Graphene Channel Liquid Container Field Effect Transistor as pH Sensor

Graphene channel liquid container field effect transistor pH sensor with interdigital microtrench for liquid ion testing is presented. Growth morphology and pH sensing property of continuous few-layer graphene (FLG) and quasi-continuous monolayer graphene (MG) channels are compared. The experiment results show that the source-to-drain current of the graphene channel FET has a significant and fast response after adsorption of the measured molecule and ion at the room temperature; at the same time, the FLG response time is less than 4 s. The resolution of MG (0.01) on pH value is one order of magnitude higher than that of FLG (0.1). The reason is that with fewer defects, the MG is more likely to adsorb measured molecule and ion, and the molecules and ions can make the transport property change. The output sensitivities of MG are from 34.5% to 57.4% when the pH value is between 7 and 8, while sensitivity of FLG is 4.75% when the pH=7. The sensor fabrication combines traditional silicon technique and flexible electronic technology and provides an easy way to develop graphene-based electrolyte gas sensor or even biological sensors

A medical image encryption method based on chaos floating-point operation and its realization by FPGA

Aiming at the high confidentiality requirement of medical image data transmission in the Internet, an encryption method based on Logistic chaotic floating point number operation is proposed for medical image encryption. In this encryption method, PRNG based on Logistic chaos was designed with double-precision floating-point operations and described by the Verilog . The comprehensive design of encryption method is realized on the development platform of Cyclone IV series DE2-115 of Altera Corporation. The security of encryption algorithm is analyzed from the cryptographic perspectives such as key sensitivity test, histogram analysis, correlation test, information entropy processing, etc. By comparing with some existing image encryption algorithms, it is found that the image encrypted by this encryption algorithm has the characteristics of being sensitive to keys, small correlation coefficient and high information entropy. In addition, the FPGA-based hardware encryption system has high encryption stability and good real-time performance

Codeformer: A GNN-Nested Transformer Model for Binary Code Similarity Detection

Binary code similarity detection is used to calculate the code similarity of a pair of binary functions or files, through a certain calculation method and judgment method. It is a fundamental task in the field of computer binary security. Traditional methods of similarity detection usually use graph matching algorithms, but these methods have poor performance and unsatisfactory effects. Recently, graph neural networks have become an effective method for analyzing graph embeddings in natural language processing. Although these methods are effective, the existing methods still do not sufficiently learn the information of the binary code. To solve this problem, we propose Codeformer, an iterative model of a graph neural network (GNN)-nested Transformer. The model uses a Transformer to obtain an embedding vector of the basic block and uses the GNN to update the embedding vector of each basic block of the control flow graph (CFG). Codeformer iteratively executes basic block embedding to learn abundant global information and finally uses the GNN to aggregate all the basic blocks of a function. We conducted experiments on the OpenSSL, Clamav and Curl datasets. The evaluation results show that our method outperforms the state-of-the-art models

Binary code similarity analysis based on naming function and common vector space

Abstract Binary code similarity analysis is widely used in the field of vulnerability search where source code may not be available to detect whether two binary functions are similar or not. Based on deep learning and natural processing techniques, several approaches have been proposed to perform cross-platform binary code similarity analysis using control flow graphs. However, existing schemes suffer from the shortcomings of large differences in instruction syntaxes across different target platforms, inability to align control flow graph nodes, and less introduction of high-level semantics of stability, which pose challenges for identifying similar computations between binary functions of different platforms generated from the same source code. We argue that extracting stable, platform-independent semantics can improve model accuracy, and a cross-platform binary function similarity comparison model N_Match is proposed. The model elevates different platform instructions to the same semantic space to shield their underlying platform instruction differences, uses graph embedding technology to learn the stability semantics of neighbors, extracts high-level knowledge of naming function to alleviate the differences brought about by cross-platform and cross-optimization levels, and combines the stable graph structure as well as the stable, platform-independent API knowledge of naming function to represent the final semantics of functions. The experimental results show that the model accuracy of N_Match outperforms the baseline model in terms of cross-platform, cross-optimization level, and industrial scenarios. In the vulnerability search experiment, N_Match significantly improves hit@N, the mAP exceeds the current graph embedding model by 66%. In addition, we also give several interesting observations from the experiments. The code and model are publicly available at https://www.github.com/CSecurityZhongYuan/Binary-Name_Match

A Software Defined Radio Evaluation Platform for WBAN Systems

In recent years, the Wireless Body Area Network (WBAN) concept has attracted significant academic and industrial attention. WBAN specifies a network dedicated to collecting personal biomedical data from advanced sensors that are then used for health and lifestyle purposes. In 2012, the 802.15.6 WBAN standard was released by the Institute of Electrical and Electronics Engineers (IEEE), which regulates and specifies the configurations of WBAN. Compared to the prevailing wireless communication technologies such as Bluetooth and ZigBee, the WBAN standard has the advantages of ultra-low power consumption, high reliability, and high-security protection while transmitting sensitive personal data. Based on the standard specification, several implementations have been published. However, in terms of evaluation, different designs were implemented in proprietary evaluation environments, which may lead to unfair comparison. In this paper, a Software-Defined Radio (SDR) evaluation platform for WBAN systems is proposed to evaluate the RF channel specified in the IEEE 802.15.6 standard. A narrowband communication protocol demonstration with a security scheme in WBAN has been performed to successfully validate the design in the proposed evaluation platform

An Optimized Faster R-CNN Method Based on DRNet and RoI Align for Building Detection in Remote Sensing Images

In recent years, the increase of satellites and UAV (unmanned aerial vehicles) has multiplied the amount of remote sensing data available to people, but only a small part of the remote sensing data has been properly used; problems such as land planning, disaster management and resource monitoring still need to be solved. Buildings in remote sensing images have obvious positioning characteristics; thus, the detection of buildings can not only help the mapping and automatic updating of geographic information systems but also have guiding significance for the detection of other types of ground objects in remote sensing images. Aiming at the deficiency of traditional building remote sensing detection, an improved Faster R-CNN (region-based Convolutional Neural Network) algorithm was proposed in this paper, which adopts DRNet (Dense Residual Network) and RoI (Region of Interest) Align to utilize texture information and to solve the region mismatch problems. The experimental results showed that this method could reach 82.1% mAP (mean average precision) for the detection of landmark buildings, and the prediction box of building coordinates was relatively accurate, which improves the building detection results. Moreover, the recognition of buildings in a complex environment was also excellent

Silver Nanoparticle-Decorated Boron Nitride with Tunable Electronic Properties for Enhancement of Adsorption Performance

In this paper, a series of silver nanoparticle (AgNP)-decorated boron nitride (Ag-BN) with different Ag amounts were successfully synthesized by a one-pot pyrolysis method and used as novel high-efficiency adsorbents for the removal of organic pollutant tetracycline (TC) and rhodamine B (RhB). According to the adsorption capacity of the samples, the obtained optimal Ag/B molar ratio was 1%. The adsorption data fitted well with the pseudo-second-order kinetics and Langmuir isotherm models with the maximum adsorption capacity of 358 and 880 mg/g for TC and RhB, respectively. The thermodynamic studies suggested that the adsorption process was spontaneous and endothermic in nature. The introduction of AgNP onto BN enhanced the adsorption capacity on account of tunable electronic properties. The adsorption mechanism is discussed in detail with the effect of pH, density function theory (DFT), and thermodynamics