Security Enhancement by Identifying Attacks Using Machine Learning for 5G Network

Need of security enhancement for 5G network has been increased in last decade. Data transmitted over network need to be secure from external attacks. Thus there is need to enhance the security during data transmission over 5G network. There remains different security system that focus on identification of attacks. In order to identify attack different machine learning mechanism are considered. But the issue with existing research work is limited security and performance issue. There remains need to enhance security of 5G network. To achieve this objective hybrid mechanism are introduced. Different treats such as Denial-of-Service, Denial-of-Detection, Unfair use or resources are classified using enhanced machine learning approach. Proposed work has make use of LSTM model to improve accuracy during decision making and classification of attack of 5G network. Research work is considering accuracy parameters such as Recall, precision and F-Score to assure the reliability of proposed model. Simulation results conclude that proposed model is providing better accuracy as compared to conventional model

Exploring Artificial Intelligence (AI) Techniques for Forecasting Network Traffic: Network QoS and Security Perspectives

This thesis identifies the research gaps in the field of network intrusion detection and network QoS prediction, and proposes novel solutions to address these challenges. Our first topic presents a novel network intrusion detection system using a stacking ensemble technique using UNSW-15 and CICIDS-2017 datasets. In contrast to earlier research, our proposed novel network intrusion detection techniques not only determine if the network traffic is benign or normal, but also reveal the type of assault in the flow. Our proposed stacking ensemble model provides a more effective detection capability than the existing works. Our proposed stacking ensemble technique can detect 90.4% and 98.7% cyberattacks with an f1-score of 90.0% and 98.5%, respectively. Our second topic proposes a novel QoS prediction model tested in a live 5G network environment. Compared to the existing work in this domain, our study is the first approach to conduct a large-scale field test in a 5G network to measure and forecast the network QoS metrics. More than 50 days of continuous data have been collected, cleaned, and used for training the deep sequence models to predict the 5G network QoS metrics such as throughput, latency, jitter, and packet loss. Our experiments demonstrate the effectiveness of predicting the QoS metrics using LSTM and LSTM Encoder-Decoder models, providing lower prediction errors of 14.57% and 13.75%, respectively