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Adaptive Anomaly Detection Framework Model Objects in Cyberspace
Applied Bionics and Biomechanics ( IF 2.2 ) Pub Date : 2020-12-10 , DOI: 10.1155/2020/6660489
Hasan Alkahtani 1 , Theyazn H H Aldhyani 2 , Mohammed Al-Yaari 3
Affiliation  

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.

中文翻译:

自适应异常检测框架模型网络空间中的对象

电信在过去十年中取得了强劲而快速的增长。因此,计算机和网络的监控对于网络管理员来说过于复杂。因此,网络安全是网络安全社区面临的最严峻的挑战之一。考虑到电子银行、电子商务和商业数据将在计算机网络上共享,这些数据可能面临入侵的威胁。这项研究的目的是提出一种方法,以实现高水平和可持续的网络攻击防护。特别是,使用深度和机器学习算法开发了自适应异常检测框架模型来管理自动配置的应用程序级防火墙。标准网络数据集用于评估为改进网络安全系统而设计的模型。基于长短期记忆递归神经网络 (LSTM-RNN) 的深度学习和机器学习算法,即支持向量机 (SVM)、K-近邻 (K-NN) 算法,对拒绝服务进行分类攻击 (DoS) 和分布式拒绝服务 (DDoS) 攻击。应用信息增益方法从网络数据集中选择相关特征。这些网络特征对于改进分类算法具有重要意义。该系统用于对四个标准数据集(即 KDD cup 199、NSL-KDD、ISCX 和 ICI-ID2017)中的 DoS 和 DDoS 攻击进行分类。实证结果表明,基于LSTM-RNN算法的深度学习获得了最高的准确率。基于 LSTM-RNN 算法的所提出的系统在 KDD Cup'99、NSL-KDD、ISCX 和 ICI-Id2017 数据集上分别产生了 99.51% 和 99.91% 的最高测试准确率。对SVM、KNN等机器学习算法与基于LSTM-RNN模型的深度学习算法进行结果对比分析。最后得出的结论是,LSTM-RNN模型对于改进基于异常的网络安全检测的网络安全系统是高效且有效的。
更新日期:2020-12-10
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