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@article{198300,
        author = {Antony lishma.S and Deepa Dharshini.M and Mercy.J and Sumithra.E and Yuvarani.R and Mr.M.SYED MOHAMED ALI, M.E.,},
        title = {INTELLIGENT CYBERATTACK DETECTION SYSTEM USING HYBRID DEEP LEARNING (LSTM)},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {13481-13486},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=198300},
        abstract = {Cyber security is critical for protecting sensitive financial and organizational data from increasingly sophisticated cyber threats. However, traditional intrusion detection systems often struggle to detect zero-day attacks and complex multi-stage intrusions, creating significant vulnerabilities in banking and enterprise environments. While conventional machine learning models can classify known attack patterns such as bruteforce, phishing, and Distributed Denial-of-Service (DDoS) attacks, they lack the ability to effectively identify novel or evolving threats. Existing security solutions are often limited by high false positive rates, poor adaptability to new attack behaviors, and insufficient realtime analysis capabilities, making them unsuitable for modern dynamic network infrastructures.},
        keywords = {},
        month = {April},
        }