Published in IEEE Transactions on Network and Service Management, 2024
In this paper, we propose a DL-based framework called the Ensemble Framework for Network Intrusion Detection System (ENIDS) to detect various types of cyberattacks, which includes dynamic data pre-processing, optimal feature selection, the handling of imbalanced data samples, and a DL-based ensemble model.
Published in [Currently in Embargo], 2023
Our study of previous QoS datasets for networks shows that most of the available datasets are generated either in a simulated environment or using different network performance test applications that do not reflect the real-life use cases of the network. Furthermore, some other datasets were generated in an uncontrolled environment using the 4G network, with fluctuations in performance across dayparts as the number of users varies. To overcome the shortages of available QoS datasets collected from real-time network traffic WING LABS developed a dataset named WING-5G-QoS2023 composed of four different 5G QoS metrics.
Published in Masters Thesis, Western University, 2022
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.
Published in 2022 International Wireless Communications and Mobile Computing (IWCMC), 2022
TThis paper presents a multi-classifier model using stacking ensemble deep neural networks that identify several types of DDoS attacks to address the issues mentioned above. Our proposed hybrid model incorporates Convolution Neural Network (CNN), Long Short Term Memory (LSTM), and Gated Recurrent Unit (GRU), and we show that while evaluating models with large datasets such as CIC-DDoS2019, ensemble technique increases model performance.
Published in International Journal of Advanced Networking and Applications, 2022
In this study two different approaches of controller placement were thoroughly examined and combined to offer a solution that minimizes the propagation delay among nodes and maximizes the QoS of the network by maintaining better load balancing.
Published in 2019 International Conference on Machine Learning and Data Engineering (iCMLDE), 2020
This paper investigates the performance of Logistic Regression, Random Forest, Decision Tree and SVM (Support Vector Machine). The proposed system shows results according to accuracy, sensitivity, specificity, precision of above techniques.
Published in 2018 International Conference on Machine Learning and Data Engineering (iCMLDE), 2019
A perennial need for safety in the community depends on country, city, and district. In some instances, feeling safe is required on a 24/7 basis. A popular and cost-effective solution based on the Raspberry Pi has the promise of being both user-friendly and costeffective. It pairs the Raspberry Pi to a camera module for face recognition. It learns to detect those with granted access to the specified area under protection. Such stored faces are the subject of system training. If during operation the system recognizes the face in the dataset, then the camera shows the matching name with a confidence level possibly granting access, but alternatively it takes a photo of the subject and sends it as an email notifications warning. The proposed system can implement face recognition even from poor quality images performing well over both known and unknown datasets. Face recognition leverages techniques from the OpenCV library and is written in the Python language.