An Intelligent Hybrid Artificial Intelligence Framework for Accurate Cyber Threat Detection Using Machine Learning and Deep Learning Techniques
Authors-R.Sankeerthana, Aishwarya Mukkamla
Keyword-Cyber Security, Hybrid Threat Detection, Artificial Intelligence, Machine Learning, Deep Learn-ing, Intrusion Detection System, Network Security, Anomaly Detection, Rule-Based Detection, Cyber Attack Analysis.
The rapid growth of digital communication, cloud computing, Internet of Things (IoT), and enterprise networking has significantly increased the complexity and frequency of cyber threats. Conventional intrusion detection mechanisms that rely solely on predefined signatures or static rules often fail to recognize sophisticated attacks, zero-day exploits, and continuously evolving malicious activities. To overcome these limitations, this research proposes a hybrid artificial intelligence-based cyber threat detection framework that integrates machine learning, deep learn-ing, anomaly detection, and rule-based security mechanisms into a unified architecture. The proposed framework continuously analyzes network traffic, user behavior, and system activities to identify both known and unknown cyber attacks with improved accuracy. Machine learning algorithms are employed to discover hidden attack patterns, while deep learning models perform advanced feature learning for complex threat classification. Rule-based verification further vali-dates suspicious events and minimizes false alarms before generating security alerts. The integra-tion of these complementary techniques improves detection performance, enhances adaptability against emerging threats, and reduces false positive rates without changing the original imple-mentation methodology. Experimental observations demonstrate that the hybrid framework provides superior recall, specificity, accuracy, and overall detection efficiency compared with conventional security approaches. The proposed model establishes a scalable, intelligent, and reliable cybersecurity solution capable of protecting modern digital infrastructures against rapidly evolving cyber threats while maintaining the same algorithms and evaluation strategy presented in the original research.
Doi-[https://doi.org/10.5281/zenodo.21735133]