Deep Neural networks are efficient and flexible models that perform well for a variety of tasks such as image, speech recognition and natural language understanding. In particular, convolutional neural networks (CNN) generate a keen interest among researchers in computer vision and more specifically in classification tasks. CNN architecture and related hyperparameters are generally correlated to the nature of the processed task as the network extracts complex and relevant characteristics allowing the optimal convergence. Designing such architectures requires significant human expertise, substantial computation time and doesnt always lead to the optimal network. Model configuration topic has been extensively studied in machine learning without leading to a standard automatic method. This survey focuses on reviewing and discussing the current progress in automating CNN architecture search.
The advent of massive and highly heterogeneous information systems poses major challenges to professionals responsible for IT security. The huge amount of monitoring data currently being generated means that no human being or group of human beings can cope with their analysis. Furthermore, fully automated tools still lack the ability to track the associated events in a fine-grained and reliable way. Here, we propose the HuMa framework for detailed and reliable analysis of large amounts of data for security purposes. HuMa uses a multianalysis approach to study complex security events in a large set of logs. It is organized around three layers: the event layer, the context and attack pattern layer, and the assessment layer. We describe the framework components and the set of complementary algorithms for security assessment. We also provide an evaluation of the contribution of the context and attack pattern layer to security investigation. This work was partially supported by the French Banque Publique d'Investissement (BPI) under program FUI-AAP-19 in the frame of the HuMa project.
Behind firewalls, more and more cybersecurity attacks are specifically targeted to the very network where they are taking place. This review proposes a comprehensive framework for addressing the challenge of characterising novel complex threats and relevant countermeasures. Two kinds of attacks are particularly representative of this issue: zero-day attacks that are not publicly disclosed and multi-step attacks that are built of several individual steps, some malicious and some benign. Two main approaches are developed in the artificial intelligence field to track these attacks: statistics and machine learning. Statistical approaches include rule-based and outlier-detection-based solutions. Machine learning includes the detection of behavioural anomalies and event sequence tracking. Applications of artificial intelligence cover the field of intrusion detection, which is typically performed online, and security investigation, performed offline.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.