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Covert channels have been an effective means for leaking confidential information across security domains and numerous studies are available on typical covert channels attacks and defenses. Existing covert channel threat restriction solutions are based on the threat estimation criteria of covert channels such as capacity, accuracy, and short messages which are effective in evaluating the information transmission ability of a covert (storage) channel. However, these criteria cannot comprehensively reflect the key factors in the communication process such as shared resources and synchronization and therefore are unable to evaluate covertness and complexity of increasingly upgraded covert storage channels. As a solution, the anti-detection criterion was introduced to eliminate these limitations of cover channels. Though effective, most threat restriction techniques inevitably incur high performance overhead and hence become impractical. In this work, we avoid such overheads and present a restriction algorithm based on the anti-detection criterion to restrict threats that are associated with covert storage channels in virtual machines while maintaining the resource efficiency of the systems. Experimental evaluation shows that our proposed solution is able to counter covert storage channel attacks in an effective manner. Compared with Pump, a well-known traditional restriction algorithm used in practical systems, our solution significantly reduces the system overhead.

The dietary needs of elderly and the interaction mode of kitchen appliances are studied, and the interaction design principles of ageing-friendly kitchen appliances are proposed. Firstly, through questionnaire and interview, elderly users’ healthy diet needs and emotional demands are analyzed. Secondly, typical interaction tasks are constructed to analyze the interaction problems of elderly in health information search and cooking of kitchen appliances. Finally, the elderly user requirement hierarchy is constructed, and the user requirement weight values are analyzed by Likert scale and SPSS to derive the priority of design factors. Ageing-friendly kitchen appliances with healthy diet and emotional appeal are used as the functional basis. Ensure that the product interface is readable, the interaction logic is simple, and the feedback is significant. Based on these, the feasibility of healthy eating for elderly can be enhanced.

With the high prevalence of breast cancer, it is urgent to find out the intrinsic difference between various subtypes, so as to infer the underlying mechanisms. Given the available multi-omics data, their proper integration can improve the accuracy of breast cancer subtype recognition. In this study, DeepMO, a model using deep neural networks based on multi-omics data, was employed for classifying breast cancer subtypes. Three types of omics data including mRNA data, DNA methylation data, and copy number variation (CNV) data were collected from The Cancer Genome Atlas (TCGA). After data preprocessing and feature selection, each type of omics data was input into the deep neural network, which consists of an encoding subnetwork and a classification subnetwork. The results of DeepMO based on multi-omics on binary classification are better than other methods in terms of accuracy and area under the curve (AUC). Moreover, compared with other methods using single omics data and multi-omics data, DeepMO also had a higher prediction accuracy on multi-classification. We also validated the effect of feature selection on DeepMO. Finally, we analyzed the enrichment gene ontology (GO) terms and biological pathways of these significant genes, which were discovered during the feature selection process. We believe that the proposed model is useful for multi-omics data analysis.

Object detectors based on CNN are now able to achieve satisfactory accuracy, but their ability to deal with some targets with geometric deformation or occlusion is often poor. This is largely due to the fixed geometric structure of the convolution kernel and the single inflexible network structure. In our work, we use dual branch parallel processing to extract the different features of the target area to coordinate the prediction. To further enhance the performance of the network, this study rebuilds the feature extraction module. Finally, our detector learns to adapt to a variety of different shapes and sizes. The proposed method achieves up to 81.76% mAP on the Pascal VOC2007 dataset and 79.6% mAP on the Pascal VOC2012 dataset.

Covert channels are widely considered as a major risk of information leakage in various operating systems, such as desktop, cloud, and mobile systems. The existing works of modeling covert channels have mainly focused on using finite state machines (FSMs) and their transforms to describe the process of covert channel transmission. However, a FSM is rather an abstract model, where information about the shared resource, synchronization, and encoding/decoding cannot be presented in the model, making it difficult for researchers to realize and analyze the covert channels. In this paper, we use the high-level Petri Nets (HLPN) to model the structural and behavioral properties of covert channels. We use the HLPN to model the classic covert channel protocol. Moreover, the results from the analysis of the HLPN model are used to highlight the major shortcomings and interferences in the protocol. Furthermore, we propose two new covert channel models, namely: (a) two channel transmission protocol (TCTP) model and (b) self-adaptive protocol (SAP) model. The TCTP model circumvents the mutual inferences in encoding and synchronization operations; whereas the SAP model uses sleeping time and redundancy check to ensure correct transmission in an environment with strong noise. To demonstrate the correctness and usability of our proposed models in heterogeneous environments, we implement the TCTP and SAP in three different systems: (a) Linux, (b) Xen, and (c) Fiasco.OC. Our implementation also indicates the practicability of the models in heterogeneous, scalable and flexible environments.

Data centers provides both the applications, systems software and the hardware as services over the Internet, which is named cloud computing [1–3]. It is core infrastructure of cloud computing, supporting dynamic deployment and elastic resource management. With the powerful computing and storing capabilities, cloud computing has become increasingly popular [4, 5]. The fundamental mechanism of cloud is virtualization which allows virtual machines (VM) instantiate stand-alone operating systems on demand based on a software layer called virtual machine monitor (VMM) or hypervisor [6]. Although the virtualization technology provides strong isolation for the cloud, security and privacy are always the open problems [7]. Some of the problems are essentially traditional web application and data-hosting ones, e.g., phishing, downtime, data loss, and password weakness. One of the new problems introduced by the shared environment to cloud computing is the covert channel attack [8]. By this way, information is leaked from the data centers and meanwhile the security provided by isolation is breaken down [9, 10].

Continuing bug reports and exploits in hyper visors indicate that hyper visors face similar integrity threats as tradition software. Previous approaches to protect a hyper visor that utilize hardware features are not easy to be extended. Besides, they mainly focus on code or control data integrity, without pay much attention to protecting non-control data. In this paper, we present Hyper Verify, a novel architecture to monitor hyper visor non-control data using a trusted VM. Since a VM cannot directly access a hyper visor's memory, Hyper Verify programs a popular device driver to read the hyper visor's hardware state in the trusted VM. Then a memory analysis library is used to translate the low-level hardware state into the high level hyper visor context. Several monitoring processes use such context to monitor hyper visor non-control data integrity. Each of the processes is responsible for monitoring one kind of non-control data. It is flexible for Hyper Verify to support monitoring new kinds of data structure. The experimental evaluation of our prototype shows that Hyper Verify incurs at most 4% performance overhead to end users.

Covert channel has been studied for years due to its ability to divulge sensitive information in computer systems. Constructing covert communication scenarios is the first step to learn the threat of a channel. There are several challenges in the existing design of covert channel communications: lacking general communicating model description, low transmission accuracy and weak anti-interference ability. In this paper, we explore how to construct robust and efficient covert channel communications in operating systems. Firstly, we design three general covert communicating protocol models: the Basic Protocol (BP), the Two-Channel Transmission Protocol (TCTP) and the Self-Adaptive Protocol (SAP). Then we implement them in Linux operating systems. To simulate real attack scenarios, a toy Trojan program extracting passwords to cooperate with the covert protocols is presented. To identify potential covert channels in Linux kernel, we use Directed Information Flow Graph (DIFG) to analyze the source code and choose last_pid and temporary files channels in our implementation. Finally we evaluate the transmitting rate and accuracy of the three protocols. The results demonstrate that without special protective measures, the TCTP can achieve rather high accuracy and rate (100% and 31bps in our lab). When equipped with some restricting or interfering mechanisms, the SAP can achieve 97% accuracy and 18bps rate. This result reveals that attackers can bypass countermeasures to steal sensitive data from victims by well-designed covert protocols.

Cloud computing security has become the focus in information security, where much attention has been drawn to the user privacy leakage. Although isolation and some other security policies have been provided to protect the security of cloud computing, confidential information can be still stolen by timing channels without being detected. In this paper, a new method named XenPump is presented aiming to mitigate the threat of the timing channels by adding latency. XenPump is designed as a module located in hypervisor, monitoring the hypercalls used by the timing channels and adding latencies to lower the threat into an acceptable level. The prototype of XenPump has been implemented in Xen virtualization platform, and the performance is evaluated by the shared memory based timing channel. The experiment results show that XenPump can mitigate the threat of the timing channel by interrupting both the capacity and transmission accuracy. It is believed that after small extension, XenPump can mitigate the incoming timing channels.

In this paper, I develop a new approach to improve the Contradiction Detection (CD) incorporating the additional information from the context, which aimed at improving the CD precision. My work focuses on using additional context from the sentences or pages to disambiguate the event/entity. I give a new method but the probability to do the ambiguity examination. What’s more, I develop a CD system which inspired by AUCONTRAIRE CD system and give the concrete steps to achieve the goal.