Biblio

Software Defined Networking (SDN) decouples the control plane and the data plane and solves the difficulty of new services deployment. However, the threat of a single point of failure is also introduced at the same time. The attacker can launch DDoS attacks towards the controller through switches. In this paper, a DDoS attack detection method based on information entropy and deep learning is proposed. Firstly, suspicious traffic can be inspected through information entropy detection by the controller. Then, fine-grained packet-based detection is executed by the convolutional neural network (CNN) model to distinguish between normal traffic and attack traffic. Finally, the controller performs the defense strategy to intercept the attack. The experiments indicate that the accuracy of this method reaches 98.98%, which has the potential to detect DDoS attack traffic effectively in the SDN environment.

This paper designs a re-transmission strategy to intensify the security of communication over the additive white Gaussian noise (AWGN) wire-tap channel. In this scheme, irregular low-density parity-check (LDPC) codes work with reliability-based hybrid automatic repeat-request (RB-HARQ). For irregular LDPC codes, the variable nodes have different degrees, which means miscellaneous protection for the nodes. In RB-HARQ protocol, the legitimate receiver calls for re-transmissions including the most unreliable bits at decoder's outputting. The bits' reliability can be evaluated by the average magnitude of a posteriori probability log-likelihood ratios (APP LLRs). Specifically, this scheme utilizes the bit-error rate (BER) to assess the secrecy performance. Besides, the paper gives close analyses of BER through theoretical arguments and simulations. Results of numerical example demonstrate that RB-HARQ protocol with irregular LDPC codes can hugely reinforce the security performance of the communication system.

The bioinformatics features are collected by pattern recognition technology, and the digital coding and format conversion of the feature data are realized by using the theory of topological group transformation. Authentication and Signature based on Zero Knowledge Proof Technology can be used as the trusted credentials of cloud platform and cannot be forged, thus realizing trusted and secure access.

This paper studies initial-value privacy problems of linear dynamical systems. We consider a standard linear time-invariant system with random process and measurement noises. For such a system, eavesdroppers having access to system output trajectories may infer the system initial states, leading to initial-value privacy risks. When a finite number of output trajectories are eavesdropped, we consider a requirement that any guess about the initial values can be plausibly denied. When an infinite number of output trajectories are eavesdropped, we consider a requirement that the initial values should not be uniquely recoverable. In view of these two privacy requirements, we define differential initial-value privacy and intrinsic initial-value privacy, respectively, for the system as metrics of privacy risks. First of all, we prove that the intrinsic initial-value privacy is equivalent to unobservability, while the differential initial-value privacy can be achieved for a privacy budget depending on an extended observability matrix of the system and the covariance of the noises. Next, the inherent network nature of the considered linear system is explored, where each individual state corresponds to a node and the state and output matrices induce interaction and sensing graphs, leading to a network system. Under this network system perspective, we allow the initial states at some nodes to be public, and investigate the resulting intrinsic initial- value privacy of each individual node. We establish necessary and sufficient conditions for such individual node initial-value privacy, and also prove that the intrinsic initial-value privacy of individual nodes is generically determined by the network structure.

We explore how to build a blind certificate authority (CA). Unlike conventional CAs, which learn the exact identity of those registering a public key, a blind CA can simultaneously validate an identity and provide a certificate binding a public key to it, without ever learning the identity. Blind CAs would therefore allow bootstrapping truly anonymous systems in which no party ever learns who participates. In this work we focus on constructing blind CAs that can bind an email address to a public key. To do so, we first introduce secure channel injection (SCI) protocols. These allow one party (in our setting, the blind CA) to insert a private message into another party's encrypted communications. We construct an efficient SCI protocol for communications delivered over TLS, and use it to realize anonymous proofs of account ownership for SMTP servers. Combined with a zero-knowledge certificate signing protocol, we build the first blind CA that allows Alice to obtain a X.509 certificate binding her email address alice@domain.com to a public key of her choosing without ever revealing “alice” to the CA. We show experimentally that our system works with standard email server implementations as well as Gmail.

The output feedback problem of finite-time command filtering for nonlinear systems with random disturbance is addressed in this paper. This is the first time that command filtering and output feedback are integrated so that a nonlinear system with random disturbance converge rapidly in finite time. The uncertain functions and unmeasured states are estimated by the fuzzy logic system (FLS) and nonlinear state observer, respectively. Based on the adaptive framework, command filtering technology is applied to mitigate the problem of ``term explosion'' inherent in traditional methods, and error compensation mechanism is considered to improve the control performance of the system. The developed output feedback controller ensures the boundedness of all signals in the stochastic system within a finite time, and the convergence residual can converge to a small region. The validity of this scheme is well verified in a numerical example.

We propose a novel hybrid imaging system to acquire 4D high-speed hyperspectral (HSHS) videos with high spatial and spectral resolution. The proposed system consists of two branches: one branch performs Nyquist sampling in the temporal dimension while integrating the whole spectrum, resulting in a high-frame-rate panchromatic video; the other branch performs compressive sampling in the spectral dimension with longer exposures, resulting in a low-frame-rate hyperspectral video. Owing to the high light throughput and complementary sampling, these two branches jointly provide reliable measurements for recovering the underlying HSHS video. Moreover, the panchromatic video can be used to learn an over-complete 3D dictionary to represent each band-wise video sparsely, thanks to the inherent structural similarity in the spectral dimension. Based on the joint measurements and the self-adaptive dictionary, we further propose a simultaneous spectral sparse (3S) model to reinforce the structural similarity across different bands and develop an efficient computational reconstruction algorithm to recover the HSHS video. Both simulation and hardware experiments validate the effectiveness of the proposed approach. To the best of our knowledge, this is the first time that hyperspectral videos can be acquired at a frame rate up to 100fps with commodity optical elements and under ordinary indoor illumination.

With the emergence of quantum computers, traditional digital signature schemes based on problems such as large integer solutions and discrete logarithms will no longer be secure, and it is urgent to find effective digital signature schemes that can resist quantum attacks. Lattice cryptography has the advantages of computational simplicity and high security. In this paper, we propose an identity-based digital signature scheme based on the rejection sampling algorithm. Unlike most schemes that use a common Gaussian distribution, this paper uses a bimodal Gaussian distribution, which improves efficiency. The identity-based signature scheme is more convenient for practical application than the traditional certificate-based signature scheme.

In this paper, an adaptive scan chain structure based plaintext analysis technique is proposed. The technology is implemented by three circuits, including adaptive scan chain circuit, plaintext analysis circuit and controller circuit. The plaintext is analyzed whether meet the characteristics of the differential cryptanalysis in the plaintext analysis module. The adaptive scan chain contains MUX, XOR and traditional scan chain, which is easy to implement. If the last bit of two plaintexts differs by one, the adaptive scan chain is controlled to input them into different scan chain. Compared with complicated scan chain, the structure of adaptive scan chain is variable and can mislead attackers who use differential cryptanalysis attack. Through experimental analysis, it is proved that the security of the adaptive scan chain structure is greatly improved.

The CAPTCHA is an effective method commonly used in live interactive proofs on the Internet. The widely used CAPTCHAs are text-based schemes. In this paper, we document how we have broken such text-based scheme used by a website CAPTCHA. We use the sliding window to segment 1001 pieces of CAPTCHA to get 5900 images with single-character useful information, a total of 25 categories. In order to make the convolution neural network learn more image features, we augmented the data set to get 129924 pictures. The data set is trained and tested in AlexNet and GoogLeNet to get the accuracy of 87.45% and 98.92%, respectively. The experiment shows that the optimized network parameters can make the accuracy rate up to 92.7% in AlexNet and 98.96% in GoogLeNet.

Reliable detection of intrusion is the basis of safety in cognitive radio networks (CRNs). So far, few scholars applied intrusion detection systems (IDSs) to combat intrusion against CRNs. In order to improve the performance of intrusion detection in CRNs, a distributed intrusion detection scheme has been proposed. In this paper, a method base on Dempster-Shafer's (D-S) evidence theory to detect intrusion in CRNs is put forward, in which the detection data and credibility of different local IDS Agent is combined by D-S in the cooperative detection center, so that different local detection decisions are taken into consideration in the final decision. The effectiveness of the proposed scheme is verified by simulation, and the results reflect a noticeable performance improvement between the proposed scheme and the traditional method.

In Energy Internet mode, a large number of alarm information is generated when equipment exception and multiple faults in large power grid, which seriously affects the information collection, fault analysis and delays the accident treatment for the monitors. To this point, this paper proposed a method for power grid monitoring to monitor and diagnose fault in real time, constructed the equipment fault logical model based on five section alarm information, built the standard fault information set, realized fault information optimization, fault equipment location, fault type diagnosis, false-report message and missing-report message analysis using matching algorithm. The validity and practicality of the proposed method by an actual case was verified, which can shorten the time of obtaining and analyzing fault information, accelerate the progress of accident treatment, ensure the safe and stable operation of power grid.

Two-factor authentication (2FA) popularly works by verifying something the user knows (a password) and something she possesses (a token, popularly instantiated with a smart phone). Conventional 2FA systems require extra interaction like typing a verification code, which is not very user-friendly. For improved user experience, recent work aims at zero-effort 2FA, in which a smart phone placed close to a computer (where the user enters her username/password into a browser to log into a server) automatically assists with the authentication. To prove her possession of the smart phone, the user needs to prove the phone is on the login spot, which reduces zero-effort 2FA to co-presence detection. In this paper, we propose SoundAuth, a secure zero-effort 2FA mechanism based on (two kinds of) ambient audio signals. SoundAuth looks for signs of proximity by having the browser and the smart phone compare both their surrounding sounds and certain unpredictable near-ultrasounds; if significant distinguishability is found, SoundAuth rejects the login request. For the ambient signals comparison, we regard it as a classification problem and employ a machine learning technique to analyze the audio signals. Experiments with real login attempts show that SoundAuth not only is comparable to existent schemes concerning utility, but also outperforms them in terms of resilience to attacks. SoundAuth can be easily deployed as it is readily supported by most smart phones and major browsers.

This paper aims at identifying the neural correlates of human trust in autonomous systems using electroencephalography (EEG) signals. Quantifying the relationship between trust and brain activities allows for real-time assessment of human trust in automation. This line of effort contributes to the design of trusted autonomous systems, and more generally, modeling the interaction in human-autonomy interaction. To study the correlates of trust, we use an investment game in which artificial agents with different levels of trustworthiness are employed. We collected EEG signals from 10 human subjects while they are playing the game; then computed three types of features from these signals considering the signal time-dependency, complexity and power spectrum using an autoregressive model (AR), sample entropy and Fourier analysis, respectively. Results of a mixed model analysis showed significant correlation between human trust and EEG features from certain electrodes. The frontal and the occipital area are identified as the predominant brain areas correlated with trust.

Top-level domains play an important role in domain name system. Close attention should be paid to security of top level domains. In this paper, we found many configuration anomalies of top-level domains by analyzing their resource records. We got resource records of top-level domains from root name servers and authoritative servers of top-level domains. By comparing these resource records, we observed the anomalies in top-level domains. For example, there are 8 servers shared by more than one hundred top-level domains; Some TTL fields or SERIAL fields of resource records obtained on each NS servers of the same top-level domain were inconsistent; some authoritative servers of top-level domains were unreachable. Those anomalies may affect the availability of top-level domains. We hope that these anomalies can draw top-level domain administrators' attention to security of top-level domains.

Aiming at the single hopping strategy in the terminal information hopping active defense technology, a variety of heterogeneous hopping modes are introduced into the terminal information hopping system, the definition of the terminal information is expanded, and the adaptive adjustment of the hopping strategy is given. A network adversarial training simulation system is researched and designed, and related subsystems are discussed from the perspective of key technologies and their implementation, including interactive adversarial training simulation system, adversarial training simulation support software system, adversarial training simulation evaluation system and adversarial training Mock Repository. The system can provide a good environment for network confrontation theory research and network confrontation training simulation, which is of great significance.

In this paper, we design a model for resource allocation in IoT system considering the cyber security, to achieve optimal resource allocation when defend the attack and threat. The resource allocation problem is constructed as a dynamic game, where the threat level is the state and the defend cost is the objective function. Open loop solution and feedback solutions are both given to the defender as the optimal control variables under different solutions situations. The optimal allocated resource and the optimal threat level for the defender is simulated through the numerical simulations.

Aiming at the big data security and privacy protection issues in the smart grid, the current key technologies for big data security and privacy protection in smart grids are sorted out, and a privacy-protecting smart grid association rule is proposed according to the privacy-protecting smart grid big data analysis and mining technology route The mining plan specifically analyzes the risk factors in the operation of the new power grid, and discusses the information security of power grid users from the perspective of the user, focusing on the protection of privacy and security, using safe multi-party calculation of the support and confidence of the association rules. Privacy-protecting smart grid big data mining enables power companies to improve service quality to 7.5% without divulging customer private information.

The large amounts of synchrophasor data obtained by Phasor Measurement Units (PMUs) provide dynamic visibility into power systems. Extracting reliable information from the data can enhance power system situational awareness. The data quality often suffers from data losses, bad data, and cyber data attacks. Data privacy is also an increasing concern. In this paper, we discuss our recently proposed framework of data recovery, error correction, data privacy enhancement, and event identification methods by exploiting the intrinsic low-dimensional structures in the high-dimensional spatial-temporal blocks of PMU data. Our data-driven approaches are computationally efficient with provable analytical guarantees. The data recovery method can recover the ground-truth data even if simultaneous and consecutive data losses and errors happen across all PMU channels for some time. We can identify PMU channels that are under false data injection attacks by locating abnormal dynamics in the data. The data recovery method for the operator can extract the information accurately by collectively processing the privacy-preserving data from many PMUs. A cyber intruder with access to partial measurements cannot recover the data correctly even using the same approach. A real-time event identification method is also proposed, based on the new idea of characterizing an event by the low-dimensional subspace spanned by the dominant singular vectors of the data matrix.

In recent years, the demand for high reliability in industrial wireless communication has been increasing. To meet the strict requirement, many researchers have studied various bit interleaving coding schemes for long packet transmission in industrial wireless networks. Current research shows that the use of bit interleaving structure can improve the performance of the communication system for long packet transmission, but to improve reliability of industrial wireless communications by combining the bit interleaving and channel coding for short packets still requires further analysis. With this aim, bit interleaving structure is applied to convolution code coding scheme for short packet transmission in this paper. We prove that the use of interleaver fail to improve the reliability of data transmission under the circumstance of short packet transmission.

SM9 is an identity-based cryptography algorithm published by the State Cryptography Administration of China. With SM9, a user's private key for signing is generated by a central system called key generation center (KGC). When the owner of the private key wants to shirk responsibility by denying that the signature was generated by himself, he can claim that the operator of KGC forged the signature using the generated private key. To address this issue, in this paper, two schemes of SM9 digital signature with non-repudiation are proposed. With the proposed schemes, the user's private key for signing is collaboratively generated by two separate components, one of which is deployed in the private key service provider's site while the other is deployed in the user's site. The private key can only be calculated in the user's site with the help of homomorphic encryption. Therefore, only the user can obtain the private key and he cannot deny that the signature was generated by himself. The proposed schemes can achieve the non-repudiation of SM9 digital signature.

Nanosize multiferroic La-doped YFeO3 powders are harvested via a low-temperature solid-state reaction method. X-ray diffraction (XRD), scanning electron microscopy (SEM) and Raman spectra analysis reveal that with La addition, YFeO3 powders are successfully fabricated at a lower temperature with the size below 60 nm, and a refined structure is obtained. Magnetic hysteresis loop illustrates ferromagnetic behavior of YFeO3 nano particles can be enhanced with La addition. The maximum and remnant magnetization of the powders are about 4.03 and 1.22 emu/g, respectively. It is shown that the optical band gap is around 2.25 eV, proving that La doped YFeO3 nano particles can strongly absorb visible light. Both magnetic and optical properties are greatly enhanced with La addition, proving its potential application in magnetic and optical field.

To solve the high-resolution three-dimensional (3D) microwave imaging is a challenging topic due to its inherent unmanageable computation. Recently, deep learning techniques that can fully explore the prior of meaningful pattern embodied in data have begun to show its intriguing merits in various areas of inverse problem. Motivated by this observation, we here present a deep-learning-inspired approach to the high-resolution 3D microwave imaging in the context of Generative Adversarial Network (GAN), termed as GANMI in this work. Simulation and experimental results have been provided to demonstrate that the proposed GANMI can remarkably outperform conventional methods in terms of both the image quality and computational time.

With the advantage in compact representation and efficient comparison, binary hashing has been extensively investigated for approximate nearest neighbor search. In this paper, we propose a novel and general hashing framework, which simultaneously considers a new linear pair-wise distance preserving objective and point-wise constraint. The direct distance preserving objective aims to keep the linear relationships between the Euclidean distance and the Hamming distance of data points. Based on different point-wise constraints, we propose two methods to instantiate this framework. The first one is a pseudo-supervised hashing method, which uses existing unsupervised hashing methods to generate binary codes as pseudo-supervised information. The second one is an unsupervised hashing method, in which quantization loss is considered. We validate our framework on two large-scale datasets. The experiments demonstrate that our pseudo-supervised method achieves consistent improvement for the state-of-the-art unsupervised hashing methods, while our unsupervised method outperforms the state-of-the-art methods.