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.