A NOVEL APPROACH TO INTRUSION-DETECTION SYSTEM: COMBINING LSTM AND THE SNAKE ALGORITHM
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 adv...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Scientific Research Support Fund of Jordan (SRSF) and Princess Sumaya University for Technology (PSUT)
2023-12-01
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| Series: | Jordanian Journal of Computers and Information Technology |
| Subjects: | |
| Online Access: | https://www.jjcit.org/?mno=168616 |
| _version_ | 1827624957581983744 |
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| author | sanaa Ali Jebbar Soukaena H hashem Shatha Habib Jafer |
| author_facet | sanaa Ali Jebbar Soukaena H hashem Shatha Habib Jafer |
| author_sort | sanaa Ali Jebbar |
| collection | DOAJ |
| description | 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 [JJCIT 2023; 9(4.000): 360-376] |
| first_indexed | 2024-03-09T12:20:54Z |
| format | Article |
| id | doaj.art-7b74856aed204589b4e029f63369796c |
| institution | Directory Open Access Journal |
| issn | 2413-9351 2415-1076 |
| language | English |
| last_indexed | 2024-03-09T12:20:54Z |
| publishDate | 2023-12-01 |
| publisher | Scientific Research Support Fund of Jordan (SRSF) and Princess Sumaya University for Technology (PSUT) |
| record_format | Article |
| series | Jordanian Journal of Computers and Information Technology |
| spelling | doaj.art-7b74856aed204589b4e029f63369796c2023-11-30T22:41:05ZengScientific Research Support Fund of Jordan (SRSF) and Princess Sumaya University for Technology (PSUT)Jordanian Journal of Computers and Information Technology2413-93512415-10762023-12-019436037610.5455/jjcit.71-1694088480168616A NOVEL APPROACH TO INTRUSION-DETECTION SYSTEM: COMBINING LSTM AND THE SNAKE ALGORITHMsanaa Ali Jebbar0Soukaena H hashem1Shatha Habib Jafer2lecturer prof Assistant ProfessorIn 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 [JJCIT 2023; 9(4.000): 360-376]https://www.jjcit.org/?mno=168616cyber threatsintrusion detectioncloud environmentslong short-term memory (lstm)snake algorithmintrusion detection systems (ids) |
| spellingShingle | sanaa Ali Jebbar Soukaena H hashem Shatha Habib Jafer A NOVEL APPROACH TO INTRUSION-DETECTION SYSTEM: COMBINING LSTM AND THE SNAKE ALGORITHM Jordanian Journal of Computers and Information Technology cyber threats intrusion detection cloud environments long short-term memory (lstm) snake algorithm intrusion detection systems (ids) |
| title | A NOVEL APPROACH TO INTRUSION-DETECTION SYSTEM: COMBINING LSTM AND THE SNAKE ALGORITHM |
| title_full | A NOVEL APPROACH TO INTRUSION-DETECTION SYSTEM: COMBINING LSTM AND THE SNAKE ALGORITHM |
| title_fullStr | A NOVEL APPROACH TO INTRUSION-DETECTION SYSTEM: COMBINING LSTM AND THE SNAKE ALGORITHM |
| title_full_unstemmed | A NOVEL APPROACH TO INTRUSION-DETECTION SYSTEM: COMBINING LSTM AND THE SNAKE ALGORITHM |
| title_short | A NOVEL APPROACH TO INTRUSION-DETECTION SYSTEM: COMBINING LSTM AND THE SNAKE ALGORITHM |
| title_sort | novel approach to intrusion detection system combining lstm and the snake algorithm |
| topic | cyber threats intrusion detection cloud environments long short-term memory (lstm) snake algorithm intrusion detection systems (ids) |
| url | https://www.jjcit.org/?mno=168616 |
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