In recent years, with the continuous development of the Internet of Things, radio frequency identification (RFID) technology has also been widely concerned. The computing power of low cost tags is limited because of their high hardware requirements. Symmetric encryption algorithms and asymmetric encryption algorithms, such as RSA, DES, AES, etc., cannot be suitable for low cost RFID protocols. Therefore, research on RFID security authentication protocols with low cost and high security has become a focus. Recently, an ultralightweight RFID authentication protocol LP2UF was proposed to provide security and prevent all possible attacks. However, it is discovered that a type of desynchronization attack can successfully break the proposed scheme. To overcome the vulnerability against desynchronization attacks, we propose here a new ultra-lightweight RFID two-way authentication protocol based on stream cipher technology that uses only XOR. The stream cipher is employed to ensure security between readers and tags. Analysis shows that our protocol can effectively resist position tracking attacks, desynchronization attacks, and replay attacks.

This paper presents a conformal retrodirective metagrating with multi-azimuthal-angle operating ability. First, a flat metagrating composed of a periodic array of single rectangular patch elements, two-layer stacked substrates, and a ground plane is implemented to achieve one-directional retroreflection at a specific angle. The elevation angle of the retroreflection is manipulated by precisely tuning the value of the period. To control the energy coupling to the retrodirective mode, the dimensions of the length and width of the rectangular patch are investigated under the effect of changing the substrate thickness. Three values of the length, width, and thickness are then chosen to obtain a high retroreflection power efficiency. Next, to create a conformal design operating simultaneously at multiple azimuthal angles, the rectangular patch array using a flexible ultra-thin guiding layer is conformed to a dielectric cylindrical substrate backed by a perfect electric conductor ground plane. Furthermore, to further optimize the retroreflection efficiency, two circular metallic plates are added at the two ends of the cylindrical substrate to eliminate the specular reflection inside the space of the cylinder. The measured radar cross-section shows a power efficiency of the retrodirective metagrating of approximately 91% and 93% for 30° retrodirected elevation angle at the azimuthal angles of 0° and 90°, respectively, at 5.8GHz.

A model extraction attack is a security issue in deep neural networks (DNNs). Information on a trained DNN model is an attractive target for an adversary not only in terms of intellectual property but also of security. Thus, an adversary tries to reveal the sensitive information contained in the trained DNN model from machine-learning services. Previous studies on model extraction attacks assumed that the victim provides a machine-learning cloud service and the adversary accesses the service through formal queries. However, when a DNN model is implemented on an edge device, adversaries can physically access the device and try to reveal the sensitive information contained in the implemented DNN model. We call these physical model extraction attacks model reverse-engineering (MRE) attacks to distinguish them from attacks on cloud services. Power side-channel analyses are often used in MRE attacks to reveal the internal operation from power consumption or electromagnetic leakage. Previous studies, including ours, evaluated MRE attacks against several types of DNN processors with power side-channel analyses. In this paper, information leakage from a systolic array which is used for the matrix multiplication unit in the DNN processors is evaluated. We utilized correlation power analysis (CPA) for the MRE attack and reveal weight parameters of a DNN model from the systolic array. Two types of the systolic array were implemented on field-programmable gate array (FPGA) to demonstrate that CPA reveals weight parameters from those systolic arrays. In addition, we applied an extended analysis approach called “chain CPA” for robust CPA analysis against the systolic arrays. Our experimental results indicate that an adversary can reveal trained model parameters from a DNN accelerator even if the DNN model parameters in the off-chip bus are protected with data encryption. Countermeasures against side-channel leaks will be important for implementing a DNN accelerator on a FPGA or application-specific integrated circuit (ASIC).

Aiming at the problems of traditional algorithms that require high prior knowledge and weak timeliness, this paper proposes an emergency communication network topology planning method based on deep reinforcement learning. Based on the characteristics of the emergency communication network, and drawing on chess, we map the node layout and topology planning problems in the network planning to chess game problems; The two factors of network coverage and connectivity are considered to construct the evaluation criteria for network planning; The method of combining Monte Carlo tree search and self-game is used to realize network planning sample data generation, and the network planning strategy network and value network structure based on residual network are designed. On this basis, the model was constructed and trained based on Tensorflow library. Simulation results show that the proposed planning method can effectively implement intelligent planning of network topology, and has excellent timeliness and feasibility.

Web-based social engineering (SE) attacks manipulate users to perform specific actions, such as downloading malware and exposing personal information. Aiming to effectively lure users, some SE attacks, which we call multi-step SE attacks, constitute a sequence of web pages starting from a landing page and require browser interactions at each web page. Also, different browser interactions executed on a web page often branch to multiple sequences to redirect users to different SE attacks. Although common systems analyze only landing pages or conduct browser interactions limited to a specific attack, little effort has been made to follow such sequences of web pages to collect multi-step SE attacks. We propose STRAYSHEEP, a system to automatically crawl a sequence of web pages and detect diverse multi-step SE attacks. We evaluate the effectiveness of STRAYSHEEP's three modules (landing-page-collection, web-crawling, and SE-detection) in terms of the rate of collected landing pages leading to SE attacks, efficiency of web crawling to reach more SE attacks, and accuracy in detecting the attacks. Our experimental results indicate that STRAYSHEEP can lead to 20% more SE attacks than Alexa top sites and search results of trend words, crawl five times more efficiently than a simple crawling module, and detect SE attacks with 95.5% accuracy. We demonstrate that STRAYSHEEP can collect various SE attacks, not limited to a specific attack. We also clarify attackers' techniques for tricking users and browser interactions, redirecting users to attacks.

A fully homomorphic encryption (FHE) would be the important cryptosystem as the basic scheme for the cloud computing. Since Gentry discovered in 2009 the first fully homomorphic encryption scheme, some fully homomorphic encryption schemes were proposed. In the systems proposed until now the bootstrapping process is the main bottleneck and the large complexity for computing the ciphertext is required. In 2011 Zvika Brakerski et al. proposed a leveled FHE without bootstrapping. But circuit of arbitrary level cannot be evaluated in their scheme while in our scheme circuit of any level can be evaluated. The existence of an efficient fully homomorphic cryptosystem would have great practical implications in the outsourcing of private computations, for instance, in the field of the cloud computing. In this paper, IND-CCA1secure FHE based on the difficulty of prime factorization is proposed which does not need the bootstrapping and it is thought that our scheme is more efficient than the previous schemes. In particular the computational overhead for homomorphic evaluation is O(1).

Multivariate public key cryptosystem (MPKC) is one of the major post quantum cryptosystems (PQC), and the National Institute of Standards and Technology (NIST) recently selected four MPKCs as candidates of their PQC. The security of MPKC depends on the hardness of solving systems of algebraic equations over finite fields. In particular, the multivariate quadratic (MQ) problem is that of solving such a system consisting of quadratic polynomials and is regarded as an important research subject in cryptography. In the Fukuoka MQ challenge project, the hardness of the MQ problem is discussed, and algorithms for solving the MQ problem and the computational results obtained by these algorithms are reported. Algorithms for computing Gröbner basis are used as the main tools for solving the MQ problem. For example, the F4 algorithm and M4GB algorithm have succeeded in solving many instances of the MQ problem provided by the project. In this paper, based on the F4-style algorithm, we present an efficient algorithm to solve the MQ problems with dense polynomials generated in the Fukuoka MQ challenge project. We experimentally show that our algorithm requires less computational time and memory for these MQ problems than the F4 algorithm and M4GB algorithm. We succeeded in solving Type II and III problems of Fukuoka MQ challenge using our algorithm when the number of variables was 37 in both problems.

We propose a video authentication scheme to verify whether a given video file is recorded by a camera device or touched by a video editing tool. The proposed scheme prepares software characteristics of camera devices and video editing tools in advance, and compares them with the metadata of the given video file. Through practical implementation, we show that the proposed scheme has benefits of fast analysis time, high accuracy and full automation.

In this study, we investigate fundamental trade-off among identification, secrecy, template, and privacy-leakage rates in biometric identification system. Ignatenko and Willems (2015) studied this system assuming that the channel in the enrollment process of the system is noiseless and they did not consider the template rate. In the enrollment process, however, it is highly considered that noise occurs when bio-data is scanned. In this paper, we impose a noisy channel in the enrollment process and characterize the capacity region of the rate tuples. The capacity region is proved by a novel technique via two auxiliary random variables, which has never been seen in previous studies. As special cases, the obtained result shows that the characterization reduces to the one given by Ignatenko and Willems (2015) where the enrollment channel is noiseless and there is no constraint on the template rate, and it also coincides with the result derived by Günlü and Kramer (2018) where there is only one individual.

Incident ticket classification plays an important role in the complex system maintenance. However, low classification accuracy will result in high maintenance costs. To solve this issue, this paper proposes a fuzzy output support vector machine (FOSVM) based incident ticket classification approach, which can be implemented in the context of both two-class SVMs and multi-class SVMs such as one-versus-one and one-versus-rest. Our purpose is to solve the unclassifiable regions of multi-class SVMs to output reliable and robust results by more fine-grained analysis. Experiments on both benchmark data sets and real-world ticket data demonstrate that our method has better performance than commonly used multi-class SVM and fuzzy SVM methods.

The term “flash crowd” describes a situation in which a large number of users access a Web service simultaneously. Flash crowds, in particular, constitute a critical problem in e-commerce applications because of the potential for enormous economic damage as well as difficulty in management. Flash crowds can become more serious depending on users' behavior. When a flash crowd occurs, the delay in server response may cause users to retransmit their requests, thereby adding to the server load. In the present paper, we propose to use the psychological factors of the users for flash crowd mitigation. We aim to analyze changes in the user behavior by presenting feedback information. To evaluate the proposed method, we performed subject experiments and stress tests. Subject experiments showed that, by providing feedback information, the average number of request retransmissions decreased from 1.33 to 0.09, and the subjects that abandoned the service decreased from 81% to 0%. This confirmed that feedback information is effective in influencing user behavior in terms of abandonment and retransmission of requests. Stress tests showed that the average number of retransmissions decreased by 41%, and the proportion of abandonments decreased by 30%. These results revealed that the presentation of feedback information could mitigate the damage caused by flash crowds in real websites, although the effect is limited. The proposed method can be used in conjunction with conventional methods to handle flash crowds.

This paper proposes a salient chromagram by removing local trend to improve cover song identification accuracy. The proposed salient chromagram emphasizes tonal contents of music, which are well-preserved between an original song and its cover version, while reducing the effects of timber difference. We apply the proposed salient chromagram to the sequence-alignment based cover song identification. Experiments on two cover song datasets confirm that the proposed salient chromagram improves the cover song identification accuracy.

During the execution of software systems, their execution data can be recorded. By fully exploiting these data, software practitioners can discover behavioral models describing the actual execution of the underlying software system. The recorded unstructured software execution data may be too complex, spanning over several days, etc. Applying existing discovery techniques results in spaghetti-like models with no clear structure and no valuable information for comprehension. Starting from the observation that a software system is composed of a set of logical components, Liu et al. propose to decompose the software behavior discovery problem into smaller independent ones by discovering a behavioral model per component in [1]. However, the effectiveness of the proposed approach is not fully evaluated and compared with existing approaches. In this paper, we evaluate the quality (in terms of understandability/complexity) of discovered component behavior models in a quantitative manner. Based on evaluation, we show that this approach can reduce the complexity of the discovered model and gives a better understanding.

As characterizing videos simultaneously from spatial and temporal cues have been shown crucial for video processing, with the shortage of temporal information of soft assignment, the vector of locally aggregated descriptor (VLAD) should be considered as a suboptimal framework for learning the spatio-temporal video representation. With the development of attention mechanisms in natural language processing, in this work, we present a novel model with VLAD following spatio-temporal self-attention operations, named spatio-temporal self-attention weighted VLAD (ST-SAWVLAD). In particular, sequential convolutional feature maps extracted from two modalities i.e., RGB and Flow are receptively fed into the self-attention module to learn soft spatio-temporal assignments parameters, which enabling aggregate not only detailed spatial information but also fine motion information from successive video frames. In experiments, we evaluate ST-SAWVLAD by using competitive action recognition datasets, UCF101 and HMDB51, the results shcoutstanding performance. The source code is available at:https://github.com/badstones/st-sawvlad.

This paper reports our new communication components and downlink tests for realizing 2.65Gbps by utilizing two circular polarizations. We have developed an on-board X-band transmitter, an on-board dual circularly polarized-wave antenna, and a ground station. In the on-board transmitter, we optimized the bias conditions of GaN High Power Amplifier (HPA) to linearize AM-AM performance. We have also designed and fabricated a dual circularly polarized-wave antenna for low-crosstalk polarization multiplexing. The antenna is composed of a corrugated horn antenna and a septum-type polarizer. The antenna achieves Cross Polarization Discrimination (XPD) of 37-43dB in the target X-band. We also modify an existing 10m ground station antenna by replacing its primary radiator and adding a polarizer. We put the polarizer and Low Noise Amplifiers (LNAs) in a cryogenic chamber to reduce thermal noise. Total system noise temperature of the antenna is 58K (maximum) for 18K physical temperature when the angle of elevation is 90° on a fine winter day. The dual circularly polarized-wave ground station antenna has 39.0dB/K of Gain - system-noise Temperature ratio (G/T) and an XPD higher than 37dB. The downlinked signals are stored in a data recorder at the antenna site. Afterwards, we decoded the signals by using our non-real-time software demodulator. Our system has high frequency efficiency with a roll-off factor α=0.05 and polarization multiplexing of 64APSK. The communication bits per hertz corresponds to 8.41bit/Hz (2.65Gbit/315MHz). The system is demonstrated in orbit on board the RAPid Innovative payload demonstration Satellite (RAPIS-1). RAPIS-1 was launched from Uchinoura Space Center on January 19th, 2019. We decoded 1010 bits of downlinked R- and L-channel signals and found that the downlinked binary data was error free. Consequently, we have achieved 2.65Gbps communication speed in the X-band for earth observation satellites at 300 Mega symbols per second (Msps) and polarization multiplexing of 64APSK (coding rate: 4/5) for right- and left-hand circular polarizations.

Knowledge distillation approaches can transfer information from a large network (teacher network) to a small network (student network) to compress and accelerate deep neural networks. This paper proposes a novel knowledge distillation approach called multi-knowledge distillation (MKD). MKD consists of two stages. In the first stage, it employs autoencoders to learn compact and precise representations of the feature maps (FM) from the teacher network and the student network, these representations can be treated as the essential of the FM, i.e., EFM. In the second stage, MKD utilizes multiple kinds of knowledge, i.e., the magnitude of individual sample's EFM and the similarity relationships among several samples' EFM to enhance the generalization ability of the student network. Compared with previous approaches that employ FM or the handcrafted features from FM, the EFM learned from autoencoders can be transferred more efficiently and reliably. Furthermore, the rich information provided by the multiple kinds of knowledge guarantees the student network to mimic the teacher network as closely as possible. Experimental results also show that MKD is superior to the-state-of-arts.

We propose a novel framework for integrating beginners' machine operational experiences with those of experts' to obtain a detailed task model. Beginners can provide valuable information for operation guidance and task design; for example, from the operations that are easy or difficult for them, the mistakes they make, and the strategy they tend to choose. However, beginners' experiences often vary widely and are difficult to integrate directly. Thus, we consider an operational experience as a sequence of hand-machine interactions at hotspots. Then, a few experts' experiences and a sufficient number of beginners' experiences are unified using two aggregation steps that align and integrate sequences of interactions. We applied our method to more than 40 experiences of a sewing task. The results demonstrate good potential for modeling and obtaining important properties of the task.

The transition dynamics of a multistable tunnel-diode oscillator is characterized for modulating amplitude of outputted oscillatory signal. The base oscillator possesses fixed-point and limit-cycle stable points for a unique bias voltage. Switching these two stable points by external signal can render an efficient method for modulation of output amplitude. The time required for state transition is expected to be dominated by the aftereffect of the limiting point. However, it is found that its influence decreases exponentially with respect to the amplitude of external signal. Herein, we first describe numerically the pulse generation scheme with the transition dynamics of the oscillator and then validate it with several time-domain measurements using a test circuit.

New boundary integral equations are proposed for two-port slab waveguides which satisfy single mode condition. The boundary integral equations are combined with the orthogonality of guided mode and non-guided field. They are solved by the standard boundary element method with no use of mode expansion technique. Reflection and transmission coefficients of guided mode are directly determined by the boundary element method. To validate the proposed method, step waveguides for TE wave incidence and triangular rib waveguides for TM wave incidence are investigated by numerical calculations.

To be suitable in practice, pairings are typically carried out by two steps, which consist of the Miller loop and final exponentiation. To improve the final exponentiation step of a pairing on the BLS family of pairing-friendly elliptic curves with embedding degree 15, the authors provide a new representation of the exponent. The proposal can achieve a more reduction of the calculation cost of the final exponentiation than the previous method by Fouotsa et al.