Secure data-sharing technology is a bridge for various collaborative operations among intelligent terminals in the edge-cloud collaborative application scenario. For the shared data involves different levels of confidentiality, intelligent terminals for collaborative operations may be distributed in multiple management domains, and the private information of intelligent terminals is easy to be leaked in edge-cloud collaboration scenarios, the security of data sharing is severely threatened. To solve these problems, this paper proposed a fine-grained and traceable multidomain secure data-sharing model for intelligent terminals. In this model, a key self-certification algorithm is proposed, which avoids potential security threats of key leakage during the key distribution process. The model combines attribute encryption and threshold function to achieve more fine-grained and more flexible secure data sharing; it uses blockchain technology to achieve integrity verification of stored data and traceability of shared data, and it combines on-chain and off-chain databases to achieve rapid retrieval and positioning of shared data distributed among multiple domains, which improves the efficiency of data sharing among domains. The security of the model proposed by us is proved, and compared with the cited literature, it is shown that the proposed model has certain advantages in terms of computational complexity and time consumption.

Recent researchers have shown that deep neural networks (DNNs) are vulnerable to adversarial exemplars, making them unsuitable for security-critical applications. Transferability of adversarial examples is crucial for attacking black-box models, which facilitates adversarial attacks in more practical scenarios. We propose a novel adversarial attack with high transferability. Unlike existing attacks that directly modify the input pixels, our attack is executed in the feature space. More specifically, we corrupt the abstract features by maximizing the feature distance between the adversarial example and clean images with a perceptual similarity network, inducing model misclassification. In addition, we apply a spectral transformation to the input, thus narrowing the search space in the frequency domain to enhance the transferability of adversarial examples. The disruption of crucial features in a specific frequency component achieves greater transferability. Extensive evaluations illustrate that our approach is easily compatible with many existing frameworks for transfer attacks and can significantly improve the baseline performance of black-box attacks. Moreover, we can obtain a higher fooling rate even if the model has a defense technique. We achieve a maximum black-box fooling rate of 61.70% on the defense model. Our work indicates that existing pixel space defense techniques are difficult to guarantee the robustness of the feature space, and the feature space from a frequency perspective is promising for developing more robust models.

Deep neural networks are highly vulnerable to adversarial examples, and these adversarial examples stay malicious when transferred to other neural networks. Many works exploit this transferability of adversarial examples to execute black-box attacks. However, most existing adversarial attack methods rarely consider cross-task black-box attacks that are more similar to real-world scenarios. In this paper, we propose a class of random blur-based iterative methods (RBMs) to enhance the success rates of cross-task black-box attacks. By integrating the random erasing and Gaussian blur into the iterative gradient-based attacks, the proposed RBM augments the diversity of adversarial perturbation and alleviates the marginal effect caused by iterative gradient-based methods, generating the adversarial examples of stronger transferability. Experimental results on ImageNet and PASCAL VOC data sets show that the proposed RBM generates more transferable adversarial examples on image classification models, thereby successfully attacking cross-task black-box object detection models.

With the widespread application of Industrial Internet of Things (IIoT) technology in the industry, the security threats are also increasing. To ensure the safe sharing of resources in IIoT, this paper proposes a data security sharing model based on privacy protection (DSS-PP) for blockchain-enabled IIoT. Compared with previous works, DSS-PP has obvious advantages in several important aspects: (1) In the process of identity authentication, it protects users' personal information by using authentication technology with hidden attributes; (2) the encrypted shared resources are stored in off-chain database of the blockchain, while only the ciphertext index information is stored in the block. It reduces the storage load of the blockchain; (3) it uses blockchain logging technology to trace and account for illegal access. Under the hardness assumption of Inverse Computational Diffe–Hellman (ICDH) problem, this model is proven to be correct and safe. Through the analysis of performance, DSS-PP has better performance than the referred works.

Android platform has been the target of attackers due to its openness and increasing popularity. Android malware has explosively increased in recent years, which poses serious threats to Android security. Thus proposing efficient Android malware detection methods is curial in defeating malware. Various features extracted from static or dynamic analysis using machine learning have played an important role in malware detection recently. However, existing code obfuscation, code encryption, and dynamic code loading techniques can be employed to hinder systems that single based on static analysis, purely dynamic analysis systems cannot detect all potential code execution paths. To address these issues, we propose CoDroid, a sequence-based hybrid Android malware detection method, which utilizes the sequences of static opcode and dynamic system call. We treat one sequence as a sentence in the natural language processing and construct a CNN–BiLSTM–Attention classifier which consists of Convolutional Neural Networks (CNNs), the Bidirectional Long Short-Term Memory (BiLSTM) with an attention language model. We extensively evaluate CoDroid under a real-world data set and perform comprehensive analysis against other existing related detection methods. The evaluations show the effectiveness and flexibility of CoDroid across a variety of experimental settings.