Eigenvalue Based Active User Enumeration for Grant-Free Access Under Carrier Frequency Offsets

This paper investigates a grant-free non-orthogonal multiple access (GF-NOMA) system in the presence of carrier frequency offsets. We propose two schemes for enumerating active users in such a GF-NOMA system, which is equivalent to estimating the sparsity level. Both schemes utilize a short common pilot and the eigenvalues of the sample covariance matrix of the received signal. The two schemes differ in their treatment of noise variance: one exploits known variance information, while the other is designed to function without this knowledge. Simulation results demonstrate the effectiveness of the proposed schemes in terms of the normalized mean-squared error.

Dealing with Imbalanced Classes in Bot-IoT Dataset

With the rapidly spreading usage of Internet of Things (IoT) devices, a network intrusion detection system (NIDS) plays an important role in detecting and protecting various types of attacks in the IoT network. To evaluate the robustness of the NIDS in the IoT network, the existing work proposed a realistic botnet dataset in the IoT network (Bot-IoT dataset) and applied it to machine learning-based anomaly detection. This dataset contains imbalanced normal and attack packets because the number of normal packets is much smaller than that of attack ones. The nature of imbalanced data may make it difficult to identify the minority class correctly. In this thesis, to address the class imbalance problem in the Bot-IoT dataset, we propose a binary classification method with synthetic minority over-sampling techniques (SMOTE). The proposed classifier aims to detect attack packets and overcome the class imbalance problem using the SMOTE algorithm. Through numerical results, we demonstrate the proposed classifier's fundamental characteristics and the impact of imbalanced data on its performance.