Biblio

While backtracking analysis has been successful in assisting the investigation of complex security attacks, it faces a critical dependency explosion problem. To address this problem, security analysts currently need to tune backtracking analysis manually with different case-specific heuristics. However, existing systems fail to fulfill two important system requirements to achieve effective backtracking analysis. First, there need flexible abstractions to express various types of heuristics. Second, the system needs to be responsive in providing updates so that the progress of backtracking analysis can be frequently inspected, which typically involves multiple rounds of manual tuning. In this paper, we propose a novel system, APTrace, to meet both of the above requirements. As we demonstrate in the evaluation, security analysts can effectively express heuristics to reduce more than 99.5% of irrelevant events in the backtracking analysis of real-world attack cases. To improve the responsiveness of backtracking analysis, we present a novel execution-window partitioning algorithm that significantly reduces the waiting time between two consecutive updates (especially, 57 times reduction for the top 1% waiting time).

Security of cloud storage and sharing is concerned for years since a semi-trusted party, Cloud Server Provider (CSP), has access to user data on cloud server that may leak users' private data without constraint. Intuitively, an efficient solution of protecting cloud data is to encrypt it before uploading to the cloud server. However, a new requirement, data sharing, makes it difficult to manage secret keys among data owners and target users. Therefore conditional proxy broadcast re-encryption technology (CPBRE) is proposed in recent years to provide data encryption and sharing approaches for cloud environment. It enables a data owner to upload encrypted data to the cloud server and a third party proxy can re-encrypted cloud data under certain condition to a new ciphertext so that target users can decrypt re-encrypted data using their own private key. But few CPBRE schemes are applicable for a dynamic cloud environment. In this paper, we propose a new dynamic conditional proxy broadcast reencryption scheme that can be dynamic in system user setting and target user group. The initialization phase does not require a fixed system user setup so that users can join or leave the system in any time. And data owner can dynamically change the group of user he wants to share data with. We also provide security analysis which proves our scheme to be secure against CSP, and performance analysis shows that our scheme exceeds other schemes in terms of functionality and resource cost.

It is necessary to improve the safety of the underwater acoustic sensor networks (UASNs) since it is mostly used in the military industry. Specific emitter identification is the process of identifying different transmitters based on the radio frequency fingerprint extracted from the received signal. The sonar transmitter is a typical low-frequency radiation source and is an important part of the UASNs. Class D power amplifier, a typical nonlinear amplifier, is usually used in sonar transmitters. The inherent nonlinearity of power amplifiers provides fingerprint features that can be distinguished without transmitters for specific emitter recognition. First, the nonlinearity of the sonar transmitter is studied in-depth, and the nonlinearity of the power amplifier is modeled and its nonlinearity characteristics are analyzed. After obtaining the nonlinear model of an amplifier, a similar amplifier in practical application is obtained by changing its model parameters as the research object. The output signals are collected by giving the same input of different models, and, then, the output signals are extracted and classified. In this paper, the memory polynomial model is used to model the amplifier. The power spectrum features of the output signals are extracted as fingerprint features. Then, the dimensionality of the high-dimensional features is reduced. Finally, the classifier is used to recognize the amplifier. The experimental results show that the individual sonar transmitter can be well identified by using the nonlinear characteristics of the signal. By this way, this method can enhance the communication safety of the UASNs.

A secure multi-party batch matrix multiplication problem (SMBMM) is considered, where the goal is to allow a master to efficiently compute the pairwise products of two batches of massive matrices, by distributing the computation across S servers. Any X colluding servers gain no information about the input, and the master gains no additional information about the input beyond the product. A solution called Generalized Cross Subspace Alignment codes with Noise Alignment (GCSA- NA) is proposed in this work, based on cross-subspace alignment codes. The state of art solution to SMBMM is a coding scheme called polynomial sharing (PS) that was proposed by Nodehi and Maddah-Ali. GCSA-NA outperforms PS codes in several key aspects - more efficient and secure inter-server communication, lower latency, flexible inter-server network topology, efficient batch processing, and tolerance to stragglers.

The Global Positioning System (GPS) can determine the position of any person or object on earth based on satellite signals. But when inside the building, the GPS cannot receive signals, the indoor positioning system will determine the precise position. How to achieve more precise positioning is the difficulty of an indoor positioning system now. In this paper, we proposed an ultra-wideband fingerprinting positioning method based on a convolutional neural network (CNN), and we collect the dataset in a room to test the model, then compare our method with the existing method. In the experiment, our method can reach an accuracy of 98.36%. Compared with other fingerprint positioning methods our method has a great improvement in robustness. That results show that our method has good practicality while achieves higher accuracy.

With widely applied in various fields, deep learning (DL) is becoming the key driving force in industry. Although it has achieved great success in artificial intelligence tasks, similar to traditional software, it has defects that, once it failed, unpredictable accidents and losses would be caused. In this paper, we propose a test cases generation technique based on an adversarial samples generation algorithm for image classification deep neural networks (DNNs), which can generate a large number of good test cases for the testing of DNNs, especially in case that test cases are insufficient. We briefly introduce our method, and implement the framework. We conduct experiments on some classic DNN models and datasets. We further evaluate the test set by using a coverage metric based on states of the DNN.

The digital low dropout regulators are widely used because it can operate at low supply voltage. In the digital low drop-out regulators, the high sampling frequency circuit has a short setup time, but it will produce overshoot, and then the output can be stabilized; although the low sampling frequency circuit output can be directly stabilized, the setup time is too long. This paper proposes a two sampling frequency circuit model, which aims to include the high and low sampling frequencies in the same circuit. By controlling the sampling frequency of the circuit under different conditions, this allows the circuit to combine the advantages of the circuit operating at different sampling frequencies. This shortens the circuit setup time and the stabilization time at the same time.

This paper designs three distribution devices for the smart grid, which are, respectively, novel transformer with dc bias restraining ability, energy-saving contactor, and controllable reactor with adjustable intrinsic magnetic state based on the magnetically tunable nanocomposite material core. First, the magnetic performance of this magnetic material was analyzed and the magnetic properties processing method was put forward. One kind of nanocomposite which is close to the semihard magnetic state with low coercivity and high remanence was attained. Nanocomposite with four magnetic properties was processed and prepared using the distribution devices design. Second, in order to adjust the magnetic state better, the magnetization and demagnetization control circuit based on the single-phase supply power of rectification and inverter for the nanocomposite magnetic performance adjustment has been designed, which can mutual transform the material's soft and hard magnetic phases. Finally, based on the nanocomposite and the control circuit, a novel power transformer, an energy-saving contactor, and a magnetically controllable reactor were manufactured for the smart grid. The maintained remanence of the nanocomposite core after the magnetization could neutralize the dc bias magnetic flux in the transformer main core without changing the transformer neutral point connection mode, could pull in the contactor movable core instead of the traditional electromagnetic-type fixed core, and could adjust the reactor core saturation degree instead of the traditional electromagnetic coil. The simulation and experimental results verify the correctness of the design, which provides reliable, intelligent, interactive, and energy-saving power equipment for the smart power grids safe operation.

This paper proposes a novel kind of direct-current and alternate-decay-current hybrid integrative magnetization and demagnetization power supplies applied to transformer dc bias treatment based on a nanocomposite magnetic material. First, according to the single-phase transformer structure, one dc bias magnetic compensation mechanism was provided. The dc bias flux in the transformer main core could be eliminated directionally by utilizing the material remanence. Second, for the rapid response characteristic of the magnetic material to an external magnetic field, one positive and negative dc magnetization superimposed decaying ac demagnetization hybrid integrative power supplies based on single-phase rectifier circuit and inverter circuit was designed. In order to accurately control the magnetic field strength by which a good de/-magnetization effect could be achieved, this paper adopts the double-loop control technology of the magnetic field strength and magnetizing current for the nanocomposite magnetic state adjustment. Finally, two 10 kVA transformers and the experiment module of the hybrid integrative power supplies were manufactured and built. Experimental results showed that the integrated power supplies have good de/-magnetization effect and practicability, proving the validity and feasibility of the proposed scheme.

BP algorithm used by autoencoder classification algorithm. But the BP algorithm is not only complicated and inefficient, but sometimes falls into local optimum. This makes autoencoder classification algorithm are not very good. So in this paper we combie Quantum Particle Swarm Optimization (QPSO) and autoencoder classification algorithm. QPSO used to optimize the weight of autoencoder neural network and the parameter of softmax. This method has been tested on some database, and the experimental result shows that this method has got good results.

In this paper, a method is proposed for improving the physical layer security for indoor visible light communication (VLC) networks with angle diversity transmitters. An angle diversity transmitter usually consists of multiple narrow-beam light-emitting diode (LED) elements with different orientations. Angle diversity transmitters are suitable for confidential data transmission, since data transmission via narrow light beams can effectively avoid the leakage of messages. In order to improve security performance, protection zones are introduced to the systems with angle diversity transmitters. Simulation results show that over 50% performance improvement can be obtained by adding protection zones.

Graphics processing unit (GPU) has been applied successfully in many scientific computing realms due to its superior performances on float-pointing calculation and memory bandwidth, and has great potential in power system applications. The N-1 static security analysis (SSA) appears to be a candidate application in which massive alternating current power flow (ACPF) problems need to be solved. However, when applying existing GPU-accelerated algorithms to solve N-1 SSA problem, the degree of parallelism is limited because existing researches have been devoted to accelerating the solution of a single ACPF. This paper therefore proposes a GPU-accelerated solution that creates an additional layer of parallelism among batch ACPFs and consequently achieves a much higher level of overall parallelism. First, this paper establishes two basic principles for determining well-designed GPU algorithms, through which the limitation of GPU-accelerated sequential-ACPF solution is demonstrated. Next, being the first of its kind, this paper proposes a novel GPU-accelerated batch-QR solver, which packages massive number of QR tasks to formulate a new larger-scale problem and then achieves higher level of parallelism and better coalesced memory accesses. To further improve the efficiency of solving SSA, a GPU-accelerated batch-Jacobian-Matrix generating and contingency screening is developed and carefully optimized. Lastly, the complete process of the proposed GPU-accelerated batch-ACPF solution for SSA is presented. Case studies on an 8503-bus system show dramatic computation time reduction is achieved compared with all reported existing GPU-accelerated methods. In comparison to UMFPACK-library-based single-CPU counterpart using Intel Xeon E5-2620, the proposed GPU-accelerated SSA framework using NVIDIA K20C achieves up to 57.6 times speedup. It can even achieve four times speedup when compared to one of the fastest multi-core CPU parallel computing solution using KLU library. The prop- sed batch-solving method is practically very promising and lays a critical foundation for many other power system applications that need to deal with massive subtasks, such as Monte-Carlo simulation and probabilistic power flow.

The wide-spreading mobile malware has become a dreadful issue in the increasingly popular mobile networks. Most of the mobile malware relies on network interface to coordinate operations, steal users' private information, and launch attack activities. In this paper, we propose TextDroid, an effective and automated malware detection method combining natural language processing and machine learning. TextDroid can extract distinguishable features (n-gram sequences) to characterize malware samples. A malware detection model is then developed to detect mobile malware using a Support Vector Machine (SVM) classifier. The trained SVM model presents a superior performance on two different data sets, with the malware detection rate reaching 96.36% in the test set and 76.99% in an app set captured in the wild, respectively. In addition, we also design a flow header visualization method to visualize the highlighted texts generated during the apps' network interactions, which assists security researchers in understanding the apps' complex network activities.

We present a gradient-based attack against SVM-based forensic techniques relying on high-dimensional SPAM features. As opposed to prior work, the attack works directly in the pixel domain even if the relationship between pixel values and SPAM features can not be inverted. The proposed method relies on the estimation of the gradient of the SVM output with respect to pixel values, however it departs from gradient descent methodology due to the necessity of preserving the integer nature of pixels and to reduce the effect of the attack on image quality. A fast algorithm to estimate the gradient is also introduced to reduce the complexity of the attack. We tested the proposed attack against SVM detection of histogram stretching, adaptive histogram equalization and median filtering. In all cases the attack succeeded in inducing a decision error with a very limited distortion, the PSNR between the original and the attacked images ranging from 50 to 70 dBs. The attack is also effective in the case of attacks with Limited Knowledge (LK) when the SVM used by the attacker is trained on a different dataset with respect to that used by the analyst.