The Internet of Things (IoT) benefits our lives by integrating physical devices to the real world and offers a crucial internet infrastructure for future civilization. Because IoT devices are widely distributed and restricted in resources, it is difficult for them to adopt traditional security methods to resist malicious attacks. Unauthorized access to IoT devices, which results in severe privacy and security problems, has become a major challenge that has impeded IoT technology from being widely adopted. Therefore, the access control for IoT devices urgently needs to be improved when dealing with authorization issues. In this paper, we propose an attribute-based access control scheme that provides decentralized, flexible, and fine-grained authorization for IoT devices. Blockchain is utilized to provide authentic and reliable credentials. More importantly, a verifiable collaboration mechanism is designed to meet the needs of controlled access authorization in emergencies. Authority nodes are constructed to execute major computation tasks and interact with the blockchain. The security analysis shows that our scheme can reliably guarantee the security of authorized access. More than security assurance, a proof-of-concept prototype has been implemented to prove that our scheme is scalable, efficient, and accommodates IoT devices well.

With the rapid development of the Internet, malware traffic is seriously endangering the security of cyberspace. Convolutional neural networks (CNNs)-based malware traffic classification can automatically learn features from raw traffic, avoiding the inaccuracy of hand-design traffic features. Through the experiments and comparisons of LeNet, AlexNet, VGGNet, and ResNet, it is found that LeNet has good and stable classification ability for malware traffic and normal traffic. Then, a field programmable gate array (FPGA) accelerator for CNNs-based malware traffic classification is designed, which consists of a parameterized hardware accelerator and a fully automatic software framework. By fully exploring the parallelism between CNN layers, parallel computation and pipeline optimization are used in the hardware design to achieve high performance. Simultaneously, runtime reconfigurability is implemented by using a global register list. By encapsulating the underlying driver, a three-layer software framework is provided for users to deploy their pre-trained models. Finally, a typical CNNs-based malware traffic classification model was selected to test and verify the hardware accelerator. The typical application system can classify each traffic image from the test dataset in 18.97 μs with the accuracy of 99.77%, and the throughput of the system is 411.83 Mbps.