A Multi-agent System-Based Distributed Intrusion Detection System for a Cloud Computing

Achbarou, Omar; Kiram, My Ahmed El; Bourkoukou, Outmane; Elbouanani, Salim

doi:10.1007/978-3-030-02852-7_9

Cited by 4 publications

(2 citation statements)

References 19 publications

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“…Application Layer: This layer may provide global management of the application based on the information processed by the IoT devices in the bottom layer (middleware layer). According to the ITU-T Y.2060 recommendation, the application layer contains the authorization, authentication, application data confidentiality, integrity and privacy protection, and the security audit [17]. The application layer provides services as per user demand and the processed information that comes from the lower layers is used to produce useful services for the customers [18].…”

Section: Iot Architecture Systemmentioning

confidence: 99%

BIM in People2People and Things2People Interactive Process

Mataloto

Ferreira

Resende

et al. 2020

Sensors

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In this research work, we present an IoT solution to environment variables using a LoRa transmission technology to give real-time information to users in a Things2People process and achieve savings by promoting behavior changes in a People2People process. These data are stored and later processed to identify patterns and integrate with visualization tools, which allow us to develop an environmental perception while using the system. In this project, we implemented a different approach based on the development of a 3D visualization tool that presents the system collected data, warnings, and other users’ perception in an interactive 3D model of the building. This data representation introduces a new People2People interaction approach to achieve savings in shared spaces like public buildings by combining sensor data with the users’ individual and collective perception. This approach was validated at the ISCTE-IUL University Campus, where this 3D IoT data representation was presented in mobile devices, and from this, influenced user behavior toward meeting campus sustainability goals.

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Section: Iot Architecture Systemmentioning

confidence: 99%

BIM in People2People and Things2People Interactive Process

Mataloto

Ferreira

Resende

et al. 2020

Sensors

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“…The cloud environment is divided into three layers: infrastructure as a service layer (IaaS), software as a service (SaaS)and platform as a service layer (PaaS) [2]. The main function of IaaS layer is to provide the basis for computing or resources.…”

Section: Introductionmentioning

confidence: 99%

A distributed intrusion detection system based on CNN-GRU in cloud environment

Sun

Zhao

et al. 2022

International Conference on Artificial Intelligence and Intelligent Information Processing (AIIIP 2022)

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In the era of big data, the network is always full of massive data. Cloud computing provides huge technical support for processing massive data. The cloud environment stores a large number of important information of individuals, enterprises and even countries. It has high commercial value, so it has become the primary target of many network attacks. Therefore, it is necessary to monitor the traffic in the cloud environment in real time and block abnormal traffic in time to ensure a safe and stable network environment for users. The existing intrusion detection systems can be divided into software systems and hardware systems, which are deployed in the backbone network in the network environment for real-time traffic detection. It is difficult to meet the traffic detection of multi branches and massive data in the cloud environment. This paper combines the deep neural network model with Hadoop framework, and proposes a distributed intrusion detection system model based on CNN-GRU. The deep neural network model is deployed in multiple nodes in the cloud environment, and the data is stored through HDFS and mapped and integrated by MapReduce method, so as to realize the intrusion detection of multi node parallel cloud environment. Finally, through the open source intrusion detection data set, the experimental results prove the effectiveness of the proposed method.

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Enhancing Data Security in the User Layer of Mobile Cloud Computing Environment: A Novel Approach

Ogwara

Petrova

Yang

et al. 2021

Advances in Security, Networks, and Internet of Things

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This paper reviews existing Intrusion Detection Systems (IDS) that target the Mobile Cloud Computing (MCC), Cloud Computing (CC), and Mobile Device (MD) environment. The review identifies the drawbacks in existing solutions and proposes a novel approach towards enhancing the security of the User Layer (UL) in the MCC environment. The approach named MINDPRES (Mobile-Cloud Intrusion Detection and Prevention System) combines a host-based IDS and network-based IDS using Machine Learning (ML) techniques. It applies dynamic analysis of both device resources and network traffic in order to detect malicious activities at the UL in the MCCenvironment. Preliminary investigations show that our approach will enhance the security of the UL in the MCC environment. Our future work will include the development and the evaluation of the proposed model across the various mobile platforms in the MCC environment.

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A Multi-agent System-Based Distributed Intrusion Detection System for a Cloud Computing

Cited by 4 publications

References 19 publications

BIM in People2People and Things2People Interactive Process

BIM in People2People and Things2People Interactive Process

A distributed intrusion detection system based on CNN-GRU in cloud environment

Enhancing Data Security in the User Layer of Mobile Cloud Computing Environment: A Novel Approach

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