| 1. |
- Eziama, E., et al.
(författare)
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Machine learning-based recommendation trust model for machine-to-machine communication
- 2018
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Ingår i: 2018 IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2018. - : Institute of Electrical and Electronics Engineers Inc.. - 9781538675687
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Konferensbidrag (refereegranskat)abstract
- The Machine Type Communication Devices (MTCDs) are usually based on Internet Protocol (IP), which can cause billions of connected objects to be part of the Internet. The enormous amount of data coming from these devices are quite heterogeneous in nature, which can lead to security issues, such as injection attacks, ballot stuffing, and bad mouthing. Consequently, this work considers machine learning trust evaluation as an effective and accurate option for solving the issues associate with security threats. In this paper, a comparative analysis is carried out with five different machine learning approaches: Naive Bayes (NB), Decision Tree (DT), Linear and Radial Support Vector Machine (SVM), KNearest Neighbor (KNN), and Random Forest (RF). As a critical element of the research, the recommendations consider different Machine-to-Machine (M2M) communication nodes with regard to their ability to identify malicious and honest information. To validate the performances of these models, two trust computation measures were used: Receiver Operating Characteristics (ROCs), Precision and Recall. The malicious data was formulated in Matlab. A scenario was created where 50% of the information were modified to be malicious. The malicious nodes were varied in the ranges of 10%, 20%, 30%, 40%, and the results were carefully analyzed.
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| 2. |
- Eziama, Elvin, et al.
(författare)
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Malicious Node Detection in Vehicular Ad-Hoc Network Using Machine Learning and Deep Learning
- 2018
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Ingår i: 2018 IEEE GLOBECOM WORKSHOPS (GC WKSHPS). - : IEEE. - 9781538649206
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Konferensbidrag (refereegranskat)abstract
- Vehicular Ad hoc Networks (VANETs) provide effective vehicular operation for safety as well as greener and more efficient communication of vehicles in the Dedicated Short Range Communication (DRSC). The dynamic nature of the vehicular network topology has posed many security challenges for effective communication among vehicles. Consequently, models have been applied in the literature to checkmate the security issues in the vehicular networks. Existing models lack flexibility and sufficient functionality in capturing the dynamic behaviors of malicious nodes in the highly volatile vehicular communication systems. Given that existing models have failed to meet up with the challenges involved in vehicular network topology, it has become imperative to adopt complementary measures to tackle the security issues in the system. The approach of trust model with respect to Machine/Deep Learning (ML/DL) is proposed in the paper due to the gap in the area of network security by the existing models. The proposed model is to provide a data-driven approach in solving the security challenges in dynamic networks. This model goes beyond the existing works conceptually by modeling trust as a classification process and the extraction of relevant features using a hybrid model like Bayesian Neural Network that combines deep learning with probabilistic modeling for intelligent decision and effective generalization in trust computation of honest and dishonest nodes in the network.
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| 3. |
- Nikoui, T. S., et al.
(författare)
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Container-Based Load Balancing and Monitoring Approach in Fog Computing System
- 2022
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Ingår i: MELECON 2022 - IEEE Mediterranean Electrotechnical Conference, Proceedings. - : Institute of Electrical and Electronics Engineers Inc.. - 9781665442800 ; , s. 1159-1164
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Konferensbidrag (refereegranskat)abstract
- The Internet of Things has become a fast-growing area and has attracted considerable attention from the research communities. To provide satisfactory performance and efficiency in smart applications, it is inevitable to apply proper and efficient load balancing mechanisms. This paper presents a container-based load balancing and monitoring approach in fog-cloud environments. The presented architecture is composed of application services, message queuing system and online monitoring tools to address fault-detection, reliability, higher efficiency and scalability that are essential requirements of smart applications. In addition, an implementation of the proposed approach by using well-known technologies is presented, and the system is evaluated. Results indicate that using this model satisfies the requirements and can be considered as a practical solution to fog computing applications.
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| 4. |
- Nikoui, T. S., et al.
(författare)
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Presenting an Edge-based Air Quality Management System for Smart City Scenarios
- 2023
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Ingår i: Int. Conf. Inf. Knowl. Technol., IKT. - : Institute of Electrical and Electronics Engineers Inc.. - 9798350349412 ; , s. 235-240
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Konferensbidrag (refereegranskat)abstract
- The IoT as a fast-growing topic attracts attention from the research communities and business owners since the IoT paradigm can improve the ease of life and present new opportunities. The smart city scenario offers technology-based facilities for citizens to enhance their quality of life, public safety, and disaster management. Smart city context includes multiple subsystems with massive numbers and different types of IoT devices. This study focuses on implementing an edge-based air quality management system and presents the requirement analysis phase as the first step to outline the essential requirements. Considering this phase, the presented architecture is defined to improve availability, scalability, and interoperability. Moreover, it provides data analysis and alerting functionalities. The system is implemented using open-source technologies to decrease the cost of development and improve time to market. In addition, several REST APIs are provided to achieve higher interoperability and more efficient integration. The system is evaluated using a real data set, and the presented results indicate that using this model can improve time efficiency while meeting non-functional requirements.
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