Abstract
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.
Highlights
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 sovereignty nor the idea of a nation-state
To answer the third question, the prediction results of deep learning based on the Long Short Memory (LSTM)-Recurrent Neural Network (RNN) algorithm to detect the Denial-of-Service attack (DoS), Distributed Denial-of-Service (DDoS) attacks and normal from standard network datasets are demonstrated
The result outcome from the machine learning, namely Support Vector Machine (SVM) and KNN and deep learning, based on the LSTMRNN algorithms for detection cyber-attack is approved by using evaluation metrics
Summary
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 sovereignty nor the idea of a nation-state. The explosion of the information 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. The task of adjusting concepts and terminology is a challenge facing
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