Covert communication in this regard has been widely used for protecting the secrecy of communication. Voice over Long Term Evolution (VoLTE) is a packet-switched core network solution for high-speed and high-quality end-to-end services which usually applied to the communication between intelligent systems. However, covert channels using inter-packet delays and packet order in VoLTE services are limited by specific rules. Since minor modifications to overt traffic can be detected, existing covert channel solutions cannot be directly applied to VoLTE. Therefore, this study presents a robust packet loss covert timing channel by cascade hash coding with intelligent system. To ensure robustness and undetectability, we design hash-based inter-codeword verification, codeword self-verification based on cyclic redundancy check, and adaptive mapping matrix. The sender modulates the covert messages according to the sequence number of the actively dropped packets, and the receiver can retrieve the covert messages using a specialized verification method. To evaluate undetectability, robustness, throughput, and construction costs, a large number of experiments in mobile intelligent terminals have been conducted. The experimental results prove that the proposed scheme is feasible for VoLTE communication, as the covert message is shown to be transmitted secretly, and the bit error and throughput are within acceptable ranges.

Machine learning algorithms are widely used for cybersecurity applications, include spam, malware detection. In these applications, the machine learning model has to face attack by adversarial samples. Therefore, how to train a robust machine learning model with small samples is a very hot research problem. portable document format (PDF) is a widely used file format, and often utilized as a vehicle for malicious behavior. There have been various PDF malware detectors based on machine learning. However, the labeling of large-scale data samples is time-consuming and laborious. This paper aims to reduce the size of training set while maintain the performance of detection. We propose a novel PDF malware detection method, using active learning to boost training. Particularly, we first make clear the meaning of uncertain samples in this paper, and theoretically explain the effectiveness of these uncertain samples for malware detection. Second, we present an active-learning based malware detection model, using mutual agreement analysis to choose the uncertain sample as the data augmentation. The detector is retrained according to the ground truth of the uncertain samples rather than the whole test samples in the previous epoch, which can not only improve the detection performance, but also reduce the training time consumption of the detector. We conduct 10 epochs of retraining experiments for comparison, using the uncertain samples and the whole test samples from the previous epoch respectively as training set augmentation. The experimental results show that our active-learning based model can achieve the same performance as the traditional model in the tenth epoch of retraining, while the former only needs to use one thirtieth of the latter's training samples.

Fifth-generation (5G) wireless systems provide an opportunity for improving the existing Voice over Internet Protocol communication service's user experience. To mitigate the security risk of 5G data leakage, building covert channel is an alternative approach of providing confidential data transmission. Due to the high transmission rate of 5G, the interpacket intervals become small and derandomized, this caused the encoding phase of the covert timing channel imports relatively large modulation errors. rearranging is a widespread phenomenon that is occurred over the data communications. In this paper, we propose a rearrangement covert channel approach named Hybrid Variable-length Packet Rearrangement Covert Timing Channel (HVPR-CTC), which artificially chooses the delimiter packets and identification (ID) packets from the overt traffics, and encodes the packet sending order between adjacent delimiter packets according to a generated hybrid variable-length codeword dictionary, and embeds the secret message by rearranging the sending order of the ID packets. The experiments demonstrate that the HVPR-CTC scheme can effectively perform strategy adjustment: its minimum Location Square Deviation is 0.832 and minimum Swap Deviation is 139. the maximum throughput reaches 14.37 bps, and the optimal Bit Error Rate is 3.27% and 9.70% for low-channel noise and high-channel noise communication conditions, respectively.

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.

Federated Learning (FL) is one of the key technologies to solve privacy protection for cloud-edge intelligent collaborative computing, and its security and privacy issues have attracted extensive attention from academia and industry. FL is a distributed privacy protection framework. Multiple edged nodes or servers jointly train a machine learning model by sharing model parameters without exchanging local data. However, there are still many security risks and privacy threats in FL in edge-cloud collaborative computing. In this paper, we mainly discuss the security and privacy challenges on FL in collaborative computing at the edge. First, we introduce the principle, classification, and threat model of FL in edge-cloud collaboration, which helps understand the challenges faced by edge-cloud collaborative computing. Second, privacy leakage attacks and poisoning attacks launched by adversaries or honest but curious actors are summarized and compared. Then, the problems existing on the attack method are summarized and analyzed. Finally, the future development direction of FL in the field of edge-cloud collaborative computing is further discussed.

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 recent cyber security attacks, the “border defense” security protection mechanism has often penetrated and broken through, and the “borderless” security defense idea—Zero Trust was proposed. The device application sandbox deployment model is one of the four essential Zero Trust architecture device deployment models. The isolation of the application sandbox directly affects the security of trusted applications. Given the security risks, such as sandbox escape in the sandbox application, we propose a hybrid isolation model based on access behavior and give the formal definition and security characteristics of the model. The model dynamically determines the security identity of the subject according to the access behavior and controls the access operation of the application sandbox. Therefore, the sandbox meets the characteristics of autonomous security, domain isolation, and integrity, ensuring that the system is always in an isolated safe state and easy to use. Finally, we implement the security model based on the container and Linux security module, and test the network and disk performance of this model. What is more, we make security comparison experiments based on the same container escape vulnerability. The experimental results show that the security model proposed in this paper effectively enhances the security of the device application sandboxing deployment model in Zero Trust architecture, and has a better performance compared with Container-SELinux.

Security monitoring and analysis can help users to timely perceive threats faced by the host, thereby protecting and backup data and improving the host's security status. In the research domain of host security analysis, many feasible solutions have been proposed. However, real-time performance and accuracy still need improvement. This paper proposes a host security analysis method based on Dempster–Shafer (D-S) evidence theory. It adopts three models of support vector regression, logistic regression, and K-nearest neighbor regression, as sensors for multisource information fusion. Multiple sensors perform security analysis on the host, respectively, and use the analysis results as evidence of D-S evidence theory. Experiments show that the proposed method provides effective security protection for the host in terms of absolute error, root mean square error, and the average absolute percentage error.