet-based and flow data are analysed. A wide range of classifiers and ensembles are applied to these data, as well as different validation schemes for better analysis of the obtained results. The high-rate classification results lead to positive conclusions about the identification of malicious SSH connections.

En el trabajo se propone difundir la teoría y los enfoques referidos a la ciencia de la sostenibilidad, con especial énfasis en el contexto académico cubano, al relacionar su emergencia a las urgencias que surgen en los vínculos entre la ciencia, la naturaleza y la sociedad, así como mostrar las articulaciones de la Ciencia de la Sostenibilidad con la política científica, la educación universitaria y la salud pública. In this paper, the theory and the approaches of the science of sustainability is promoted. Special emphasis is put on the academic Cuban context, when relating its emergence to the urgencies that appear within the links among science, nature and society. The interconnections of the science of sustainability with the scientific policy, the university education and the public health is shown as well as