Abstract In the past decade, wired and wireless computer networks have substantially evolved because of the rapid development of technologies such as the Internet of Things (IoT), wireless handled devices, vehicular networks, 4G and 5G, cyber-physical systems, etc. These technologies exchange large amount of data, and as a result, they are prone to several malicious actions, attacks and security threats that can compromise the availability and integrity of information or services. Therefore, the security and protection of the various communication infrastructures using an intrusion detection system (IDS) is of critical importance. In this research, we propose a Feed-Forward Deep Neural Network (FFDNN) wireless IDS system using a Wrapper Based Feature Extraction Unit (WFEU). The extraction method of the WFEU uses the Extra Trees algorithm in order to generate a reduced optimal feature vector. The effectiveness and efficiency of the WFEU-FFDNN is studied based on the UNSW-NB15 and the AWID intrusion detection datasets. Furthermore, the WFEU-FFDNN is compared to standard machine learning (ML) algorithms that include Random Forest (RF), Support Vector Machine (SVM), Naive Bayes (NB), Decision Tree (DT) and k-Nearest Neighbor (kNN). The experimental studies include binary and multiclass types of attacks. The results suggested that the proposed WFEU-FFDNN has greater detection accuracy than other approaches. In the instance of the UNSW-NB15, the WFEU generated an optimal feature vector consisting of 22 attributes. Using this input vector; our approach achieved overall accuracies of 87.10% and 77.16% for the binary and multiclass classification schemes, respectively. In the instance of the AWID, a reduced input vector of 26 attributes was generated by the WFEU, and the experiments demonstrated that our method obtained overall accuracies of 99.66% and 99.77% for the binary and the multiclass classification configurations, respectively.

中文翻译：

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基于Wrapper特征提取的无线入侵检测系统深度学习方法

摘要 在过去的十年中，由于物联网 (IoT)、无线处理设备、车载网络、4G 和 5G、信息物理系统等技术的快速发展，有线和无线计算机网络发生了实质性的发展。技术交换大量数据，因此，它们容易受到多种恶意操作、攻击和安全威胁，这些威胁可能危及信息或服务的可用性和完整性。因此，使用入侵检测系统 (IDS) 的各种通信基础设施的安全和保护至关重要。在这项研究中，我们提出了一种使用基于包装的特征提取单元 (WFEU) 的前馈深度神经网络 (FFDNN) 无线 IDS 系统。WFEU 的提取方法使用 Extra Trees 算法来生成简化的最优特征向量。基于 UNSW-NB15 和 AWID 入侵检测数据集研究了 WFEU-FFDNN 的有效性和效率。此外，WFEU-FFDNN 与标准机器学习 (ML) 算法进行了比较，包括随机森林 (RF)、支持向量机 (SVM)、朴素贝叶斯 (NB)、决策树 (DT) 和 k-最近邻 (kNN) . 实验研究包括二进制和多类攻击。结果表明，所提出的 WFEU-FFDNN 比其他方法具有更高的检测精度。在 UNSW-NB15 的实例中，WFEU 生成了一个由 22 个属性组成的最优特征向量。使用这个输入向量；我们的方法实现了 87.10% 和 77 的总体准确率。二元和多类分类方案分别为 16%。在 AWID 的实例中，WFEU 生成了一个包含 26 个属性的简化输入向量，实验表明，我们的方法对二元和多类分类配置的总体准确率分别为 99.66% 和 99.77%。