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Research Article

Enhanced Cybersecurity via Deep LSTM Networks for Intrusion Detection

Sohaib Ansari1 Rajat Kamble2 Shishir Mishra3 Abhishek Piperde4
123 Students, Department of Computer Science Engineering JIT, Nagpur, Maharashtra, India. 4Assistant.Professor, Department of Computer Science Engineering JIT, Nagpur, Maharashtra, India.

Published Online: September-December 2024

Pages: 17-19

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References

1. Malik, N.; Sardaraz, M.; Tahir, M.; Shah, B.; Ali, G.; Moreira, F. Energy-efficient load balancing algorithm for workflow scheduling in cloud data centers using queuing and thresholds. Appl. Sci. 2021, 11, 5849.
2. Baiyere, A.; Topi, H.; Venkatesh, V.; Wyatt, J.; Design, R.; Donnellan, B. Communications of the Association for Information Systems Internet of Things (IoT)—A Research Agenda for Information Systems.
3. Lone, A.N.; Mustajab, S.; Alam, M. A comprehensive study on cybersecurity challenges and opportunities in the IoT world. Secur. Priv. 2023, 6, e318
4. Dahou, A.; Abd Elaziz, M.; Chelloug, S.A.; Awadallah, M.A.; Al-Betar, M.A.; Al-Qaness, M.A.; Forestiero, A. Intrusion Detection System
for IoT Based on Deep Learning and Modified Reptile Search Algorithm. Comput. Intell. Neurosci. 2022, 2022, 6473507.
5. Hochreiter, S.; Schmidhuber, J. Long Short-Term Memory. Neural Comput. 1997, 9, 1735–1780.

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