An intelligent ensemble of long ‐short ‐term memory with genetic algorithm for network anomaly identification
I. Sumaiya Thaseen, Arun Krishna Chitturi, Fadi M. Al-Turjman, Achyut Shankar, Muhammad Rukunuddin Ghalib, Kumar Abhishek · Transactions on Emerging Telecommunications Technologies · 2020
Abstract Cyberattacks are increasing rapidly with rapid Internet advancement and, the cybersecurity situation is not optimistic. Anomaly detection is one of the challenging sectors of network security, which shows a significant role in any organization. Many anomaly detection systems identify malicious activities by deploying machine learning and deep learning techniques. The major contribution of this research is to develop an anomaly detection model for networks using a homogenous ensemble of Long‐Short‐Term‐Memory integrated with Genetic Algorithm (GA) utilized for feature extraction. An extensive literature on anomaly detection, which utilizes deep learning algorithms, is studied. NSL‐KDD and UNSW‐NB datasets are deployed for evaluating the proposed network anomaly model. The experimental analysis shows that the proposed ensemble is superior to other ensembles with a maximum accuracy of 99.9% and a minimum false alarm rate of 1.56% on NSL‐KDD dataset and a maximum accuracy of 99.3% is obtained on UNSW‐NB15 dataset with false alarm rate of 1.7%. Hence, the proposed model performs fair on both the datasets.