Machine Learning Based Security Mechanism Against DDoS Attacks in Mobile Edge Computing

Mobile Edge Computing environments, become very important topic in the field of networking and telecommunication by providing low latency service with their structure and resources. Distributed Denial of Service attacks are particularly vulnerable which can severely Freezing the services, affect the infrastructure, and slowing down network performance. These threats are often fail to addressed through traditional security frameworks. This research describes different categories of solutions aimed at improving resilience in MEC networks while considering their limits and constraints. The research assesses the performance of thirteen models, including machine learning, deep learning, unsupervised, hybrid, and transformer-based approaches for DDoS attack detection. In the realm of ML, Random Forest takes the crown with Accuracy (99.92%) to Boosting and Decision Tree with (99.85%) and (99.86%) respectively. CNN-LSTM in the hybrid deep learning also performed well with 99.69% accuracy, while BiLSTM trailed at 98.74%. K-Means and DBSCAN also proved to be helpful with 94.21% and 95.95% accuracy, K-Means being the least accurate among the unsupervised methods. These models can help reduce latency in Mobile Edge Computing by detecting DDoS attacks quickly and directly at the edge of the network.