摘要
The extensive proliferation of modern information services and ubiquitous digitization of society have raised cybersecurity challenges to new levels.With the massive number of connected devices,opportunities for potential network attacks are nearly unlimited.An additional problem is that many low-cost devices are not equippedwith effective security protection so that they are easily hacked and applied within a network of bots(botnet)to perform distributed denial of service(DDoS)attacks.In this paper,we propose a novel intrusion detection system(IDS)based on deep learning that aims to identify suspicious behavior in modern heterogeneous information systems.The proposed approach is based on a deep recurrent autoencoder that learns time series of normal network behavior and detects notable network anomalies.An additional feature of the proposed IDS is that it is trained with an optimized dataset,where the number of features is reduced by 94%without classification accuracy loss.Thus,the proposed IDS remains stable in response to slight system perturbations,which do not represent network anomalies.The proposed approach is evaluated under different simulation scenarios and provides a 99%detection accuracy over known datasets while reducing the training time by an order of magnitude.
基金
This work was supported by the Slovak Research and Development Agency,project number APVV-18-0214
by the Scientific Grant Agency of the Ministry of Education,science,research and sport of the Slovak Republic under the contract:1/0268/19
by the Ukrainian government projects No.0120U102201“Development the methods and unified software-hardware means for the deployment of the energy efficient intent-based multi-purpose information and communication networks,”and No.0120U100674,“Designing the novel decentralized mobile network based on blockchain architecture and artificial intelligence for 5G/6G development in Ukraine.”。
