Owing to the rapid development and wide applications of the Internet of Things (IoT) techniques, security of IoT has attracted increasing attentions. IoT is a sensor network consisting of various sensor nodes, which are readily exposed to attacks as they are usually located in sites with no monitoring [
1,
2]. To make it worse, attacks on IoT may lead to huge damages in a wide range, compared with computer networks. Hence, security risks in all aspects of IoT and strategies should be analyzed as a whole and the simplification of end security set-up is of IoT great significance [
3]. The detection systems should be further optimized based on analysis of risk categories and security structures of IoT [
4].
The intrusion detection method judges attacks based on data collected by multiple collection points in a computer network [
5,
6]. The intrusion detection is an active protection technique that can intercept and respond to intrusions before they reach the network. However, the huge network data traffic is a huge challenge to intrusion detection systems as it induces high requirements on the detection efficiency so that attacks can be detected in real time. The restricted Boltzmann machine (RBM) network was trained using the greedy algorithm, and low dimension expressions of the RBM network output were classified downwards using the back propagation algorithm [
7]. The results indicated that the proposed model shows improved accuracy of intrusion detections, thus suitable for information extractions in high-dimension space. For data compression and low clustering efficiency issues, a modified self-adjustment clustering method is established based on direct correlation to samples close to cluster center [
8]. This method effectively reduced clustering sample size and clustering time-space consumption and improves the effectiveness of intrusion detection. Aimed at feature optimization and selection in intrusion detections, a support vector machine (SVM) based two-stage feature selection method was proposed based on feature evaluations of the ratio of detection rate and false alarm rate [
9]. In this method, filter noises and irrelevant features were filtered using Fisher classification and information gain in the filtering mode, respectively, to obtain overlapping feature subsets and effectively reduce modeling and detection time. For intrusion detections of internal nodes in wireless sensor networks (WSN), a layer-clustered intrusion detection method for trust value of node a based on the Beta distribution theory and outlier factor was proposed [
10]. This method identifies abnormal nodes based on the Mahalanobis distance and exhibits low false alarm rates. Based on classification methods in data mining, optimized solutions were identified by direction calculations of relevant matrices and multi-category network attacks are analyzed by multi-objective mathematical programming model [
11]. This method exhibits advantages such as low complexity, effective detections of multi-category attacks, and low false alarm rates. A fuzzy clustering intrusion model based on genetic algorithm and hierarchical algorithm was proposed [
12]. Herein, the feature volume was determined by deletion of data set features using the Youden index, the susceptibility to initial cluster centers was relieved, and the local optimization issues in iteration were overcome. Experimental results demonstrated excellent detection performance of the proposed model for network attacks. For Kernel restriction issues in minimum enclosing ball algorithm, an intrusion detection method based on minimum enclosing ball with extensive Kernel was proposed [
13]. This method can obtain the minimum enclosing ball of the sample set according to updates of the center and the radius of sphere and categorize network intrusions according to distributions of support vectors. To enhance the accuracy of intrusion detections, an intrusion detection method combining artificial immunity and rough set was proposed for vaccine injections [
14]. This method can achieve real time detections of unknown attacks with improved effectiveness and efficiency. Also, a network intrusion detection model was proposed and an alarm system combining multiple proof techniques was established to filter false alarms [
15]. An improved multi-objective genetic algorithm-based intrusion detection integrated method has been proposed [
16]. This algorithm can effectively solve feature selection issues in intrusion detections, and the method based on this algorithm exhibited excellent detection accuracy and wide applicability to different categories of attacks.
Although intrusion detection technology has been widely used, there are still many problems, such as large number of alerts, high false alarm rate, poor generality, and false report. In this article, an intrusion detection method for IoT was proposed based on suppressed fuzzy clustering (SFC) algorithm [
17,
18] and principal component analysis (PCA) algorithm [
19,
20]. Simulations demonstrated high detection efficiency and significantly reduced detection time of the proposed method. Section
2 describes the objective prejudgment model of intrusion detections, Section
3 proposes solution of intrusion detections, Section
4 involves simulations, and Section
5 includes a conclusion.