Authors :
R Padmashani; Harshan R.; Logeshwaran C.; Srikrishna R.; Vijay Sundar
Volume/Issue :
Volume 9 - 2024, Issue 6 - June
Google Scholar :
https://tinyurl.com/36xtn576
Scribd :
https://tinyurl.com/k6kbjjh8
DOI :
https://doi.org/10.38124/ijisrt/IJISRT24JUN030
Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.
Abstract :
In the quickly changing digital world of today,
protecting oneself from cyberattacks is crucial. This study
presents a novel method that uses TensorFlow Federated
(TFF) Learning to merge BiLSTM and DNN architectures,
improving the precision and effectiveness of intrusion
detection systems (IDS). TFF offers a major paradigm
change in model training by enabling decentralized
learning on several servers or devices. TFF provides IDS
with collective intelligence by fostering collaborative
learning on remote data sources while protecting data
privacy. This improves detection accuracy and strengthens
defenses against adversarial attacks. By utilizing
TensorFlow Federated methods, IDS may run DNN and
BiLSTM models concurrently, maximizing processing
speed and resource efficiency. The system's capacity to
manage high-throughput data streams is ensured by this
concurrent execution, which speeds up threat detection and
response. Moreover, information sharing and smooth
integration between concurrent processes are made possible
via synchronization and communication protocols. The
cooperative synergy between the various models improves
IDS's dependability and efficacy in thwarting emerging
cyberthreats.
References :
- Muhammad Ashfaq khan,” HCRNNIDS: Hybrid Convolutional Recurrent Neural Network-Based Network Intrusion Detection System”,pp 6-8
- Zhang, Chen, et al. "A survey on federated learning." Knowledge-Based Systems 216 (2021): 106775.
- Javed Asharf ,Nour Moustafa , Hasnat Khurshid ,Essam Debie ,Waqas Haider ,Abdul Wahab,"A Review of Intrusion Detection Systems Using Machine and Deep Learning in Internet of Things: Challenges, Solutions and Future Directions",pp 12-26
- Conguyan Xu, Jizhong Shen ,Xin Du, Fan Zhang,”An Intrusion Detection System Using a Deep Neural Network With Gated Recurrent Units”,pp 4-9
- Li, Li, et al. "A review of applications in federated learning." Computers & Industrial Engineering 149 (2020): 106854.
- Mammen, Priyanka Mary. "Federated learning: Opportunities and challenges." arXiv preprint arXiv:2101.05428 (2021).
- AL-barakati, Niraj Thapa, Saigo Hiroto , Kaushik Roy , Robert H. Newman , Dukka KC, “RF-MaloSite and DL-Malosite: Methods based on random forest and deep learning to identify malonylation sites”, pp 8-20
- Ali Shiravi, Hadi Shiravi, Mahbod Tavallaee, Ali A. Ghorbani,”Toward developing a systematic approach to generate benchmark datasets for intrusion detection”, pp 2-14
- Lei Wang, Latifur Khan and Bhavani Thuraisingham,”An Effective Evidence Theory based K-nearest Neighbor (KNN) classification”,pp 3-12
- Rieke, Nicola, et al. "The future of digital health with federated learning." NPJ digital medicine 3.1(2020): 17.
- Hamed Alqahtani, Iqbal H. Sarker,Asra Kalim,Syed Mohammod ,Minhaz Hossain,”Cyber Intrusion Detection Using Machine Learning Classification Techniques”,pp 4-10
- Jinwon An, Sungzoon Cho, “Variational Autoencoder based Anomaly Detection using Reconstruction Probability”,pp 5
- Li, Qinbin, Bingsheng He, and Dawn Song. "Model-contrastive federated learning." Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2021
In the quickly changing digital world of today,
protecting oneself from cyberattacks is crucial. This study
presents a novel method that uses TensorFlow Federated
(TFF) Learning to merge BiLSTM and DNN architectures,
improving the precision and effectiveness of intrusion
detection systems (IDS). TFF offers a major paradigm
change in model training by enabling decentralized
learning on several servers or devices. TFF provides IDS
with collective intelligence by fostering collaborative
learning on remote data sources while protecting data
privacy. This improves detection accuracy and strengthens
defenses against adversarial attacks. By utilizing
TensorFlow Federated methods, IDS may run DNN and
BiLSTM models concurrently, maximizing processing
speed and resource efficiency. The system's capacity to
manage high-throughput data streams is ensured by this
concurrent execution, which speeds up threat detection and
response. Moreover, information sharing and smooth
integration between concurrent processes are made possible
via synchronization and communication protocols. The
cooperative synergy between the various models improves
IDS's dependability and efficacy in thwarting emerging
cyberthreats.