Data-Driven Framework for IT and Mobile Network Attack Detection

Abstract

The rapid proliferation of Internet of Things (IoT) devices and mobile communication networks has increased the vulnerability of interconnected systems to various cyberattacks, including Distributed Denial of Service (DDoS), malware, spoofing, phishing, and unauthorized access. Traditional security mechanisms often fail to detect sophisticated and evolving threats in real time. This paper presents an intelligent attack detection system for IoT and mobile networks using machine learning techniques. The proposed framework performs data pre-processing, feature extraction, and classification to distinguish normal network behaviour from malicious activities. Supervised learning algorithms are employed to identify attack patterns with high accuracy while minimizing false-positive rates. The system continuously monitors network traffic and generates real-time alerts upon detecting suspicious activities. Experimental results demonstrate significant improvements in detection accuracy, precision, recall, and response time compared with conventional security approaches. The proposed model effectively detects multiple categories of network attacks and enhances the overall security and reliability of IoT and mobile communication environments. The findings indicate that the system improves network resilience, protects sensitive information, and supports secure communication among interconnected devices. Consequently, the proposed framework provides a scalable and efficient cybersecurity solution for next-generation IoT ecosystems and mobile network infrastructures