E. C. IGODAN

The effectiveness of machine learning in classifying prostate cancer from high–dimensional microarray data is often constrained by feature selection instability and scalability challenges. To address these issues, we propose the Ensemble-based Distributed Filtering and Classification Model (EnD-FCM), which integrates stability-driven ensemble learning with distributed feature partitioning.

In order to identify distributed denial-of-service (DDoS) attacks on SIP-VoIP  infrastructures in real time, this paper proposes a genetic algorithm-trained modular neural network (MNN) with SMOTETomek balancing. In contrast to traditional firewalls and static filters, our framework leverages modular deep learning and evolutionary optimisation to accurately and adaptively identify malicious traffic.

Due to lack of scalability of feature selection algorithms when applied in a centralized manner, most classification algorithms perform sub-optimally especially in the presence of irrelevant and redundant features in high dimensional datasets-large feature size small instances. Though it is imperative to remove insignificant features to improve learning, the process is complex and time-consuming.