A Secure and Robust Framework to Mitigate Active Attacks for Cloud in Collaborative Environment

The IDSs’ capacity to identify assaults or cyberattacks is hampered by their singular point of view. This makes it necessary for several IDS nodes to exchange attack characteristics. For the purpose of efficiently sharing these assault characteristics, researchers devised a cooperative intrusion detection system. Although this method was helpful, there is no guarantee that the shared data will be secure. More precisely, there is growing worry over preserving the consistency and integrity of shared data. We present a blockchain-based Framework that guarantees the consistency and integrity of shared attack features in proposed system. This is accomplished by the suggested architecture by identifying and stopping compromised IDS nodes and false feature injection. It also guarantees the involvement of heterogeneous IDS nodes, makes it easier for IDS nodes to communicate scalable attack features, and is resilient to new and exiting public IDS nodes from the network. We assess the latency and security assessments. The outcome demonstrates that the suggested method is scalable, and it can identify and stop malicious feature injection, manipulation, and deletion in addition to compromised IDS nodes.