An Artificial Immune System for Misbehavior Detection in Mobile Ad-Hoc Networks with Virtual Thymus, Clustering, Danger Signal and Memory Detectors

In mobile ad-hoc networks, nodes act both as terminals and information relays, and participate in a common routing protocol, such as Dynamic Source Routing (DSR). The network is vulnerable to routing misbehavior, due to faulty or malicious nodes. Misbehavior detection systems aim at removing this vulnerability. For this purpose, we use an Artificial Immune System (AIS), a system inspired by the human immune system (HIS). Our goal is to build a system that, like its natural counterpart, automatically learns and detects new misbehavior. In this paper we build on our previous work and investigate the use of four concepts: (1

Global research priorities related to the World Health Organization Labour Care Guide: results of a global consultation

Background The World Health Organization (WHO) published the WHO Labour Care Guide (LCG) in 2020 to support the implementation of its 2018 recommendations on intrapartum care. The WHO LCG promotes evidence-based labour monitoring and stimulates shared decision-making between maternity care providers and labouring women. There is a need to identify critical questions that will contribute to defining the research agenda relating to implementation of the WHO LCG. Methods This mixed-methods prioritization exercise, adapted from the Child Health and Nutrition Research Initiative (CHNRI) and James Lind Alliance (JLA) methods, combined a metrics-based design with a qualitative, consensusbuilding consultation in three phases. The exercise followed the reporting guideline for priority setting of health research (REPRISE). First, 30 stakeholders were invited to submit online ideas or questions (generation of research ideas). Then, 220 stakeholders were invited to score "research avenues" (i.e., broad research ideas that could be answered through a set of research questions) against six independent and equally weighted criteria (scoring of research avenues). Finally, a technical working group (TWG) of 20 purposively selected stakeholders reviewed the scoring, and refined and ranked the research avenues (consensus-building meeting). Results Initially, 24 stakeholders submitted 89 research ideas or questions. A list of 10 consolidated research avenues was scored by 75/220 stakeholders. During the virtual consensus-building meeting, research avenues were refined, and the top three priorities agreed upon were: (1) optimize implementation strategies of WHO LCG, (2) improve understanding of the effect of WHO LCG on maternal and perinatal outcomes, and the process and experience of labour and childbirth care, and (3) assess the effect of the WHO LCG in special situations or settings. Research avenues related to the organization of care and resource utilization ranked lowest during both the scoring and consensusbuilding process. Conclusion This systematic and transparent process should encourage researchers, program implementers, and funders to support research aligned with the identified priorities related to WHO LCG. An international collaborative platform is recommended to implement prioritized research by using harmonized research tools, establishing a repository of research priorities studies, and scaling-up successful research results

M of N Features vs. Intrusion Detection

In order to complement the incomplete training audit trails, model generalization is always utilized to infer more unknown knowledge for intrusion detection. Thus, it is important to evaluate model generalization with respect to the detection performance of intrusion detection

Community Epidemic Detection using Time-Correlated Anomalies

Abstract. An epidemic is malicious code running on a subset of a community, a homogeneous set of instances of an application. Syzygy is an epidemic detection framework that looks for time-correlated anomalies, i.e., divergence from a model of dynamic behavior. We show mathematically and experimentally that, by leveraging the statistical properties of a large community, Syzygy is able to detect epidemics even under adverse conditions, such as when an exploit employs both mimicry and polymorphism. This work provides a mathematical basis for Syzygy, describes our particular implementation, and tests the approach with a variety of exploits and on commodity server and desktop applications to demonstrate its effectiveness