Incremental Database Based on Distributed Ledger Technology for IDSs
Junwei Liang, Maode Ma · 2020
Intrusion Detection Systems (IDS) is an important technology for cyber security, as it can mitigate both inner and outer threats in networks. However, a critical problem in IDSs is that the detection capacity is gradually decaying with the emergence of unknown attacks. To constantly retrain IDSs with a more extensive database is critical to make IDSs adaptive with the ever-changing network environment, but the security institutes usually lack the motivation to persistently update and maintain the database for public. Thus, in this paper, a blockchain-based database (bc-DB) is proposed, which is multilaterally maintained by the security institutes and universities using Data Coins (DCoins) as the incentives. In addition, a Lifetime Learning IDS (LL-IDS) is further designed as the supplement of the bc-DB for common IDS users. After being retrained by the latest bc-DB, the LL-IDS can detect the newly discovered attacks while uploading the suspect network packets to the database. Simulation experiments show that the proposed LL-IDS with the bc-DB are secure and effectiveness in attacks detection.