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@article{204954,
        author = {Supriya.S.R and Mrs.Usha M G and Ankitha.H.S and Bhoomi.N and Sumedha.G.M},
        title = {Multi Cloud Threat/Anomaly detection model},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {1},
        pages = {4936-4944},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=204954},
        abstract = {Multi-cloud environments face increasing security threats and anomaly attacks due to their distributed and heterogeneous nature. Traditional intrusion detection systems suffer from high false alarm rates and significant computational overhead when processing large-scale network traffic. This paper proposes a swarm intelligence-based anomaly detection framework using Particle Swarm Optimization (PSO) to enhance threat identification. The system is evaluated using the NS2 simulator to model multi-cloud network traffic and routing behaviors. Experimental results show improved detection accuracy and reduced processing delay compared to standard detection mechanisms. The proposed approach demonstrates a robust capability for securing inter-cloud communications against evolving network threats.},
        keywords = {Multi-cloud security, Anomaly detection, Swarm intelligence, Intrusion detection system, PSO, NS2 simulator.},
        month = {June},
        }