An Iterative and Toolchain-Based Approach to Automate Scanning and Mapping Computer Networks

As today's organizational computer networks are ever evolving and becoming more and more complex, finding potential vulnerabilities and conducting security audits has become a crucial element in securing these networks. The first step in auditing a network is reconnaissance by mapping it to get a comprehensive overview over its structure. The growing complexity, however, makes this task increasingly effortful, even more as mapping (instead of plain scanning), presently, still involves a lot of manual work. Therefore, the concept proposed in this paper automates the scanning and mapping of unknown and non-cooperative computer networks in order to find security weaknesses or verify access controls. It further helps to conduct audits by allowing comparing documented with actual networks and finding unauthorized network devices, as well as evaluating access control methods by conducting delta scans. It uses a novel approach of augmenting data from iteratively chained existing scanning tools with context, using genuine analytics modules to allow assessing a network's topology instead of just generating a list of scanned devices. It further contains a visualization model that provides a clear, lucid topology map and a special graph for comparative analysis. The goal is to provide maximum insight with a minimum of a priori knowledge.Comment: 7 pages, 6 figure

DeepRoute: Herding Elephant and Mice Flows with Reinforcement Learning

International audienceWide area networks are built to have enough resilience and flexibility, such as offering many paths between multiple pairs of end-hosts. To prevent congestion, current practices involve numerous tweaking of routing tables to optimize path computation, such as flow diversion to alternate paths or load balancing. However, this process is slow, costly and require difficult online decision-making to learn appropriate settings, such as flow arrival rate, workload, and current network environment. Inspired by recent advances in AI to manage resources, we present DeepRoute, a model-less reinforcement learning approach that translates the path computation problem to a learning problem. Learning from the network environment, DeepRoute learns strategies to manage arriving elephant and mice flows to improve the average path utilization in the network. Comparing to other strategies such as prioritizing certain flows and random decisions, DeepRoute is shown to improve average network path utilization to 30% and potentially reduce possible congestion across the whole network. This paper presents results in simulation and also how DeepRoute can be demonstrated by a Mininet implementation

Tracking replication enzymology in vivo by genome-wide mapping of ribonucleotide incorporation

Ribonucleotides are frequently incorporated into DNA during eukaryotic replication. Here we map the genome-wide distribution of these ribonucleotides as markers of replication enzymology in budding yeast, using a new 5′-DNA end-mapping method, Hydrolytic End Sequencing. HydEn-Seq of DNA from ribonucleotide excision repair-deficient strains reveals replicase- and strand-specific patterns of ribonucleotides in the nuclear genome. These patterns support the role of DNA polymerases α and δ in lagging strand replication and of DNA polymerase ε in leading strand replication. They identify replication origins, termination zones and variations in ribonucleotide incorporation frequency across the genome that exceed three orders of magnitude. HydEn-Seq also reveals strand-specific 5′-DNA ends at mitochondrial replication origins, suggesting unidirectional replication of a circular genome. Given the conservation of enzymes that incorporate and process ribonucleotides in DNA, HydEn-Seq can be used to track replication enzymology in other organisms

Recommendations for disclosure of artificial intelligence in scientific writing and publishing: a regional anesthesia and pain medicine modified Delphi study

IntroductionThe use of artificial intelligence (AI) in the scientific process is advancing at a remarkable speed, thanks to continued innovations in large language models. While AI provides widespread benefits, including editing for fluency and clarity, it also has drawbacks, including fabricated content, perpetuation of bias, and lack of accountability. The editorial board of Regional Anesthesia & Pain Medicine (RAPM) therefore sought to develop best practices for AI usage and disclosure. Methods A steering committee from the American Society of Regional Anesthesia and Pain Medicine used a modified Delphi process to address definitions, disclosure requirements, authorship standards, and editorial oversight for AI use in publishing. The committee reviewed existing publication guidelines and identified areas of ambiguity, which were translated into questions and distributed to an expert workgroup of authors, reviewers, editors, and AI researchers. Results Two survey rounds, with 91% and 87% response rates, were followed by focused discussion and clarification to identify consensus recommendations. The workgroup achieved consensus on recommendations to authors about definitions of AI, required items to report, disclosure locations, authorship stipulations, and AI use during manuscript preparation. The workgroup formulated recommendations to reviewers about monitoring and evaluating the responsible use of AI in the review process, including the endorsement of AI-detection software, identification of concerns about undisclosed AI use, situations where AI use may necessitate the rejection of a manuscript, and use of checklists in the review process. Finally, there was consensus about AI-driven work, including required and optional disclosures and the use of checklists for AI-associated research. Discussion Our modified Delphi study identified practical recommendations on AI use during the scientific writing and editorial process. The workgroup highlighted the need for transparency, human accountability, protection of patient confidentiality, editorial oversight, and the need for iterative updates. The proposed framework enables authors and editors to harness AI’s efficiencies while maintaining the fundamental principles of responsible scientific communication and may serve as an example for other journals

Mapping Ribonucleotides Incorporated into DNA by Hydrolytic End-Sequencing

Ribonucleotides embedded within DNA render the DNA sensitive to the formation of single-stranded breaks under alkali conditions. Here, we describe a next-generation sequencing method called hydrolytic end sequencing (HydEn-seq) to map ribonucleotides inserted into the genome of Saccharomyce cerevisiae strains deficient in ribonucleotide excision repair. We use this method to map several genomic features in wild-type and replicase variant yeast strains

Sedation and regional anaesthesia in the adult patient

This review discusses sedation for regional anaesthesia in the adult population. The first section deals with general aspects of sedation and shows that the majority of patients receiving sedation for regional anaesthesia are satisfied and would choose it again. Methods of assessing the level of sedation are discussed with emphasis on clinical measures. The pharmacology of the drugs involved in sedation is discussed, with propofol and remifentanil appearing to be the combination of choice for sedation in regional anaesthesia. The techniques for administering sedation are discussed and replacement of the traditional repeated boluses or continuous infusion with pharmacokinetic and patient-controlled systems is supported. Patient satisfaction studies suggest that patient-controlled systems are preferred

A modular traffic sampling architecture: bringing versatility and efficiency to massive traffic analysis

The massive traffic volumes and heterogeneity of services in today's networks urge for flexible, yet simple measurement solutions to assist network management tasks, without impairing network performance. To turn treatable tasks requiring traffic analysis, sampling the traffic has become mandatory, triggering substantial research in the area. Despite that, there is still a lack of an encompassing solution able to support the flexible deployment of sampling techniques in production networks, adequate to diverse traffic scenarios and measurement activities. In this context, this article proposes a modular traffic sampling architecture able to foster the flexible design and deployment of efficient measurement strategies. The architecture is composed of three layers-management plane, control plane and data plane-covering key components to achieve versatile and lightweight measurements in diverse traffic scenarios and measurement activities. Each component of the architecture is described considering the different strategies, technologies and protocols that compose the several stages of a measurement process. Following the proposed architecture, a sampling framework prototype has been developed, providing a fair environment to assess and compare sampling techniques under distinct measurement scenarios, evaluating their performance in balancing computational burden and accuracy. The results have demonstrated the relevance and applicability of the proposed architecture, revealing that a modular and configurable approach to sampling is a step forward for improving sampling scope and efficiency.This work has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT Fundacao para a Ciencia e Tecnologia within the Project Scope: UID/CEC/00319/2013.info:eu-repo/semantics/publishedVersio