End-to-End Active Learning for Computer Security Experts

International audienceLabelling a dataset for supervised learning is particularly expensive in computer security as expert knowledge is required for annotation. Some research works rely on active learning to reduce the labelling cost, but they often assimilate annotators to mere oracles providing ground-truth labels. Most of them completely overlook the user experience while active learning is an interactive procedure. In this paper, we introduce an end-to-end active learning system, ILAB, tailored to the needs of computer security experts. We have designed the active learning strategy and the user interface jointly to effectively reduce the annotation effort. Our user experiments show that ILAB is an efficient active learning system that computer security experts can deploy in real-world annotation projects

End-to-End Active Learning for Computer Security Experts

International audienceSupervised detection models can be deployed in computer security detection systems to strengthen detection. However, acquiring a training dataset is particularly expensive in this context since expert knowledge is required to annotate. Some research works rely on active learning to reduce human effort, but they often assimilate annotators to mere oracles providing ground-truth labels. Most of them completely overlook the user experience while active learning is an interactive procedure. In this paper, we introduce an end-to-end active learning system, ILAB, tailored to computer security experts needs. We have designed the active learning strategy and the user interface jointly to effectively reduce annotation effort. Our user experiments show that ILAB is an efficient active learning system that computer security experts can deploy in real-world annotation projects

Le Machine Learning confronté aux contraintes opérationnelles des systèmes de détection

International audienceLes systèmes de détection d’intrusion, reposant traditionnellementsur des signatures, n’ont pas échappé à l’attrait récent des techniquesde Machine Learning. Si les résultats présentés dans les articles de rechercheacadémique sont souvent excellents, les experts en sécurité ontcependant encore de nombreuses réticences concernant l’utilisation duMachine Learning dans les systèmes de détection d’intrusion. Ils redoutentgénéralement une inadéquation de ces techniques aux contraintes opérationnelles,notamment à cause d’un niveau d’expertise requis important,ou d’un grand nombre de faux positifs.Dans cet article, nous montrons que le Machine Learning peut êtrecompatible avec les contraintes opérationnelles des systèmes de détection.Nous expliquons comment construire un modèle de détection et présentonsde bonnes pratiques pour le valider avant sa mise en production. Laméthodologie est illustrée par un cas d’étude sur la détection de fichiersPDF malveillants et nous proposons un outil libre, SecuML, pour lamettre en oeuvre

Emerging opportunities and challenges for passive acoustics in ecological assessment and monitoring

1. High-throughput environmental sensing technologies are increasingly central to global monitoring of the ecological impacts of human activities. In particular, the recent boom in passive acoustic sensors has provided efficient, noninvasive, and taxonomically broad means to study wildlife populations and communities, and monitor their responses to environmental change. However, until recently, tech-nological costs and constraints have largely confined research in passive acoustic monitoring (PAM) to a handful of taxonomic groups (e.g., bats, cetaceans, birds), often in relatively small-scale, proof-of-concept studies.2. The arrival of low-cost, open-source sensors is now rapidly expanding access to PAM technologies, making it vital to evaluate where these tools can contribute to broader efforts in ecology and biodiversity research. Here, we synthesise and critically assess the current emerging opportunities and challenges for PAM for ecological assessment and monitoring of both species populations and communities.3. We show that terrestrial and marine PAM applications are advancing rapidly, fa-cilitated by emerging sensor hardware, the application of machine learning inno-vations to automated wildlife call identification, and work towards developing acoustic biodiversity indicators. However, the broader scope of PAM research remains constrained by limited availability of reference sound libraries and open-source audio processing tools, especially for the tropics, and lack of clarity around the accuracy, transferability and limitations of many analytical methods.4. In order to improve possibilities for PAM globally, we emphasise the need for col-laborative work to develop standardised survey and analysis protocols, publicly archived sound libraries, multiyear audio datasets, and a more robust theoretical and analytical framework for monitoring vocalising animal communities