Biblio
The development of mobile internet has brought convenience to people, but the openness and diversity of mobile Internet make it face the security threat of communication privacy data disclosure. In this paper, a trusted android device security communication method based on TrustZone is proposed. Firstly, Elliptic Curve Diffie-Hellman (ECDH) key agreement algorithm is used to make both parties negotiate the session key in the Trusted Execution Environment (TEE), and then, we stored the key safely in the TEE. Finally, TEE completes the encryption and decryption of the transmitted data. This paper constructs a secure communication between mobile devices without a trusted third party and analyzes the feasibility of the method from time efficiency and security. The experimental results show that the method can resist malicious application monitoring in the process of data encryption and ensures the security of the session key. Compared with the traditional scheme, it is found that the performance of the scheme is not significantly reduced.
Wireless networking opens up many opportunities to facilitate miniaturized robots in collaborative tasks, while the openness of wireless medium exposes robots to the threats of Sybil attackers, who can break the fundamental trust assumption in robotic collaboration by forging a large number of fictitious robots. Recent advances advocate the adoption of bulky multi-antenna systems to passively obtain fine-grained physical layer signatures, rendering them unaffordable to miniaturized robots. To overcome this conundrum, this paper presents ScatterID, a lightweight system that attaches featherlight and batteryless backscatter tags to single-antenna robots to defend against Sybil attacks. Instead of passively "observing" signatures, ScatterID actively "manipulates" multipath propagation by using backscatter tags to intentionally create rich multipath features obtainable to a single-antenna robot. These features are used to construct a distinct profile to detect the real signal source, even when the attacker is mobile and power-scaling. We implement ScatterID on the iRobot Create platform and evaluate it in typical indoor and outdoor environments. The experimental results show that our system achieves a high AUROC of 0.988 and an overall accuracy of 96.4% for identity verification.
In order to solve the problems of the existing speech content authentication algorithm, such as single format, ununiversal algorithm, low security, low accuracy of tamper detection and location in small-scale, a multi-format speech perception hashing based on time-frequency parameter fusion of energy zero ratio and frequency band bariance is proposed. Firstly, the algorithm preprocesses the processed speech signal and calculates the short-time logarithmic energy, zero-crossing rate and frequency band variance of each speech fragment. Then calculate the energy to zero ratio of each frame, perform time- frequency parameter fusion on time-frequency features by mean filtering, and the time-frequency parameters are constructed by difference hashing method. Finally, the hash sequence is scrambled with equal length by logistic chaotic map, so as to improve the security of the hash sequence in the transmission process. Experiments show that the proposed algorithm is robustness, discrimination and key dependent.
Predicting software faults before software testing activities can help rational distribution of time and resources. Software metrics are used for software fault prediction due to their close relationship with software faults. Thanks to the non-linear fitting ability, Neural networks are increasingly used in the prediction model. We first filter metric set of the embedded software by statistical methods to reduce the dimensions of model input. Then we build a back propagation neural network with simple structure but good performance and apply it to two practical embedded software projects. The verification results show that the model has good ability to predict software faults.
With the implementation of W ⊕ X security model on computer system, Return-Oriented Programming(ROP) has become the primary exploitation technique for adversaries. Although many solutions that defend against ROP exploits have been proposed, they still suffer from various shortcomings. In this paper, we propose a new way to mitigate ROP attacks that are based on return instructions. We clean the scratch registers which are also the parameter registers based on the features of ROP malicious code and calling convention. A prototype is implemented on x64-based Linux platform based on Pin. Preliminary experimental results show that our method can efficiently mitigate conventional ROP attacks.
The barcode is an important link between real life and the virtual world nowadays. One of the most common barcodes is QR code, which its appearance, black and white modules, is not visually pleasing. The QR code is applied to product packaging and campaign promotion in the market. There are more and more stores using QR code for transaction payment. If the QR code is altered or illegally duplicated, it will endanger the information security of users. Therefore, the study uses infrared watermarking to embed the infrared QR code information into the explicit QR code to strengthen the anti-counterfeiting features. The explicit graphic QR code is produced by data hiding with error diffusion in this study. With the optical characteristics of K, one of the four printed ink colors CMYK (Cyan, Magenta, Yellow, Black), only K can be rendered in infrared. Hence, we use the infrared watermarking to embed the implicit QR code information into the explicit graphic QR code. General QR code reader may be used to interpret explicit graphic QR code information. As for implicit QR code, it needs the infrared detector to extract its implicit QR code information. If the QR code is illegally copied, it will not show the hidden second QR code under infrared detection. In this study, infrared watermark hidden in the graphic QR code can enhance not only the aesthetics of QR code, but also the anti-counterfeiting feature. It can also be applied to printing related fields, such as security documents, banknotes, etc. in the future.
In this paper, we propose a deep learning framework for malware classification. There has been a huge increase in the volume of malware in recent years which poses a serious security threat to financial institutions, businesses and individuals. In order to combat the proliferation of malware, new strategies are essential to quickly identify and classify malware samples so that their behavior can be analyzed. Machine learning approaches are becoming popular for classifying malware, however, most of the existing machine learning methods for malware classification use shallow learning algorithms (e.g. SVM). Recently, Convolutional Neural Networks (CNN), a deep learning approach, have shown superior performance compared to traditional learning algorithms, especially in tasks such as image classification. Motivated by this success, we propose a CNN-based architecture to classify malware samples. We convert malware binaries to grayscale images and subsequently train a CNN for classification. Experiments on two challenging malware classification datasets, Malimg and Microsoft malware, demonstrate that our method achieves better than the state-of-the-art performance. The proposed method achieves 98.52% and 99.97% accuracy on the Malimg and Microsoft datasets respectively.
Industrial control systems are the fundamental infrastructures of a country. Since the intrusion attack methods for industrial control systems have become complex and concealed, the traditional protection methods, such as vulnerability database, virus database and rule matching cannot cope with the attacks hidden inside the terminals of industrial control systems. In this work, we propose a control flow anomaly detection algorithm based on the control flow of the business programs. First, a basic group partition method based on key paths is proposed to reduce the performance burden caused by tabbed-assert control flow analysis method through expanding basic research units. Second, the algorithm phases of standard path set acquisition and path matching are introduced. By judging whether the current control flow path is deviating from the standard set or not, the abnormal operating conditions of industrial control can be detected. Finally, the effectiveness of a control flow anomaly detection (checking) algorithm based on Path Matching (CFCPM) is demonstrated by anomaly detection ability analysis and experiments.
Deep neural networks (DNNs) are effective machine learning models to solve a large class of recognition problems, including the classification of nonlinearly separable patterns. The applications of DNNs are, however, limited by the large size and high energy consumption of the networks. Recently, stochastic computation (SC) has been considered to implement DNNs to reduce the hardware cost. However, it requires a large number of random number generators (RNGs) that lower the energy efficiency of the network. To overcome these limitations, we propose the design of an energy-efficient deep belief network (DBN) based on stochastic computation. An approximate SC activation unit (A-SCAU) is designed to implement different types of activation functions in the neurons. The A-SCAU is immune to signal correlations, so the RNGs can be shared among all neurons in the same layer with no accuracy loss. The area and energy of the proposed design are 5.27% and 3.31% (or 26.55% and 29.89%) of a 32-bit floating-point (or an 8-bit fixed-point) implementation. It is shown that the proposed SC-DBN design achieves a higher classification accuracy compared to the fixed-point implementation. The accuracy is only lower by 0.12% than the floating-point design at a similar computation speed, but with a significantly lower energy consumption.
Recently, the armed forces want to bring the Internet of Things technology to improve the effectiveness of military operations in battlefield. So the Internet of Battlefield Things (IoBT) has entered our view. And due to the high processing latency and low reliability of the “combat cloud” network for IoBT in the battlefield environment, in this paper , a novel “combat cloud-fog” network architecture for IoBT is proposed. The novel architecture adds a fog computing layer which consists of edge network equipment close to the users in the “combat-cloud” network to reduce latency and enhance reliability. Meanwhile, since the computing capability of the fog equipment are weak, it is necessary to implement distributed computing in the “combat cloud-fog” architecture. Therefore, the distributed computing load balancing problem of the fog computing layer is researched. Moreover, a distributed generalized diffusion strategy is proposed to decrease latency and enhance the stability and survivability of the “combat cloud-fog” network system. The simulation result indicates that the load balancing strategy based on generalized diffusion algorithm could decrease the task response latency and support the efficient processing of battlefield information effectively, which is suitable for the “combat cloud- fog” network architecture.