Research Article Adaptive Anomaly Detection Framework Model Objects in Cyberspace Hasan Alkahtani, 1 Theyazn H. H. Aldhyani , 2 and Mohammed Al-Yaari 3 1 College of Computer Science and Information Technology, King Faisal University, P.O. Box 4000, Al-Ahsa 31982, Saudi Arabia 2 Community College of Abqaiq, King Faisal University, P.O. Box 4000, Al-Ahsa 31982, Saudi Arabia 3 Chemical Engineering Department, King Faisal University, P.O. Box 380, Al-Ahsa 31982, Saudi Arabia Correspondence should be addressed to Theyazn H. H. Aldhyani; taldhyani@kfu.edu.sa Received 27 October 2020; Revised 23 November 2020; Accepted 28 November 2020; Published 10 December 2020 Academic Editor: Mohammed Yahya Alzahrani Copyright © 2020 Hasan Alkahtani et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Telecommunication has registered strong and rapid growth in the past decade. Accordingly, the monitoring of computers and networks is too complicated for network administrators. Hence, network security represents one of the biggest serious challenges that can be faced by network security communities. Taking into consideration the fact that e-banking, e-commerce, and business data will be shared on the computer network, these data may face a threat from intrusion. The purpose of this research is to propose a methodology that will lead to a high level and sustainable protection against cyberattacks. In particular, an adaptive anomaly detection framework model was developed using deep and machine learning algorithms to manage automatically-configured application-level firewalls. The standard network datasets were used to evaluate the proposed model which is designed for improving the cybersecurity system. The deep learning based on Long-Short Term Memory Recurrent Neural Network (LSTM-RNN) and machine learning algorithms namely Support Vector Machine (SVM), K-Nearest Neighbor (K-NN) algorithms were implemented to classify the Denial-of-Service attack (DoS) and Distributed Denial-of-Service (DDoS) attacks. The information gain method was applied to select the relevant features from the network dataset. These network features were significant to improve the classification algorithm. The system was used to classify DoS and DDoS attacks in four stand datasets namely KDD cup 199, NSL-KDD, ISCX, and ICI-ID2017. The empirical results indicate that the deep learning based on the LSTM-RNN algorithm has obtained the highest accuracy. The proposed system based on the LSTM-RNN algorithm produced the highest testing accuracy rate of 99.51% and 99.91% with respect to KDD Cup’99, NSL-KDD, ISCX, and ICI-Id2017 datasets, respectively. A comparative result analysis between the machine learning algorithms, namely SVM and KNN, and the deep learning algorithms based on the LSTM-RNN model is presented. Finally, it is concluded that the LSTM-RNN model is efficient and effective to improve the cybersecurity system for detecting anomaly-based cybersecurity. 1. Introduction The end of the Cold War has led to many challenges and threats that the international community has never seen before, known as asymmetric or asymmetric cross-border threats that recognize neither borders and national sover- eignty nor the idea of a nation-state. These threats led to shifts in the field of security and strategic studies as well as at the level of political practice. The explosion of the informa- tion revolution and the entry of the digital age, especially in the 21st century resulted in many repercussions manifested in the emergence of cyber threats and crimes. Such threats are regarded to be a major challenge to the national as well as international security making cyberspace as the fifth area of war after land, sea, air, and space. These repercussions entailed the need for security guarantees within this digital environment which led to the emergence of cybersecurity as a new dimension within the field of security studies that has acquired the interests of many researchers in this area. Having said that, we need to understand what cybersecurity is as a new variable in international relations. The task of adjusting concepts and terminology is a challenge facing Hindawi Applied Bionics and Biomechanics Volume 2020, Article ID 6660489, 14 pages https://doi.org/10.1155/2020/6660489