Artificial intelligent based intrusion detection system for safe cloud information retention

Abstract

Cloud technology's rapid growth has produced significant benefits for many sectors, including improved data retention expandability and adaptability. However, concerns about information security and the possibility of unwanted attacks and unauthorized access have been raised due to the growing amount of sensitive data stored in the cloud. This study suggests a novel artificial intelligence (AI)-assisted intrusion detection system (IDS) that is intended to guarantee the secure storage of data in cloud environments to ensure their concerns. In this paper, we present a unique hybrid cockroach swarm-intelligent integrated boosted random forest (HCSI-BRF) approach for efficient IDS. In this work, the CICIDS2017 dataset is first collected to assess the suggested HCSI-BRF approach. The effectiveness of the HCSI-BRF based architecture was verified utilizing thorough testing and assessment on the Python platform, proving its capacity to detect and address possible security risks in cloud environments. Our method achieves superior of precision (0.97), recall (0.94), accuracy (0.96), and F1 scores (0.95), in IDS compared to the current approaches. The outcomes demonstrate the system's resilience and dependability, establishing it as a viable option for guaranteeing safe and dependable data storage in cloud computing systems. This study addresses the growing concerns about the confidentiality and safety of data in the digital era by advancing the creation of robust and proactive security solutions for data storage in the cloud.