An Intelligent Unified Framework for Network Security in Threat Identification using Deep Learning Methods

Abstract

An integrated threat analysis and mitigation system that enhances network security with the help of deep learning. Policies, technology and monitoring systems are important in the protection of infrastructures against dynamic threats. The detection and mitigation systems of cyber threats (particularly botnets) are becoming increasingly more complex and require intelligent and adaptable systems that are capable of detecting and mitigating them in real-time. The presented paper presents a deep learning network that integrates the use of SMOTE data balancing, GA feature optimization, and RNN trained on the CTU-13 dataset. The proposed RNN outperformed the traditional counterparts in terms of sequential traffic patterns that are critical to intrusion detection with a high accuracy (ACC) of 99.25, precision (PRE) of 98.30, recall (REC) of 99.25, and F1-score (F1) of 98.75. Studies revealed that RNN performed better than the baseline models, e.g. Hidden Markov Model (HMM) (94.80% accuracy), Convolutional Neural Network (CNN) (97.21% accuracy), and Support Vector Machine (SVM) (92% accuracy). The fact that the RNN could model temporal dependencies minimized misclassifications, and the network was also more resilient. The above results indicate the promise of RNNs in future-generation cybersecurity, which provides scalable, proactive threat detection in dynamic networks, including network descriptions facilitated by clouds and supported by IoT, which is essential to ensure the security of critical systems.