A NOVEL APPROACH TO INTRUSION-DETECTION SYSTEM: COMBINING LSTM AND THE SNAKEALGORITHM

(Received: 7-Sep.-2023, Revised: 29-Oct.-2023 , Accepted: 11-Nov.-2023)

Authors Sanaa Ali Jabber, Soukaena H . H ashem, Shatha H. Jafer,

Keywords #Cyber threats #Intrusion detection #Cloud environments #Long short-term memory (LSTM) #Snake algorithm #Intrusion detection systems (IDSs)

Abstract 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 for a rigorous and comprehensive evaluation of cloud-based intrusion-detection systems and by using the TON-IOT dataset, a carefully curated dataset tailored for cloud-centric applications. The 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 and potential limitations.

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