Cloud storage has been used to reduce the cost and support convenient collaborations for Industrial Internet of Things (IIoT) data management. When data owners share IIoT data with authorized parties for data interaction, secure cloud data searching and file access control are fundamental security requirements. In this paper, first we discuss a new insider attack to the Cui's multi-key aggregate searchable encryption scheme, where the unauthorized inside users can guess the other users private keys. Then, we propose a novel file-centric multi-key aggregate keyword searchable encryption (Fc-MKA-KSE) system for the IIoT data in the file-centric framework. Specifically, we present two formal security models, namely the security models of the indistinguishable selective-file chosen keyword attack (IND-sF-CKA) and the indistinguishable selective-file keyword guessing attack (IND-sF-KGA), which can satisfy the security requirements. Our experimental results show that the proposed scheme achieves computational efficiency.

Biological networks, such as genetic regulatory networks, often contain positive and negative feedback loops that settle down to dynamically stable patterns. Identifying these patterns, the so-called attractors, can provide important insights for biologists to understand the molecular mechanisms underlying many coordinated cellular processes such as cellular division, differentiation, and homeostasis. Both synchronous and asynchronous Boolean networks have been used to simulate genetic regulatory networks and identify their attractors. The common methods of computing attractors are that start with a randomly selected initial state and finish with exhaustive search of the state space of a network. However, the time complexity of these methods grows exponentially with respect to the number and length of attractors. Here, we build two algorithms to achieve the computation of attractors in synchronous and asynchronous Boolean networks. For the synchronous scenario, combing with iterative methods and reduced order binary decision diagrams (ROBDD), we propose an improved algorithm to compute attractors. For another algorithm, the attractors of synchronous Boolean networks are utilized in asynchronous Boolean translation functions to derive attractors of asynchronous scenario. The proposed algorithms are implemented in a procedure called geneFAtt. Compared to existing tools such as genYsis, geneFAtt is significantly faster in computing attractors for empirical experimental systems. The software package is available at https://sites.google.com/site/desheng619/download.

Career choice has a pivotal role in college students’ life planning. In the past, professional career appraisers used questionnaires or diagnoses to quantify the factors potentially influencing career choices. However, due to the complexity of each person’s goals and ideas, it is difficult to properly forecast their career choices. Recent evidence suggests that we could use students’ behavioral data to predict their career choices. Based on the simple premise that the most remarkable characteristics of classes are reflected by the main samples of a category, we propose a model called the Approach Cluster Centers Based On XGBOOST (ACCBOX) model to predict students’ career choices. The experimental results of predicting students’ career choices clearly demonstrate the superiority of our method compared to the existing state-of-the-art techniques by evaluating on 13 M behavioral data of over four thousand students.

In recent years, malware with strong concealment uses encrypted protocol to evade detection. Thus, encrypted traffic identification can help security analysts to be more effective in narrowing down those encrypted network traffic. Existing methods are protocol-independent, such as statistical-based and machine-learning-based approaches. Statistical-based approaches, however, are confined to payload length and machine-learning-based approaches have a low recognition rate for encrypted traffic using undisclosed protocols. In this paper, we proposed a Heuristic Statistical Testing (HST) approach that combines both statistics and machine learning and has been proved to alleviate their respective deficiencies. We manually selected four randomness tests to extract small payload features for machine learning to improve real-time performances. We also proposed a simple handshake skipping method called HST-R to increase the classification accuracy. We compared our approach with other identification approaches on a testing dataset consisting of traffic that uses two known, two undisclosed and one custom cryptographic protocols. Experimental results showed that HST-R performs better than other traditional coding-based, entropy-based and ML-based approaches. We also showed our handshake skipping method could generalize better for unknown cryptographic protocols. Finally, we also conducted experimental comparisons among different classification algorithms. The results showed that C4.5, with our method, has the highest identification accuracy for SSL and SSH traffic.

Semantic segmentation, which aims to acquire pixel-level understanding about images, is among the key components in computer vision. To train a good segmentation model for real-world images, it usually requires a huge amount of time and labor effort to obtain sufficient pixel-level annotations of real-world images beforehand. To get rid of such a nontrivial burden, one can use simulators to automatically generate synthetic images that inherently contain full pixel-level annotations and use them to train a segmentation model for the real-world images. However, training with synthetic images usually cannot lead to good performance due to the domain difference between the synthetic images (i.e., source domain) and the real-world images (i.e., target domain). To deal with this issue, a number of unsupervised domain adaptation (UDA) approaches have been proposed, where no labeled real-world images are available. Different from those methods, in this work, we conduct a pioneer attempt by using easy-to-collect image-level annotations for target images to improve the performance of cross-domain segmentation. Specifically, we leverage those image-level annotations to construct curriculums for the domain adaptation problem. The curriculums describe multi-level properties of the target domain, including label distributions over full images, local regions and single pixels. Since image annotations are “weak” labels compared to pixel annotations for segmentation, we coin this new problem as weakly-supervised cross-domain segmentation. Comprehensive experiments on the GTA5 → Cityscapes and SYNTHIA → Cityscapes settings demonstrate the effectiveness of our method over the existing state-of-the-art baselines.

Massive physical devices are deployed in the Industrial Internet of Things (IoT) to collect ambiance data while heavy storage and communication cost are imposed on these IoT devices. To overcome this constraint, cloud-assisted technologies are introduced to store and manage the collected data. In order to protect data quality and security, encryption is required before uploading data to remote clouds. Consequently, a search function is added to cloud services to find the specific data. However, traditional data searching schemes are constructed in user-oriented systems, where the search function is mainly involved with the relationship between data and users rather than data and devices. As a result, traditional search schemes are not suitable to find special IoT devices. On the other hand, the status of these devices is described by many attributes, e.g., temperature, clean water storage, and machine speed in an early warning system for industrial sewage disposal equipment. Hence, multi-keyword conjunctive queries for partial attributes should be introduced so as to find the target device more accurately and more efficiently. To address these challenges, we propose a new universal device-oriented keyword searchable encryption (Do-KSE) scheme for cloud-assisted IoT in this paper. Furthermore, the functions of a single and conjunctive keyword search are maintained to handle the device search requirement of partial attributes. We conduct extensive experiments to evaluate the proposed scheme. Experimental results show that our scheme has excellent performance because of the lightweight index and query trapdoor.