In the epoch of digital transformation, cloud computing remains paramount, acting as the linchpin for a plethora of services from enterprise solutions to day-to-day consumer applications. Yet, its expansive nature has invariably rendered it susceptible to a myriad of cyber threats, necessitating advanced, adaptive defense mechanisms. This paper introduces a novel intrusion detection method tailored for cloud environments, ingeniously amalgamating the temporal pattern recognition capabilities of Long Short-Term Memory (LSTM) networks with the heuristic finesse of the Snake algorithm. Our research meticulously delineates the LSTM-Snake model’s design, implementation, and exhaustive benchmarking against prevailing approaches. Experimental results underscore the model’s prowess, registering a commendable 99% accuracy rate in intrusion detection—a marked improvement over current state-of-the-art methodologies. The ensuing discussions offer insights into the model’s practical implications, potential limitations, and avenues for future research, paving the way for a fortified cloud computing landscape
Key words: Cyber Threats, Intrusion Detection, Cloud Environments, Long Short-Term Memory (LSTM), Snake algorithm, Intrusion Detection Systems (IDS).
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