The increasingly sophisticated Android malware calls for new defensive techniques that are capable of protecting mobile users against novel threats. In this paper, we first extract the runtime Application Programming Interface (API) call sequences from Android apps, and then analyze higher-level semantic relations within the ecosystem to comprehensively characterize the apps. To model different types of entities (i.e., app, API, device, signature, affiliation) and rich relations among them, we present a structured heterogeneous graph (HG) for modeling. To efficiently classify nodes (e.g., apps) in the constructed HG, we propose the HG-Learning method to first obtain in-sample node embeddings and then learn representations of out-of-sample nodes without rerunning/adjusting HG embeddings at the first attempt. We later design a deep neural network classifier taking the learned HG representations as inputs for real-time Android malware detection. Comprehensive experiments on large-scale and real sample collections from Tencent Security Lab are performed to compare various baselines. Promising results demonstrate that our developed system AiDroid which integrates our proposed method outperforms others in real-time Android malware detection.
The explosive growth and increasing sophistication of Android malware call for new defensive techniques that are capable of protecting mobile users against novel threats. To combat the evolving Android malware attacks, systems of HinDroid and AiDroid have demonstrated the success of heterogeneous graph (HG) based classifiers in Android malware detection; however, their success may also incentivize attackers to defeat HG based models to bypass the detection. By far, there has no work on adversarial attack and/or defense on HG data. In this paper, we explore the robustness of HG based model in Android malware detection at the first attempt. In particular, based on a generic HG based classifier, (1) we first present a novel yet practical adversarial attack model (named HG-Attack) on HG data by considering Android malware attackers' current capabilities and knowledge; (2) to effectively combat the adversarial attacks on HG, we then propose a resilient yet elegant defense paradigm (named Rad-HGC) to enhance robustness of HG based classifier in Android malware detection. Promising experimental results based on the large-scale and real sample collections from Tencent Security Lab demonstrate the effectiveness of our developed system αCyber, which integrates our proposed defense model Rad-HGC that is resilient against practical adversarial malware attacks on the HG data performed by HG-Attack.
SummaryGlasses-free three-dimensional (3D) display is considered as a potential disruptive technology for display. The issue of visual fatigue, mainly caused by the inaccurate phase reconstruction in terms of image crosstalk, as well as vergence and accommodation conflict, is the critical obstacle that hinders the real applications of glasses-free 3D display. Here we propose a glasses-free 3D display by adopting metagratings for the pixelated phase modulation to form converged viewpoints. When the viewpoints are closely arranged, the holographic sampling 3D display can approximate a continuous light field. We demonstrate a video rate full-color 3D display prototype without visual fatigue under an LED white light illumination. The metagratings-based holographic sampling 3D display has a thin form factor and is compatible with traditional flat panel and thus has the potential to be used in portable electronics, window display, exhibition display, 3D TV, as well as tabletop display.
Without any special glasses, multiview 3D displays based on the diffractive optics can present high resolution, full-parallax 3D images in an ultra-wide viewing angle. The enabling optical component, namely the phase plate, can produce arbitrarily distributed view zones by carefully designing the orientation and the period of each nano-grating pixel. However, such 3D display screen is restricted to a limited size due to the time-consuming fabricating process of nano-gratings on the phase plate. In this paper, we proposed and developed a lithography system that can fabricate the phase plate efficiently. Here we made two phase plates with full nano-grating pixel coverage at a speed of 20 mm2/mins, a 500 fold increment in the efficiency when compared to the method of E-beam lithography. One 2.5-inch phase plate generated 9-view 3D images with horizontal-parallax, while the other 6-inch phase plate produced 64-view 3D images with full-parallax. The angular divergence in horizontal axis and vertical axis was 1.5 degrees, and 1.25 degrees, respectively, slightly larger than the simulated value of 1.2 degrees by Finite Difference Time Domain (FDTD). The intensity variation was less than 10% for each viewpoint, in consistency with the simulation results. On top of each phase plate, a high-resolution binary masking pattern containing amplitude information of all viewing zone was well aligned. We achieved a resolution of 400 pixels/inch and a viewing angle of 40 degrees for 9-view 3D images with horizontal parallax. In another prototype, the resolution of each view was 160 pixels/inch and the view angle was 50 degrees for 64-view 3D images with full parallax. As demonstrated in the experiments, the homemade lithography system provided the key fabricating technology for multiview 3D holographic display.
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