An effective network security scrutinising method based on deep learning

The field of network security is constantly evolving. Future dangers are difficult to predict and even more challenging to prepare for. In order to effectively confront future network security concerns, this article discusses efforts made to construct a vital support capability for an autonomous network security testing system. The purpose of this system is to simulate future network attacks on vital infrastructure in order to better protect against them. A novel attack paradigm is proposed, one that allows for more awareness and control inside a network of compromised nodes. The suggested attack framework has low memory and network requirements while still allowing for the retrieval and execution of arbitrary attacks. This framework makes it easier to conduct rapid, autonomous penetration tests and assess the state of detection systems and procedures ahead of time for autonomous network-attacks.