Visible to the public Machine Learning-Based Recommendation Trust Model for Machine-to-Machine Communication

TitleMachine Learning-Based Recommendation Trust Model for Machine-to-Machine Communication
Publication TypeConference Paper
Year of Publication2018
AuthorsEziama, E., Jaimes, L. M. S., James, A., Nwizege, K. S., Balador, A., Tepe, K.
Conference Name2018 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)
ISBN Number978-1-5386-7568-7
KeywordsBayes methods, composability, Computational modeling, computer network security, Decision Tree, Decision trees, Internet, Internet of Things(IoTs), Internet of Vehicles(IoVs), Internet protocol, k nearest neighbor, learning (artificial intelligence), machine learning, machine learning trust evaluation, Machine learning-based recommendation trust model, Machine Type Communication Devices, Machine-to-Machine communication nodes, machine-to-machine communications, Machine-to-Machine(M2M), malicious data, Mathematical model, naive Bayes, pattern classification, Peer-to-peer computing, privacy, pubcrawl, radial support vector machine, Random Forest, receiver operating characteristics, reliability, resilience, Resiliency, security, security issues, security threats, Supervisory Control and Data Supervisory Acquisition(SCADA), Support vector machines, telecommunication computing, trust computation measures, Trusted Computing

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.

Citation Keyeziama_machine_2018