Abstract Knowledge base completion (KBC) aims to predict missing information in a knowledge base. From a data governance perspective, KBC is an important task not only in knowledge management but also in downstream knowledge base applications. The prosperity of mobile applications and online systems enables devices to generate an enormous volume of data containing valuable knowledge. However, these data are vast and contain noise, so utilizing them in KBC requires particular skill. In this paper, we propose information fusion generative adversarial network (IFGAN) to handle heterogeneous data. We design a bidirectional learning architecture including a graph convolutional neural network and graph attention network to learn contextual embeddings that fuse knowledge from a knowledge base and data generated by an application. For efficient negative sampling, we employ different kinds of convolution structures, such as depthwise separable convolution and involution in the generator of the network. The convolution structure is known to be suitable for collaborative computing and promises great potential with the progress of technology since the structure is extensible. We demonstrate the effectiveness of the proposed model on KB4Rec dataset, the evaluation metrics MRR and H@10 were improved compared with previous models; the code is available at https://github.com/Tianchen627/IFGAN .

It has been observed that the Internet of Medical Things (IoMT) is being deployed to construct varieties of intelligent platforms in medical and healthcare field, in order to comprehensively improve the quality of medical services. However, the cyber security of IoMT is facing enormous threat. Although many trust schemes are proposed to address the issue, the ignorance of aging weight in trust increases the risk of long-term attacks before being detected. In this paper, we design a dynamic aging weight scheme for trust model in IoMT. Essentially, when there are cooperative behaviors between two nodes, the aging weight can be set as large as possible to slow down the increase in the trust value of normal nodes. Once noncooperative behaviors appear, the smaller aging weight could mitigate the danger of compromised nodes. The simulation results indicate that our proposed scheme could better meet the principle of “Easy to lose” for trust.

Voice assistants (VAs) such as Siri and Google Now have become an increasingly popular human-machine interaction method and have made various systems voice controllable. Prior work on attacking voice assistants shows that the hidden voice commands that are incomprehensible to people can control the VAs. Hidden voice commands, though `hidden', are nonetheless audible. In this work, we design a completely inaudible attack, DolphinAttack, that modulates voice commands on ultrasonic carriers to achieve inaudibility. By leveraging the nonlinearity of the microphone circuits, the modulated low-frequency audio commands can be successfully demodulated, recovered, and more importantly interpreted by the voice assistants. We validate DolphinAttack on popular voice assistants, including Siri, Google Now, S Voice, HiVoice, Cortana, Alexa, etc. By injecting a sequence of inaudible voice commands, we show a few proof-of-concept attacks, which include activating Siri to initiate a FaceTime call on iPhone, activating Google Now to turn on the airplane mode, and even manipulating the navigation system in an Audi automobile. We propose hardware and software defense solutions. We validate that it is feasible to detect DolphinAttack by classifying the audios using supported vector machine (SVM), and suggest to re-design voice assistants to be resilient to inaudible voice command attacks.

With the proliferation of smart devices, children can be easily exposed to violent or adult-only content on the Internet. Without any precaution, the premature and unsupervised use of smart devices can be harmful to both children and their parents. Thus, it is critical to employ parent patrol mechanisms such that children are restricted to child-friendly content only. A successful parent patrol strategy has to be user friendly and privacy aware. The apps that require explicit actions from parents are not effective because a parent may forget to enable them, and the ones that use built-in cameras or microphones to detect child users may impose privacy violations. In this article, we propose iCare, a system that can identify child users automatically and seamlessly when users operate smartphones. In particular, iCare investigates the intrinsic differences of screen-touch patterns between child and adult users from the aspect of physiological maturity. We discover that one’s touch behaviors are related to his or her age. Thus, iCare records the touch behaviors and extracts hand geometry, finger dexterity, and hand stability features that capture the age information. We conduct experiments on 100 people including 62 children (3 to 17 years old) and 38 adults (18 to 59 years old). Results show that iCare can achieve 96.6% accuracy for child identification using only a single swipe on the screen, and the accuracy becomes 98.3% with three consecutive swipes.

Speech recognition (SR) systems such as Siri or Google Now have become an increasingly popular human-computer interaction method, and have turned various systems into voice controllable systems (VCS). Prior work on attacking VCS shows that the hidden voice commands that are incomprehensible to people can control the systems. Hidden voice commands, though "hidden", are nonetheless audible. In this work, we design a totally inaudible attack, DolphinAttack, that modulates voice commands on ultrasonic carriers (e.g., f > 20 kHz) to achieve inaudibility. By leveraging the nonlinearity of the microphone circuits, the modulated low-frequency audio commands can be successfully demodulated, recovered, and more importantly interpreted by the speech recognition systems. We validated DolphinAttack on popular speech recognition systems, including Siri, Google Now, Samsung S Voice, Huawei HiVoice, Cortana and Alexa. By injecting a sequence of inaudible voice commands, we show a few proof-of-concept attacks, which include activating Siri to initiate a FaceTime call on iPhone, activating Google Now to switch the phone to the airplane mode, and even manipulating the navigation system in an Audi automobile. We propose hardware and software defense solutions, and suggest to re-design voice controllable systems to be resilient to inaudible voice command attacks.