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Internet of Things (IoTs) has been playing an important role in many sectors, such as smart cities, smart agriculture, smart healthcare, and smart manufacturing. However, IoT devices are highly vulnerable to cyber-attacks, which may result in security breaches and data leakages. To effectively prevent these attacks, a variety of machine learning-based network intrusion detection methods for IoT networks have been developed, which often rely on either feature extraction or feature selection techniques for reducing the dimension of input data before being fed into machine learning models. This aims to make the detection complexity low enough for real-time operations, which is particularly vital in any intrusion detection systems. This paper provides a comprehensive comparison between these two feature reduction methods of intrusion detection in terms of various performance metrics, namely, precision rate, recall rate, detection accuracy, as well as runtime complexity, in the presence of the modern UNSW-NB15 dataset as well as both binary and multiclass classification. For example, in general, the feature selection method not only provides better detection performance but also lower training and inference time compared to its feature extraction counterpart, especially when the number of reduced features K increases. However, the feature extraction method is much more reliable than its selection counterpart, particularly when K is very small, such as K = 4 . Additionally, feature extraction is less sensitive to changing the number of reduced features K than feature selection, and this holds true for both binary and multiclass classifications. Based on this comparison, we provide a useful guideline for selecting a suitable intrusion detection type for each specific scenario, as detailed in Table 14 at the end of Sect. 4. Note that such the comparison between feature selection and feature extraction over UNSW-NB15 as well as theoretical guideline have been overlooked in the literature. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) devices are widely used but also vulnerable to cyberattacks that can cause security issues. To protect against this, machine learning approaches have been developed for network intrusion detection in IoT. These often use feature reduction techniques like feature selection or extraction before feeding data to models. This helps make detection efficient for real-time needs. This paper thoroughly compares feature extraction and selection for IoT network intrusion detection in machine learning-based attack classification framework. It looks at performance metrics like accuracy, f1-score, and runtime, etc. on the heterogenous IoT dataset named Network TON-IoT using binary and multiclass classification. Overall, feature extraction gives better detection performance than feature selection as the number of features is small. Moreover, extraction shows less feature reduction compared with that of selection, and is less sensitive to changes in the number of features. However, feature selection achieves less model training and inference time compared with its counterpart. Also, more space to improve the accuracy for selection than extraction when the number of features changes. This holds for both binary and multiclass classification. The study provides guidelines for selecting appropriate intrusion detection methods for particular scenarios. Before, the TON-IoT heterogeneous IoT dataset comparison and recommendations were overlooked. Overall, the research presents a thorough comparison of feature reduction techniques for machine learning-driven intrusion detection in IoT networks. [ABSTRACT FROM AUTHOR]

The recent trend in network intrusion detection leverages key features of machine learning (ML) algorithms to detect network traffic anomalies. Network traffic flows contain high dimensional features which significantly affect data-driven approaches. Therefore, the performance of ML-based approaches mainly depends on the appropriate set of features of network data. Different feature selection and extraction methods are extensively employed to attain the informative and compact set of features. Existing methods often suffer from achieving the expected performance due to the lacking of effectively removing redundant features as well as incorporating features with complementary information. In this paper, we present a cluster-based feature extraction method using Mahalanobis distance (cFEM) that clusters the correlated features and extracts new feature representations based on a distance metric. The extracted features on the transformed dimensions are employed to train different machine learning classifiers. We conducted extensive experiments using three renowned datasets. The results show that cFEM outperforms the state-of-the-art intrusion detection methods in several performance metrics such as detection rate (99.61%) and false alarm rate (0.26%). Further experiments on extracted features show that our extracted features are discriminative, free of redundancy, and able to capture complementary information. [ABSTRACT FROM AUTHOR]

With the emergence of big data era, the dimensions of data are enhanced exponentially and it becomes a difficult task to handle information of high dimensions in various sectors like text mining, machine learning and data analysis. Redundant and inappropriate feature enhances the complexities in dimensions that further results in poor performances. In the intrusion detection system, the feature selection is considered as one of the most significant processes to improve the performances of the system. Due to high dimensional data, there occurs a drop in accuracy and efficiency. To overcome such drawback, this paper proposes three major phases namely the data pre-processing, feature selection and classification phases. In data-pre processing phase, the input data comprising of various noise signals, high dimensional and redundant data, numerous irrelevant features etc. are extracted. The second phase involves the selection of features using cooperative and competitive (C2) search based learning algorithm. In the classification phase, the extracted features are classified optimally using Bonferroni based Hybrid k-nearest neighbour (B-HkNN) algorithm thereby obtaining an optimal intrusion detection system. Furthermore, the proposed approach based on intrusion detection system is evaluated by the standard CICIDS2017 and ADFA-LD datasets to determine the accuracy and efficiency of the system. [ABSTRACT FROM AUTHOR]