0% Complete
Home
/
14th International Conference on Computer and Knowledge Engineering
Enhancing Cloud Security with Federated CNN-LSTM: A Novel Approach to Intrusion Detection
Authors :
Reyhaneh Ilaghi
1
Raheleh Ilaghi
2
Fereshteh Rahmani
3
Seyyed hamid Ghafoori
4
1- Department of Electrical Engineering Kerman Branch, Islamic Azad University Kerman, Iran
2- Department of Electrical Engineering Kerman Branch, Islamic Azad University Kerman, Iran
3- University of Science and Arts of Yazd
4- Department of Electrical Engineering Kerman Branch, Islamic Azad University Kerman, Iran
Keywords :
intrusion detection،cloud computing،Neural Networks،Distributed Learning،Federated Learning
Abstract :
Intrusion detection in cloud environments faces challenges from scalable architecture, virtualized resources, and high data throughput. As cyber threats evolve, particularly within cloud infrastructures, there is a critical need for advanced, adaptable security mechanisms. Vulnerable to a wide range of attacks due to their scalable, decentralized nature and shared resources, cloud systems require robust security solutions. This paper introduces an innovative intrusion detection framework using distributed collaborative learning via federated learning, tailored for the unique security challenges of cloud environments. It enables nodes to independently train and update intrusion detection models without central data aggregation, enhancing privacy and collaborative learning while boosting attack detection capabilities across the network. Employing a convolutional neural network (CNN) within this federated structure, the proposed method significantly enhances privacy, security, detection accuracy, and system scalability. By utilizing distributed intelligence, this group learning approach surpasses traditional centralized learning, achieving a balance between local knowledge and comprehensive detection efficacy.
Papers List
List of archived papers
A Cloud Broker with Gap Analysis Perspective for Scheduling Multi-Workflows Across On-Demand and Reserved Resources
Negin Shafinezhad - Hamidreza Abrishami - Saeid Abrishami
MIPS-Core Application Specific Instruction-Set Processor for IDEA Cryptography − Comparison between Single-Cycle and Multi-Cycle Architectures
Ahmad Ahmadi - Reza Faghih Mirzaee
Evaluation of Efficient Electrocardiomatrix-based Identification Using Deep Learning Methods
Amirhossein Safari - Narges Mokhtari - Mohsen Hooshmand - Sadegh Sadeghi - Peyman Pahlevani
A Deep CNN Model Based Ensemble Approach for Semantic and Instance Segmentation of Indoor Environment
Sajad Rezaei - Jafar Tanha - Zahra Jafari - SeyedEhsan Roshan - Mohammad-Amin Memar Kochebagh
Segmentation of Coronary Artery Stenosis in X-ray Angiography using Mamba Models
Fatemeh Fouladi - Ali Rostami - Hedieh Sajedi
GAP: Fault tolerance Improvement of Convolutional Neural Networks through GAN-aided Pruning
Pouya Hosseinzadeh - Yasser Sedaghat - Ahad Harati
Disturbance Rejection in Quadruple-Tank System by Proposing New Method in Reinforcement Learning
Alireza Nezamzadeh - Mohammadreza Esmaeilidehkordi
Attention-Boosted Ensemble of Pre-trained Convolutional Neural Networks for Accurate Diabetic Retinopathy Detection
Benyamin Mirab Golkhatmi - Mohammad Hossein Moattar
A Smart Electrochemical Biosensor for Arsenic Detection in Water
Keyvan Asefpour Vakilian
Enhancing Persian Word Sense Disambiguation with Large Language Models: Techniques and Applications
Fatemeh Zahra Arshia - Saeedeh Sadat Sadidpour
more
Samin Hamayesh - Version 41.5.3