Biblio

Arbitrary style transfer refers to generate a new image based on any set of existing images. Meanwhile, the generated image retains the content structure of one and the style pattern of another. In terms of content retention and style transfer, the recent arbitrary style transfer algorithms normally perform well in one, but it is difficult to find a trade-off between the two. In this paper, we propose the Deep Content Guidance Network (DCGN) which is stacked by content guidance (CG) layers. And each CG layer involves one position self-attention (pSA) module, one channel self-attention (cSA) module and one content guidance attention (cGA) module. Specially, the pSA module extracts more effective content information on the spatial layout of content images and the cSA module makes the style representation of style images in the channel dimension richer. And in the non-local view, the cGA module utilizes content information to guide the distribution of style features, which obtains a more detailed style expression. Moreover, we introduce a new permutation loss to generalize feature expression, so as to obtain abundant feature expressions while maintaining content structure. Qualitative and quantitative experiments verify that our approach can transform into better stylized images than the state-of-the-art methods.

Nearest neighbor (NN) search is an essential operation in many applications, such as one/few-shot learning and image classification. As such, fast and low-energy hardware support for accurate NN search is highly desirable. Ternary content-addressable memories (TCAMs) have been proposed to accelerate NN search for few-shot learning tasks by implementing \$L\$∞ and Hamming distance metrics, but they cannot achieve software-comparable accuracies. This paper proposes a novel distance function that can be natively evaluated with multi-bit content-addressable memories (MCAMs) based on ferroelectric FETs (Fe-FETs) to perform a single-step, in-memory NN search. Moreover, this approach achieves accuracies comparable to floating-point precision implementations in software for NN classification and one/few-shot learning tasks. As an example, the proposed method achieves a 98.34% accuracy for a 5-way, 5-shot classification task for the Omniglot dataset (only 0.8% lower than software-based implementations) with a 3-bit MCAM. This represents a 13% accuracy improvement over state-of-the-art TCAM-based implementations at iso-energy and iso-delay. The presented distance function is resilient to the effects of FeFET device-to-device variations. Furthermore, this work experimentally demonstrates a 2-bit implementation of FeFET MCAM using AND arrays from GLOBALFOUNDRIES to further validate proof of concept.

This paper examines the fast approaching highly secure and content centric data sharing architecture Named Data Networking. The content name plays the key role in NDN. Most of the users are interested only in the content or information and thereby the host centric internet architecture is losing its importance. Different naming conventions and caching strategies used in Named Data Networking based applications have been discussed in this study. The convergence of NDN with the vehicular networks and the ongoing studies in it will make the path to Intelligent Transportation system more optimized and efficient. It describes the future internet and this idea has taken root in most of the upcoming IOT applications which are going to conquer every phase of life. Though it is in its infancy stage of development, NDN will soon take over traditional IP Architecture.

Named Data Network (NDN) is considered to be the future of Internet architecture. The nature of NDN is to disseminate data based on the naming scheme rather than the location of the node. This feature caters to the need of vehicular applications, resulting in Vehicular Named Data Networks (VNDN). Although it is still in the initial stages of research, the collaboration has assured various advantages which attract the researchers to explore the architecture further. VNDN face challenges such as intermittent connectivity, mobility of nodes, design of efficient forwarding and naming schemes, among others. In order to develop effective forwarding strategies, behavior of data and interest packets under various circumstances needs to be studied. In this paper, propagation behavior of data and interest packets is analyzed by considering metrics such as Interest Satisfaction Ratio (ISR), Hop Count Difference (HCD) and Copies of Data Packets Processed (CDPP). These metrics are evaluated under network conditions such as varying network size, node mobility and amount of interest produced by each node. Simulation results show that data packets do not follow the reverse path of interest packets.

Recently, methods are studied to overcome various problems for Named Data Networking(NDN). Among them, a new method which can overcome content storm problem is required to reduce network congestion and deliver content packet to consumer reliably. According to the various studies, the content storm problems could be overcame by scoped interest flooding. However, because these methods do not considers not only network congestion ratio but also the number another different paths, the correspond content packets could be transmitted unnecessary and network congestion could be worse. Therefore, in this paper, we propose a new content forwarding method for NDN to overcome the content storm problem. In the proposed method, if the network is locally congested and another paths are generated, an intermediate node could postpone or withdraw the content packet transmission to reduce congestion.

In order to increase the availability and reliability of photovoltaic (PV) systems, fault diagnosis and condition monitoring of inverters are of crucial means to meet the goals. Numerous methods are implemented for fault diagnosis of PV inverters, providing robust features and handling massive amount of data. However, existing methods rely on simplistic frameworks that are incapable of inspecting a wide range of intrinsic and explicit features, as well as being time-consuming. In this paper, a novel method based on a multilayer deep belief network (DBN) is suggested for fault diagnosis, which allows the framework to discover the probabilistic reconstruction across its inputs. This approach equips a robust hierarchical generative model for exploiting features associated with faults, interprets functions that are highly variable, and needs lesser prior information. Moreover, the method instantaneously categorizes the fault conditions, which eventually strengthens the adaptability of applying it on a variety of diagnostic problems in an inverter domain. The proposed method is evaluated using multiple input signals at different sampling frequencies. To evaluate the efficacy of DBN, a test model based on a three-phase 2-level grid-tied PV inverter was used. The results show that the method is capable of achieving precise diagnosis operations.

The core looseness fault is an important part of transformer fault. The state of the core can be obtained by analyzing the vibration signal. Vibration analysis method has been used in transformer condition monitoring and fault diagnosis for many years, while different methods produce different results. In order to select the correct method in engineering application, five kinds of joint time-frequency analysis methods, such as short-time Fourier transform, Wigner-Ville distribution, S transform, wavelet transform and empirical mode decomposition are compared, and the advantages and disadvantages of these methods for dealing with the vibration signal of transformer core are analyzed in this paper. It indicates that wavelet transform and empirical mode decomposition have more advantages in the diagnosis of core looseness fault. The conclusions have referential significance for the diagnosis of transformer faults in engineering.

Anomalous users detection in social network is an imperative task for security problems. Motivated by the great power of Graph Neural Networks(GNNs), many current researches adopt GNN-based detectors to reveal the anomalous users. However, the increasing scale of social activities, explosive growth of users and manifold technical disguise render the user detection a difficult task. In this paper, we propose an innovate Relevance-aware Anomalous Users Detection model (RAU-GNN) to obtain a fine-grained detection result. RAU-GNN first extracts multiple relations of all types of users in social network, including both benign and anomalous users, and accordingly constructs the multiple user relation graph. Secondly, we employ relevance-aware GNN framework to learn the hidden features of users, and discriminate the anomalous users after discriminating. Concretely, by integrating Graph Convolution Network(GCN) and Graph Attention Network(GAT), we design a GCN-based relation fusion layer to aggregate initial information from different relations, and a GAT-based embedding layer to obtain the high-level embeddings. Lastly, we feed the learned representations to the following GNN layer in order to consolidate the node embedding by aggregating the final users' embeddings. We conduct extensive experiment on real-world datasets. The experimental results show that our approach can achieve high accuracy for anomalous users detection.

Verification code recognition system based on convolutional neural network. In order to strengthen the network security defense work, this paper proposes a novel verification code recognition system based on convolutional neural network. The system combines Internet technology and big data technology, combined with advanced captcha technology, can prevent hackers from brute force cracking behavior to a certain extent. In addition, the system combines convolutional neural network, which makes the verification code combine numbers and letters, which improves the complexity of the verification code and the security of the user account. Based on this, the system uses threshold segmentation method and projection positioning method to construct an 8-layer convolutional neural network model, which enhances the security of the verification code input link. The research results show that the system can enhance the complexity of captcha, improve the recognition rate of captcha, and improve the security of user accounting.

Any technique to ensure memory safety requires knowledge of (a) precise array bounds and (b) the data types accessed by memory load/store and pointer move instructions (called, owners) in the program. While this information can be effectively derived by compiler-level approaches much of this information may be lost during the compilation process and become unavailable to binary-level tools. In this work we conduct the first detailed study on how accurately can this information be extracted or reconstructed by current state-of-the-art static reverse engineering (RE) platforms for binaries compiled with and without debug symbol information. Furthermore, it is also unclear how the imprecision in array bounds and instruction owner information that is obtained by the RE tools impacts the ability of techniques to detect illegal memory accesses at run-time. We study this issue by designing, building, and deploying a novel binary-level technique to assess the properties and effectiveness of the information provided by the static RE algorithms in the first stage to guide the run-time instrumentation to detect illegal memory accesses in the decoupled second stage. Our work explores the limitations and challenges for static binary analysis tools to develop accurate binary-level techniques to detect memory errors.

Modern cyberattacks are the most powerful disturbance factor for computer networks, as they have a complex and devastating impact. The impact of cyberattacks is primarily aimed at disrupting the performance of computer network protection means. Therefore, managing this defense system in the face of cyberattacks is an important task. The paper examines a technique for constructing an effective control system for a computer network security system operating in real time in the context of cyber attacks. It is supposed that it is built on the basis of constructing a system state space and a stack of control decisions. The probability of finding the security system in certain state at each control step is calculated using a finite Markov chain. The technique makes it possible to predict the number of iterations for managing the security system when exposed to cyber attacks, depending on the segment of the space of its states and the selected number of transitions, as well as automatically generate control decisions. An algorithm has been developed for situational control of a computer network security system in conditions of cyber attacks. The experimental results obtained using the generated dataset demonstrated the high efficiency of the developed technique and the ability to use it to determine the parameters that are most susceptible to abnormal deviations during the impact of cyber attacks.

In a centralized Networked Control System (NCS), all agents share local data with a central processing unit that generates control commands for agents. The use of a communication network between the agents gives NCSs a distinct advantage in efficiency, design cost, and simplicity. However, this benefit comes at the expense of vulnerability to a range of cyber-physical attacks. Recently, novel defense mechanisms to counteract false data injection (FDI) attacks on NCSs have been developed for agents with linear dynamics but have not been thoroughly investigated for NCSs with nonlinear dynamics. This paper proposes an FDI attack mitigation strategy for NCSs composed of agents with nonlinear dynamics under disturbances and measurement noises. The proposed algorithm uses both learning and model-based approaches to estimate agents'states for FDI attack mitigation. A neural network is used to model uncertain dynamics and estimate the effect of FDI attacks. The controller and estimator are designed based on Lyapunov stability analysis. A simulation of robots with Euler-Lagrange dynamics is considered to demonstrate the developed controller's performance to respond to FDI attacks in real-time.

With the reform of the power market(PM), various power applications based on computer networks have also developed. As a network application system supporting the operation of the PM, the technical support system(TSS) of the PM has become increasingly important for its network information security(NIS). The purpose of this article is to study the security protection of computer network information in power systems. This paper proposes an identity authentication algorithm based on digital signatures to verify the legitimacy of system user identities; on the basis of PMI, according to the characteristics of PM access control, a role-based access control model with time and space constraints is proposed, and a role-based access control model is designed. The access control algorithm based on the attribute certificate is used to manage the user's authority. Finally, according to the characteristics of the electricity market data, the data security transmission algorithm is designed and the feasibility is verified. This paper presents the supporting platform for the security test and evaluation of the network information system, and designs the subsystem and its architecture of the security situation assessment (TSSA) and prediction, and then designs the key technologies in this process in detail. This paper implements the subsystem of security situation assessment and prediction, and uses this subsystem to combine with other subsystems in the support platform to perform experiments, and finally adopts multiple manifestations, and the trend of the system's security status the graph is presented to users intuitively. Experimental studies have shown that the residual risks in the power system after implementing risk measures in virtual mode can reduce the risk value of the power system to a fairly low level by implementing only three reinforcement schemes.

The model-free adaptive security tracking control (MFASTC) problem of nonlinear networked control systems is explored in this paper with DoS attacks and delays consideration. In order to alleviate the impact of DoS attack and RTT delays on NCSs performance, an attack compensation mechanism and a networked predictive-based delay compensation mechanism are designed, respectively. The data-based designed method need not the dynamic and structure of the system, The MFASTC algorithm is proposed to ensure the output tracking error being bounded in the mean-square sense. Finally, an example is given to illustrate the effectiveness of the new algorithm by a comparison.

The transportation sector is undergoing rapid changes to reduce pollution and increase life quality in urban areas. One of the most effective approaches is flexible car rental and sharing to reduce traffic congestion and parking space issues. In this paper, we envision a flexible car sharing framework where vehicle owners want to make their vehicles available for flexible rental to other users. The owners delegate the management of their vehicles to intermediate services under certain policies, such as municipalities or authorized services, which manage the due infrastructure and services that can be accessed by users. We investigate the design of an accountable solution that allow vehicles owners, who want to share their vehicles securely under certain usage policies, to control that delegated services and users comply with the policies. While monitoring users behavior, our approach also takes care of users privacy, preventing tracking or profiling procedures by other parties. Existing approaches put high trust assumptions on users and third parties, do not consider users' privacy requirements, or have limitations in terms of flexibility or applicability. We propose an accountable protocol that extends standard delegated authorizations and integrate it with Security Credential Management Systems (SCMS), while considering the requirements and constraints of vehicular networks. We show that the proposed approach represents a practical approach to guarantee accountability in realistic scenarios with acceptable overhead.

Named Data Networking (NDN) is an emerging network architecture, which is built by keeping data as its pivotal point. The in-network cache, one of the important characteristics, makes data packets to be available from multiple locations on the Internet. Hence data access control and their enforcement mechanisms become even more critical in the NDNs. In this paper, we propose a novel encryption-based data access control scheme using Role-Based Encryption (RBE). The inheritance property of our scheme provides a natural way to achieve efficient data access control over hierarchical content. This in turn makes our scheme suitable for large scale real world content-centric applications and services such as Netflix. Further, the proposed scheme introduces an anonymous signature-based authentication mechanism to reject bogus data requests nearer to the source, thereby preventing them from entering the network. This in turn helps to mitigate better denial of service attacks. In addition, the signature mechanism supports unlinkability, which is essential to prevent leakages of individual user's access patterns. Another major feature of the proposed scheme is that it provides accountability of the Internet Service Providers (ISPs) using batch signature verification. Moreover, we have developed a transparent and secure dispute resolution and payment mechanism using smart-contract and blockchain technologies. We present a formal security analysis of our scheme to show it is provably secure against Chosen Plaintext Attacks. We also demonstrate that our scheme supports more functionalities than the existing schemes and its performance is better in terms of computation, communication and storage.

Based on the third generation neural network spiking neural network, this paper optimizes and improves a classification and coding method, and proposes an image recognition method. Firstly, the read image is converted into a spike sequence, and then the spike sequence is encoded in groups and sent to the neurons in the spike neural network. After learning and training for many times, the quantization standard code is obtained. In this process, the spike sequence transformation matrix and dynamic weight matrix are obtained, and the unclassified data are output through the same matrix for image recognition and classification. Simulation results show that the above methods can get correct coding and preliminary recognition classification, and the spiking neural network can be applied.

This paper introduces the GBT-NN, a novel class of Graph-based Transform within the context of block-based predictive transform coding using intra-prediction. The GBT-NNis constructed by learning a mapping function to map a graph Laplacian representing the covariance matrix of the current block. Our objective of learning such a mapping functionis to design a GBT that performs as well as the KLT without requiring to explicitly com-pute the covariance matrix for each residual block to be transformed. To avoid signallingany additional information required to compute the inverse GBT-NN, we also introduce acoding framework that uses a template-based prediction to predict residuals at the decoder. Evaluation results on several video frames and medical images, in terms of the percentageof preserved energy and mean square error, show that the GBT-NN can outperform the DST and DCT.

Modulated signal identification plays a crucial role in both military reconnaissance and civilian signal regulation. Traditionally, modulated signal identification is based on high-order statistics, but this approach has many drawbacks. With the development of deep learning, its advantages are fully exploited by combining it with modulated signals to avoid the complex process of computing a priori knowledge while having good fault tolerance. In this paper, ten digital modulated signals are classified and recognized, and improvements are made on the basis of convolutional neural networks, using feature reuse to increase the depth of the convolutional layer and extract signal features with better results. After experimental analysis, the recognition accuracy increases with the rise of the signal-to-noise ratio, and can reach 90% and above when the signal-to-noise ratio is 30dB.

The exponential increase in the use of Internet of Things (IoT) devices has been accompanied by a spike in cyberattacks on IoT networks. In this research, we investigate the Bot-IoT dataset with a focus on classifying IoT reconnaissance attacks. Reconnaissance attacks are a foundational step in the cyberattack lifecycle. Our contribution is centered on the building of predictive models with the aid of Random Undersampling (RUS) and ensemble Feature Selection Techniques (FSTs). As far as we are aware, this type of experimentation has never been performed for the Reconnaissance attack category of Bot-IoT. Our work uses the Area Under the Receiver Operating Characteristic Curve (AUC) metric to quantify the performance of a diverse range of classifiers: Light GBM, CatBoost, XGBoost, Random Forest (RF), Logistic Regression (LR), Naive Bayes (NB), Decision Tree (DT), and a Multilayer Perceptron (MLP). For this study, we determined that the best learners are DT and DT-based ensemble classifiers, the best RUS ratio is 1:1 or 1:3, and the best ensemble FST is our ``6 Agree'' technique.

With the rapid development of mobile communication technology, the reconnaissance on terminal capability and identity information is not only an important guarantee to maintain the normal order of mobile communication, but also an essential means to ensure the electromagnetic space security. According to the characteristics of 5G mobile communication terminal's transporting capability and identity information, the smart jamming is first used to make the target terminal away from the 5G network, and then the jamming is turned off at once. Next the terminal will return to the 5G network. Through the time-frequency matching detection method, interactive signals of random access process and network registration between the terminal and the base station are quickly captured in this process, and the scheduling information in Physical Downlink Control Channel (PDCCH) and the capability and identity information in Physical Uplink Shared Channel (PUSCH) are demodulated and decoded under non-cooperative conditions. Finally, the experiment is carried out on the actual 5G communication terminal of China Telecom. The capability and identity information of this terminal are extracted successfully in the Stand Alone (SA) mode, which verifies the effectiveness and correctness of the method. This is a significant technical foundation for the subsequent development on the 5G terminal control equipment.

The increasing use of Multiprocessor Systems-on-Chip (MPSoCs) within scalable multi-tenant systems, such as fog/cloud computing, faces the challenge of potential attacks originated by the execution of malicious tasks. Flooding Denial- of-Service (FDoS) attacks are one of the most common and powerful threats for Network-on-Chip (NoC)-based MPSoCs. Since, by overwhelming the NoC, the system is unable to forward legitimate traffic. However, the effectiveness of FDoS attacks depend on the NoC configuration. Moreover, designing a secure MPSoC capable of detecting such attacks while avoiding excessive power/energy and area costs is challenging. To this end, we present two contributions. First, we demonstrate two types of FDoS attacks: based on the packet injection rate (PIR-based FDoS) and based on the packet's payload length (PPL-based FDoS). We show that fair round-robin NoCs are intrinsically protected against PIR-based FDoS. Instead, PPL-based FDoS attacks represent a real threat to MPSoCs. Second, we propose a novel lightweight monitoring method for detecting communication disruptions. Simulation and synthesis results show the feasibility and efficiency of the presented approach.

This paper introduces a scheme for achieving trustworthy computing on SoCs that use an outsourced AXI interconnect for on-chip communication. This is achieved through component guarding, data tagging, event verification, and consequently responding dynamically to an attack. Experimental results confirm the ability of the proposed scheme to detect HT attacks and respond to them at run-time. The proposed scheme extends the state-of-art in trustworthy computing on untrustworthy components by focusing on the issue of an untrusted on-chip interconnect for the first time, and by developing a scheme that is independent of untrusted third-party IP.

Network-on-Chip (NoC) is widely used as an efficient communication architecture in multi-core and many-core System-on-Chips (SoCs). However, the shared communication resources in NoCs, e.g., channels, buffers, and routers might be used to conduct attacks compromising the security of NoC-based SoCs. Almost all of the proposed encryption-based protection methods in the literature need to leave some parts of the packet unencrypted to allow the routers to process/forward packets accordingly. This uncovers the source/destination information of the packet to malicious routers, which can be used in various attacks. In this paper, we propose the idea of secure anonymous routing with minimal hardware overhead to hide the source/destination information while exchanging secure information over the network. The proposed method uses a novel source-routing algorithm that works with encrypted destination addresses and prevents malicious routers from discovering the source/destination of secure packets. To support our proposal, we have designed and implemented a new NoC architecture that works with encrypted addresses. The conducted hardware evaluations show that the proposed security solution combats the security threats at an affordable cost of 1% area and 10% power overheads chip-wide.

Support Vector Machine (SVM) is a relatively novel classification technology, which has shown higher performance than traditional learning methods in many applications. Therefore, some security researchers have proposed an intrusion detection method based on SVM. However, the SVM algorithm is very sensitive to the choice of kernel function and parameter adjustment. Once the parameter selection is unscientific, it will lead to poor classification accuracy. To solve this problem, this paper presents a Grey Wolf Optimizer Algorithm based on Particle Swarm Optimization (PSOGWO) algorithm to improve the Intrusion Detection System (IDS) based on SVM. This method uses PSOGWO algorithm to optimize the parameters of SVM to improve the overall performance of intrusion detection based on SVM. The "optimal detection model" of SVM classifier is determined by the fusion of PSOGWO algorithm and SVM. The comparison experiments based on NSL-KDD dataset show that the intrusion detection method based on PSOGWO-SVM achieves the optimization of the parameters of SVM, and has improved significantly in terms of detection rate, convergence speed and model balance. This shows that the method has better performance for network intrusion detection.