The Internet of Things (IoT) is a large-scale network of various sensing devices connected via the Internet to achieve intelligent functions. Efficient secure data transmission is a significant guarantee for specific functions of IoT. In recent years, compressive sensing (CS) has been applied to the field of the IoT and a substantial number of CS-based IoT data transmission schemes have been proposed. While existing IoT data transmission schemes based on CS could mostly accomplish signal sampling, data compression, and encrypted transmission, the goal of privacy protection has rarely been achieved. Based on chaos theory and semi-tensor product CS (STP-CS), this article proposes two novel secure data transmission methods for different scenarios of IoT: 1) multiple concealing method (MC method) and 2) precise concealing method (PC method). Both methods can provide different levels of reconstruction results for different receivers with various authorization levels. The feasibility, storage requirements, robustness, and security of the proposed methods are theoretically analyzed and experimentally simulated. The results show that the proposed methods ensure both the stability and security of data transmission, save storage space of sensors, and flexibly protect the privacy of the content of data transmitted.

As a result of information explosion and the development of the technology of Internet of Things (IoT), tons of data are collected and transmitted, some of which may concern private or confidential information. Sensors, as the building blocks of such processes, are commonly resource-limited. For instance, they rarely possess large storage space, abundant electricity reserves, or sufficient computing power. So, how to improve efficiency of data acquisition and transmission without trimming security has attracted much attention of researchers. Recently, compressive sensing theory has been exploited to improve efficiency of data collection and transmission, while there are some drawbacks to existing compressive sensing-based data collection and transmission schemes. For instance, many existing CS-based data transmission methods lack data encryption or privacy protection mechanisms, meaning that private or confidential information may be exposed to uncertainty if such data are captured during transmission. This paper proposes a method of collecting and transmitting data securely and efficiently based on P-tensor product compressive sensing and secret key encryption mechanism. It could provide data security under the assumption that participants and data processing domains are trusted. And adversaries could not obtain useful information, even if they manage to capture data that are transmitted. Compared with most existing compressive-sensing-based methods, the proposed method addresses privacy protection issues by realizing multi-level critical information concealment. By reducing the scale of measurement matrices, it achieves lower computing and storage resource consumption. To transmit data more securely, it also provides an encryption mechanism with sufficient key space and high key sensitivity. Overall, the proposed method is secure, energy-efficient, and provides privacy protection functions, which makes it particularly suitable for IoT sensor nodes with restricted computing power and storage capacity.

Nowadays, the requirements for image data sharing among participant nodes of the Internet of Things (IoT) are constantly emerging. And thanks to the popularity of digital cameras and the development of digital photography technologies, the processes of data sharing commonly concern substantial amounts of data that may contain critical or private information. So, how to design a secure and efficient secret image sharing (SIS) method suitable for IoT apparatuses is attracting ever-increasing attention. With the aim of simultaneously realizing SIS, image data compression, and critical information or privacy protection, this article proposes a multigroup SIS method based on the model of compressed sensing (CS) and chaos theory. In the proposed method, participants of SIS are classified into two groups with different authorization levels. Solely members of full-authorized groups could reconstruct secret images with visible critical sectors. Members of restricted-authorized groups, however, could merely obtain reconstruction results with critical sectors concealed. The CS model is introduced to the proposed method to accomplish image data compression and applications of the chaos theory contribute to the secure data transmission. Experimental simulations and theoretical analyses are performed to discuss the feasibility, flexibility, and security of the proposed method.

With the rapid development of the Internet of Things (IoT), a substantial number of mobile devices may need to be connected to and communicate with the other devices. IoT smart nodes, which play a key role in these processes, provide not only connectivity but also data security during data collection and transmission. While existing research has considered security issues concerning data collection and transmission to some extent, the inherent vulnerabilities of smart nodes themselves have rarely received adequate attention. Actually, smart nodes can be susceptible to malware attacks, and the fundamental task to protect them is the timely and accurate identification of malware. However, the stealthy nature of malware, the imbalance in its types, and the sharp increase in malware variants make this task challenging. Existing static malware classification methods, which require known malware features, may struggle with a whole new set of malware variants. Dynamic methods plausibly have the potential to identify malware variants but necessitate running malware beforehand. In this article, we propose a deep-learning-based malware classification method designed to help most smart IoT nodes running mainstream operating systems preventing attacks from the malware and its variants. The proposed method incorporates an automatically adjusting threshold mechanism to extract trivial features of malware, refining the classification process even if original features are modified. It also performs well with insufficient malware samples and is compatible with most mainstream convolutional neural network (CNN) models allowing flexible implements. Experiments concerning byte-level variants, operation-code-level variants, and noise-caused variants are conducted. The results show that for the malware files and their variants, the proposed method can accurately accomplish the malware classification tasks. In addition, the proposed method offers a robust solution for the malware classification, which helps to enhance the security of smart nodes against a wide range of malware and its variants. Last but not least, its compatibility with mainstream CNN models and flexibility in deployment make it a practical approach for real-world applications.

With the rapid development of the Internet of Things (IoT), the sophistication and intelligence of sensors are continually evolving, playing increasingly important roles in smart homes, industrial automation, and remote healthcare. However, these intelligent sensors face many security threats, particularly from malware attacks. Identifying and classifying malware is crucial for preventing such attacks. As the number of sensors and their applications grow, malware targeting sensors proliferates. Processing massive malware samples is challenging due to limited bandwidth and resources in IoT environments. Therefore, compressing malware samples before transmission and classification can improve efficiency. Additionally, sharing malware samples between classification participants poses security risks, necessitating methods that prevent sample exploitation. Moreover, the complex network environments also necessitate robust classification methods. To address these challenges, this paper proposes CSMC (Compressed Sensing Malware Classification), an efficient malware classification method based on compressed sensing. This method compresses malware samples before sharing and classification, thus facilitating more effective sharing and processing. By introducing deep learning, the method can extract malware family features during compression, which classical methods cannot achieve. Furthermore, the irreversibility of the method enhances security by preventing classification participants from exploiting malware samples. Experimental results demonstrate that for malware targeting Windows and Android operating systems, CSMC outperforms many existing methods based on compressed sensing and machine or deep learning. Additionally, experiments on sample reconstruction and noise demonstrate CSMC’s capabilities in terms of security and robustness.