More and more attacks are found due to the insecure channel between different network domains in legacy mobile network. In this letter, we discover an attack exploiting SUCI to track a subscriber in 5G network, which is directly caused by the insecure air channel. To cover this issue, a secure authentication scheme is proposed utilizing the existing PKI mechanism. Not only dose our protocol ensure the authentication signalling security in the channel between UE and SN, but also SN and HN. Further, formal methods are adopted to prove the security of the proposed protocol.

5G network will serve billions of people worldwide in the near future and protecting human privacy from being violated is one of its most important goals. In this paper, we carefully studied the 5G authentication protocols (namely 5G AKA and EAP-AKA') and a location sniffing attack exploiting 5G authentication protocols vulnerability is found. The attack can be implemented by an attacker through inexpensive devices. To cover this vulnerability, a fix scheme based on the existing PKI mechanism of 5G is proposed to enhance the authentication protocols. The proposed scheme is successfully verified with formal methods and automatic verification tool TAMARIN. Finally, the communication overhead, computational cost and storage overhead of the scheme are analyzed. The results show that the security of the fixed authentication protocol is greatly improved by just adding a little calculation and communication overhead.

System logs record system states and significant events at various critical points to help debug performance issues and failures. Therefore, the rapid and accurate detection of the system log is crucial to the security and stability of the system. In this paper, proposed is a novel attention-based neural network model, which would learn log patterns from normal execution. Concretely, our model adopts a GRU module with attention mechanism to extract the comprehensive and intricate correlations and patterns embedded in a sequence of log entries. Experimental results demonstrate that our proposed approach is effective and achieve better performance than conventional methods.

Analysis of satellite images requires classificatio of image objects. Since different categories may have almost the same brightness or feature in high dimensional remote sensing data, many object categories overlap with each other. How to segment the object categories accurately is still an open question. It is widely recognized that the assumptions required by many classification methods (maximum likelihood estimation, etc.) are suspect for textural features based on image pixel brightness. We propose an image feature based neural network approach for the segmentation of AVHRR images. The learning algoriothm is a modified backpropagation with gain and weight decay, since feedforward networks using the backpropagation algorithm have been generally successful and enjoy wide popularity. Destructive algorithms that adapt the neural architecture during the training have been developed. The classification accuracy of 100% is reached for a validation data set. Classification result is compared with that of Kohonen's LVQ and basic backpropagation algorithm based pixel-by-pixel method. Visual investigation of the result images shows that our method can not only distinguish the categories with similar signatures very well, but also is robustic to noise.

Existing cyber deception technologies (e.g., operating system obfuscation) can effectively disturb attackers' network reconnaissance and hide fingerprint information of valuable cyber assets (e.g., containers). However, they exhibit ineffectiveness against skilled attackers. In this study, a proactive fingerprint deception method is proposed, termed as Continuously Anonymizing Containers' Fingerprints (CACF), which modifies the container's fingerprint in the cloud resource pool to satisfy the anonymization standard. As demonstrated by experimental results, the CACF can effectively increase the difficulty for attackers.

We present a recovery scheme based on Self-protected Spanning Tree (SST), which recovers from failure all by itself. In the recovery scheme, the links are assigned birthdays to denote the order in which they are to be considered for adding to the SST. The recovery mechanism, named Birthday-based Link Replacing Mechanism (BLRM), is able to transform a SST into a new spanning tree by replacing some tree links with some non-tree links of the same birthday, which ensures the network connectivity after any single link or node failure. First, we theoretically prove that the SST-based recovery scheme can be applied to arbitrary two-edge connected or two connected networks. Then, the recovery time of BLRM is analyzed and evaluated using Ethernet, and the simulation results demonstrate the effectiveness of BLRM in achieving fast recovery. Also, we point out that BLRM provides a novel load balancing mechanism by fast changing the topology of the SST.

Although the classical vector median filter (VMF) has been widely used to suppress the impulse noise in the color image, many thin color curve pixels aligned in arbitrary directions are usually removed out as impulse noise. This serious problem can be solved by the proposed method that can protect the thin curves in arbitrary direction in color image and remove out the impulse noise at the same time. Firstly, samples in the 3x3 filter window are considered to preliminarily detect whether the center pixel is corrupted by impulse noise or not. Then, samples outside a 5x5 filter window are conditionally and partly considered to accurately distinguish the impulse noise and the noise-free pixel. At last, based on the previous outputs, samples on the processed positions in a 3x3 filter window are chosen as the samples of VMF operation to suppress the impulse noise. Extensive experimental results indicate that the proposed algorithm can be used to remove the impulse noise of color image while protecting the thin curves in arbitrary directions.

A low-power low-noise intermediate-frequency (IF) circuit is proposed for Gaussian frequency shift keying (GFSK) low-IF receivers. The proposed IF circuit is realized by an all-analog architecture composed of a couple of limiting amplifiers (LAs) and received signal strength indicators (RSSIs), a couple of band-pass filters (BPFs), a frequency detector (FD), a low-pass filter (LPF) and a slicer. The LA and RSSI are realized by an optimized combination of folded amplifiers and current subtractor based rectifiers to avoid the process induced depressing on accuracy. In addition, taking into account the nonlinearity and static current of rectifiers, we propose an analytical model as an accurate approximation of RSSIs' transfer character. An active-RC based GFSK demodulation scheme is proposed, and then both low power consumption and a large dynamic range are obtained. The chip is implemented with HJTC 0.18 µm CMOS technology and measured under an intermediate frequency of 200 kHz, a data rate of 100 kb/s and a modulation index of 1. The RSSI has a dynamic range of 51 dB with a logarithmic linearity error of less than 1 dB, and the slope is 23.9 mV/dB. For 0.1% bit error ratio (BER), the proposed IF circuit has the minimum input signal-to-noise ratio (SNR) of 5 dB and an input dynamic range of 55.4 dB, whereas it can tolerate a frequency offset of -3%+9.5% at 6 dB input SNR. The total power consumption is 5.655.89 mW.

We review our recent achievements in monolithic 3D-ICs and flexible electronics based on single-grain Si TFTs that are fabricated inside a single-grain with a low-temperature process. Based on pulsed-laser crystallization and submicron sized cavities made in the substrate, amorphous-Si precursor film was converted into poly-Si having grains that are formed on predetermined positions. Using the method called µ-Czochralski process and LPCVD a-Si precursor film, two layers of the SG Si TFT layers with the grains having a diameter of 6µm were vertically stacked with a maximum process temperature of 550°C. Mobility for electrons and holes were 600cm2/Vs and 200cm2/Vs, respectively. As a demonstration of monolithic 3D-ICs, the two SG-TFT layers were successfully implemented into CMOS inverter, 3D 6T-SRAM and single-grain lateral PIN photo-diode with in-pixel amplifier. The SG Si TFTs were applied to flexible electronics. In this case, the a-Si precursor was prepared by doctor-blade coating of liquid-Si based on pure cyclopentasilane (CPS) on a polyimide (PI) substrate with maximum process temperature of 350°C. The µ-Czochralski process provided location-controlled Si grains with a diameter of 3µm and mobilities of 460 and 121cm2/Vs for electrons and holes, respectively, were obtained. The devices on PI were transferred to a plastic foil which can operate with a bending diameter of 6mm. Those results indicate that the SG TFTs are attractive for their use in both monolithic 3D-ICs and flexible electronics.

Objects tracking methods have been wildly used in the field of video surveillance, motion monitoring, robotics and so on. Particle filter is one of the promising methods, but it is difficult to apply to real-time objects tracking because of its high computation cost. In order to reduce the processing cost without sacrificing the tracking quality, this paper proposes a new method for real-time 3D objects tracking, using parallelized particle filter algorithms by MapReduce architecture which is running on GPGPU. Our methods are as follows. First, we use a Kinect to get the 3D information of objects. Unlike the conventional 2D-based objects tracking, 3D objects tracking adds depth information. It can track not only from the x and y axis but also from the z axis, and the depth information can correct some errors in 2D objects tracking. Second, to solve the high computation cost problem, we use the MapReduce architecture on GPGPU to parallelize the particle filter algorithm. We implement the particle filter algorithms on GPU and evaluate the performance by actually running a program on CUDA5.5.

Network embedding has attracted an increasing amount of attention in recent years due to its wide-ranging applications in graph mining tasks such as vertex classification, community detection, and network visualization. Network embedding is an important method to learn low-dimensional representations of vertices in networks, aiming to capture and preserve the network structure. Almost all the existing network embedding methods adopt the so-called Skip-gram model in Word2vec. However, as a bag-of-words model, the skip-gram model mainly utilized the local structure information. The lack of information metrics for vertices in global network leads to the mix of vertices with different labels in the new embedding space. To solve this problem, in this paper we propose a Network Representation Learning method with Deep Metric Learning, namely DML-NRL. By setting the initialized anchor vertices and adding the similarity measure in the training progress, the distance information between different labels of vertices in the network is integrated into the vertex representation, which improves the accuracy of network embedding algorithm effectively. We compare our method with baselines by applying them to the tasks of multi-label classification and data visualization of vertices. The experimental results show that our method outperforms the baselines in all three datasets, and the method has proved to be effective and robust.