English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Current frameworks for training offensive penetration testing agents withdeep reinforcement learning struggle to produce agents that perform well inreal-world scenarios, due to the reality gap in simulation-based frameworks andthe lack of scalability in emulation-based frameworks. Additionally, existingframeworks often use an unrealistic metric that measures the agents'performance on the training data. NASimEmu, a new framework introduced in thispaper, addresses these issues by providing both a simulator and an emulatorwith a shared interface. This approach allows agents to be trained insimulation and deployed in the emulator, thus verifying the realism of the usedabstraction. Our framework promotes the development of general agents that cantransfer to novel scenarios unseen during their training. For the simulationpart, we adopt an existing simulator NASim and enhance its realism. Theemulator is implemented with industry-level tools, such as Vagrant, VirtualBox,and Metasploit. Experiments demonstrate that a simulation-trained agent can bedeployed in emulation, and we show how to use the framework to train a generalagent that transfers into novel, structurally different scenarios. NASimEmu isavailable as open-source.

NASimEmu: Network Attack Simulator & Emulator for Training Agents Generalizing to Novel Scenarios