Biblio

Traditional attributed network embedding methods are designed to map structural and attribute information of networks jointly into a continuous Euclidean space, while recently a novel branch of them named binarized attributed network embedding has emerged to learn binary codes in Hamming space, aiming to save time and memory costs and to naturally fit node retrieval task. However, current binarized attributed network embedding methods are scarce and mostly ignore the local attribute similarity between each pair of nodes. Besides, none of them attempt to control the independency of each dimension(bit) of the learned binary representation vectors. As existing methods still need improving, we propose an unsupervised Neural-based Binarized Attributed Network Embedding (NBANE) approach. Firstly, we inherit the Weisfeiler-Lehman proximity matrix from predecessors to aggregate high-order features for each node. Secondly, we feed the aggregated features into an autoencoder with the attribute similarity penalizing term and the orthogonality term to make further dimension reduction. To solve the problem of integer optimization we adopt the relaxation-quantization method during the process of training neural networks. Empirically, we evaluate the performance of NBANE through node classification and clustering tasks on three real-world datasets and study a case on fast retrieval in academic networks. Our method achieves better performance over state- of-the-art baselines methods of various types.

Advances in sensing and tracking technology lead to the proliferation of location-based services. Location service providers (LSPs) often resort to commercial public clouds to store the tremendous geospatial data and process location-based queries from data users. To protect the privacy of LSP's geospatial data and data user's query location against the untrusted cloud, they are required to be encrypted before sending to the cloud. Nevertheless, it is not easy to design a fast and secure location-based query processing scheme over the encrypted data. In this paper, we propose a Fast and Secure kNN (FSkNN) scheme to support secure k nearest neighbor (k NN) search in cloud computing. We reveal the inherent connection between an Sk NN protocol and a secure range query protocol and further describe how to construct FSkNN based on a secure range query protocol. FSkNN leverages a customized accuracy-assured strategy to ensure the result accuracy and adopts a data structure named random Bloom filter (RBF) to build a secure index for efficiently searching. We formally prove the security of FSkNN under the random oracle model. Our evaluation results show that FSkNN is highly practical.

When using deep learning for DDoS attack detection, there is a general degradation in detection performance due to small sample size. This paper proposes a small-sample DDoS attack detection method based on deep transfer learning. First, deep learning techniques are used to train several neural networks that can be used for transfer in DDoS attacks with sufficient samples. Then we design a transferability metric to compare the transfer performance of different networks. With this metric, the network with the best transfer performance can be selected among the four networks. Then for a small sample of DDoS attacks, this paper demonstrates that the deep learning detection technique brings deterioration in performance, with the detection performance dropping from 99.28% to 67%. Finally, we end up with a 20.8% improvement in detection performance by deep transfer of the 8LANN network in the target domain. The experiment shows that the detection method based on deep transfer learning proposed in this paper can well improve the performance deterioration of deep learning techniques for small sample DDoS attack detection.

Current physical layer authentication (PLA) mechanisms are mostly designed for static communications, and the accuracy degrades significantly when used in dynamic scenarios, where the network environments and wireless channels change frequently. To improve the authentication performance, it is necessary to update the hypothesis test models and parameters in time, which however brings high computational complexity and authentication delay. In this paper, we propose a lightweight cross-layer authentication scheme for dynamic communication scenarios. We use multiple characteristics based PLA to guarantee the reliability and accuracy of authentication, and propose an upper layer assisted method to ensure the performance stability. Specifically, upper layer authentication (ULA) helps to update the PLA models and parameters. By properly choosing the period of triggering ULA, a balance between complexity and performance can be easily obtained. Simulation results show that our scheme can achieve pretty good authentication performance with reduced complexity.

Network covert timing channel(NCTC) is a process of transmitting hidden information by means of inter-packet delay (IPD) of legitimate network traffic. Their ability to evade traditional security policies makes NCTCs a grave security concern. However, a robust method that can be used to detect a large number of NCTCs is missing. In this paper, a NCTC detection method based on chaos theory and threshold secret sharing is proposed. Our method uses chaos theory to reconstruct a high-dimensional phase space from one-dimensional time series and extract the unique and stable channel traits. Then, a channel identifier is constructed using the secret reconstruction strategy from threshold secret sharing to realize the mapping of the channel features to channel identifiers. Experimental results show that the approach can detect varieties of NCTCs with a guaranteed true positive rate and greatly improve the versatility and robustness.

Due to the noise in the images, the edges extracted from these noisy images are always discontinuous and inaccurate by traditional operators. In order to solve these problems, this paper proposes multi-direction edge detection operator to detect edges from noisy images. The new operator is designed by introducing the shear transformation into the traditional operator. On the one hand, the shear transformation can provide a more favorable treatment for directions, which can make the new operator detect edges in different directions and overcome the directional limitation in the traditional operator. On the other hand, all the single pixel edge images in different directions can be fused. In this case, the edge information can complement each other. The experimental results indicate that the new operator is superior to the traditional ones in terms of the effectiveness of edge detection and the ability of noise rejection.

The PyPI ecosystem has indexed millions of Python libraries to allow developers to automatically download and install dependencies of their projects based on the specified version constraints. Despite the convenience brought by automation, version constraints in Python projects can easily conflict, resulting in build failures. We refer to such conflicts as Dependency Conflict (DC) issues. Although DC issues are common in Python projects, developers lack tool support to gain a comprehensive knowledge for diagnosing the root causes of these issues. In this paper, we conducted an empirical study on 235 real-world DC issues. We studied the manifestation patterns and fixing strategies of these issues and found several key factors that can lead to DC issues and their regressions. Based on our findings, we designed and implemented Watchman, a technique to continuously monitor dependency conflicts for the PyPI ecosystem. In our evaluation, Watchman analyzed PyPI snapshots between 11 Jul 2019 and 16 Aug 2019, and found 117 potential DC issues. We reported these issues to the developers of the corresponding projects. So far, 63 issues have been confirmed, 38 of which have been quickly fixed by applying our suggested patches.

Trust is an important characteristic of successful interactions between humans and agents in many scenarios. Self-driving scenarios are of particular relevance when discussing the issue of trust due to the high-risk nature of erroneous decisions being made. The present study aims to investigate decision-making and aspects of trust in a realistic driving scenario in which an autonomous agent provides guidance to humans. To this end, a simulated driving environment based on a college campus was developed and presented. An online and an in-person experiment were conducted to examine the impacts of mistakes made by the self-driving AI agent on participants’ decisions and trust. During the experiments, participants were asked to complete a series of driving tasks and make a sequence of decisions in a time-limited situation. Behavior analysis indicated a similar relative trend in the decisions across these two experiments. Survey results revealed that a mistake made by the self-driving AI agent at the beginning had a significant impact on participants’ trust. In addition, similar overall experience and feelings across the two experimental conditions were reported. The findings in this study add to our understanding of trust in human-robot interaction scenarios and provide valuable insights for future research work in the field of human-robot trust.

Trust is a key element for successful human-robot interaction. One challenging problem in this domain is the issue of how to construct a formulation that optimally models this trust phenomenon. This paper presents a framework for modeling human-robot trust based on representing the human decision-making process as a formulation based on trust states. Using this formulation, we then discuss a generalized model of human-robot trust based on Hidden Markov Models and Logistic Regression. The proposed approach is validated on datasets collected from two different human subject studies in which the human is provided the ability to take advice from a robot. Both experimental scenarios were time-sensitive, in that a decision had to be made by the human in a limited time period, but each scenario featured different levels of cognitive load. The experimental results demonstrate that the proposed formulation can be utilized to model trust, in which the system can predict whether the human will decide to take advice (or not) from the robot. It was found that our prediction performance degrades after the robot made a mistake. The validation of this approach on two scenarios implies that this model can be applied to other interactive scenarios as long as the interaction dynamics fits into the proposed formulation. Directions for future improvements are discussed.

In view of the problem that the overall security of the network is difficult to evaluate quantitatively, we propose the edge authority attack graph model, which aims to make up for the traditional dependence attack graph to describe the relationship between vulnerability behaviors. This paper proposed a network security metrics based on probability, and proposes a network vulnerability algorithm based on vulnerability exploit probability and attack target asset value. Finally, a network security reinforcement algorithm with network vulnerability index as the optimization target is proposed based on this metric algorithm.

Air-gapped networks achieve security by using the physical isolation to keep the computers and network from the Internet. However, magnetic covert channels based on CPU utilization have been proposed to help secret data to escape the Faraday-cage and the air-gap. Despite the success of such cover channels, they suffer from the high risk of being detected by the transmitter computer and the challenge of installing malware into such a computer. In this paper, we propose MagView, a distributed magnetic cover channel, where sensitive information is embedded in other data such as video and can be transmitted over the air-gapped internal network. When any computer uses the data such as playing the video, the sensitive information will leak through the magnetic covert channel. The "separation" of information embedding and leaking, combined with the fact that the covert channel can be created on any computer, overcomes these limitations. We demonstrate that CPU utilization for video decoding can be effectively controlled by changing the video frame type and reducing the quantization parameter without video quality degradation. We prototype MagView and achieve up to 8.9 bps throughput with BER as low as 0.0057. Experiments under different environment are conducted to show the robustness of MagView. Limitations and possible countermeasures are also discussed.

The purpose of this paper is to improve the safety of chaotic image encryption algorithm. Firstly, to achieve this goal, it put forward two improved chaotic system logistic and henon, which covered an promoted henon chaotic system with better probability density, and an 2-dimension logistic chaotic system with high Lyapunov exponents. Secondly, the chaotic key stream was generated by the new 2D logistic chaotic system and optimized henon mapping, which mixed in dynamic proportions. The conducted sequence has better randomness and higher safety for image cryptosystem. Thirdly, we proposed algorithm takes advantage of the compounded chaotic system Simulation experiment results and security analysis showed that the proposed scheme was more effective and secure. It can resist various typical attacks, has high security, satisfies the requirements of image encryption theoretical.

The current evaluation of API recommendation systems mainly focuses on correctness, which is calculated through matching results with ground-truth APIs. However, this measurement may be affected if there exist more than one APIs in a result. In practice, some APIs are used to implement basic functionalities (e.g., print and log generation). These APIs can be invoked everywhere, and they may contribute less than functionally related APIs to the given requirements in recommendation. To study the impacts of correct-but-useless APIs, we use utility to measure them. Our study is conducted on more than 5,000 matched results generated by two specification-based API recommendation techniques. The results show that the matched APIs are heavily overlapped, 10% APIs compose more than 80% matched results. The selected 10% APIs are all correct, but few of them are used to implement the required functionality. We further propose a heuristic approach to measure the utility and conduct an online evaluation with 15 developers. Their reports confirm that the matched results with higher utility score usually have more efforts on programming than the lower ones.

Most anti-collusion audio fingerprinting schemes are aiming at finding colluders from the illegal redistributed audio copies. However, the loss caused by the redistributed versions is inevitable. In this letter, a novel fingerprinting scheme is proposed to eliminate the motivation of collusion attack. The audio signal is transformed to the frequency domain by the Fourier transform, and the coefficients in frequency domain are reversed in different degrees according to the fingerprint sequence. Different from other fingerprinting schemes, the coefficients of the host media are excessively modified by the proposed method in order to reduce the quality of the colluded version significantly, but the imperceptibility is well preserved. Experiments show that the colluded audio cannot be reused because of the poor quality. In addition, the proposed method can also resist other common attacks. Various kinds of copyright risks and losses caused by the illegal redistribution are effectively avoided, which is significant for protecting the copyright of audio.

With the deep research on coherent optical OFDM offset quadrature amplitude modulation OFDM/OQAM in these years, and the communication system exposed to potential threat from various capable attackers, which prompt people lay emphasis on encryption methods for transmission. Therefore, in this paper, we systematically discuss an encryption project with the main purpose of improving security in coherent optical OFDM/OQAM (CO-OFDM/OQAM) system, and the scheme applied the chaotic constellation scrambling (CCS) which founded on chaotic cross mapping to encrypt transmitted information. Besides, we also systematically discuss the basic principle of the encryption scheme for CO-OFDM/OQAM system. According to numerous studies and analysis on experiment data with caution, such as the performance of entropy, bit error rate (BER). It's conforms that the security of CO-OFDM/OQAM system have been enhanced.

Personally identifiable information (PII) has become a major target of cyber-attacks, causing severe losses to data breach victims. To protect data breach victims, researchers focus on collecting exposed PII to assess privacy risk and identify at-risk individuals. However, existing studies mostly rely on exposed PII collected from either the dark web or the surface web. Due to the wide exposure of PII on both the dark web and surface web, collecting from only the dark web or the surface web could result in an underestimation of privacy risk. Despite its research and practical value, jointly collecting PII from both sources is a non-trivial task. In this paper, we summarize our effort to systematically identify, collect, and monitor a total of 1,212,004,819 exposed PII records across both the dark web and surface web. Our effort resulted in 5.8 million stolen SSNs, 845,000 stolen credit/debit cards, and 1.2 billion stolen account credentials. From the surface web, we identified and collected over 1.3 million PII records of the victims whose PII is exposed on the dark web. To the best of our knowledge, this is the largest academic collection of exposed PII, which, if properly anonymized, enables various privacy research inquiries, including assessing privacy risk and identifying at-risk populations.

Generative Adversarial Network (GAN) has already made a big splash in the field of generating realistic "fake" data. However, when data is distributed and data-holders are reluctant to share data for privacy reasons, GAN's training is difficult. To address this issue, we propose private FL-GAN, a differential privacy generative adversarial network model based on federated learning. By strategically combining the Lipschitz limit with the differential privacy sensitivity, the model can generate high-quality synthetic data without sacrificing the privacy of the training data. We theoretically prove that private FL-GAN can provide strict privacy guarantee with differential privacy, and experimentally demonstrate our model can generate satisfactory data.

In VANETs, pseudonyms are often used to replace the identity of vehicles in communication. When vehicles drive out of the network or misbehave, their pseudonym certificates need to be revoked by the certificate authority (CA). The certificate revocation lists (CRLs) are usually used to store the revoked certificates before their expiration. However, using CRLs would incur additional storage, communication and computation overhead. Some existing schemes have proposed to use Bloom Filter to compress the original CRLs, but they are unable to delete the expired certificates and introduce the false positive problem. In this paper, we propose an improved pseudonym certificates revocation scheme, using Cuckoo Filter for compression to reduce the impact of these problems. In order to optimize deletion efficiency, we propose the concept of Certificate Expiration List (CEL) which can be implemented with priority queue. The experimental results show that our scheme can effectively reduce the storage and communication overhead of pseudonym certificates revocation, while retaining moderately low false positive rates. In addition, our scheme can also greatly improve the lookup performance on CRLs, and reduce the revocation operation costs by allowing deletion.

A Robot Operating System (ROS) plays a significant role in organizing industrial robots for manufacturing. With an increasing number of the robots, the operators integrate a ROS with networked communication to share the data. This cyber-physical nature exposes the ROS to cyber attacks. To this end, this paper proposes a cross-layer approach to achieve secure and resilient control of a ROS. In the physical layer, due to the delay caused by the security mechanism, we design a time-delay controller for the ROS agent. In the cyber layer, we define cyber states and use Markov Decision Process to evaluate the tradeoffs between physical and security performance. Due to the uncertainty of the cyber state, we extend the MDP to a Partially Observed Markov Decision Process (POMDP). We propose a threshold solution based on our theoretical results. Finally, we present numerical examples to evaluate the performance of the secure and resilient mechanism.

Dynamic spectrum access (DSA) is a promising platform to solve the spectrum shortage problem, in which auction based mechanisms have been extensively studied due to good spectrum allocation efficiency and fairness. Recently, Sybil attacks were introduced in DSA, and Sybil-proof spectrum auction mechanisms have been proposed, which guarantee that each single secondary user (SU) cannot obtain a higher utility under more than one fictitious identities. However, existing Sybil-poof spectrum auction mechanisms achieve only Sybil-proofness for SUs, but not for primary users (PUs), and simulations show that a cheating PU in those mechanisms can obtain a higher utility by Sybil attacks. In this paper, we propose TSUNAMI, the first Truthful and primary user Sybil-proof aUctioN mechAnisM for onlIne spectrum allocation. Specifically, we compute the opportunity cost of each SU and screen out cost-efficient SUs to participate in spectrum allocation. In addition, we present a bid-independent sorting method and a sequential matching approach to achieve primary user Sybil-proofness and 2-D truthfulness, which means that each SU or PU can gain her maximal utility by bidding with her true valuation of spectrum. We evaluate the performance and validate the desired properties of our proposed mechanism through extensive simulations.

For solving multi-objective optimization problems, this paper firstly combines a multi-objective evolutionary algorithm based on decomposition (MOEA/D) with good convergence and non-dominated sorting genetic algorithm II (NSGA-II) with good distribution to construct. Thus we propose a hybrid multi-objective optimization solving algorithm. Then, we consider that the population diversity needs to be improved while applying multi-objective particle swarm optimization (MOPSO) to solve the multi-objective optimization problems and an improved MOPSO algorithm is proposed. We give the distance function between the individual and the population, and the individual with the largest distance is selected as the global optimal individual to maintain population diversity. Finally, the simulation experiments are performed on the ZDT\textbackslashtextbackslashDTLZ test functions and track planning problems. The results indicate the better performance of the improved algorithms.

The increasing of malwares has presented a serious threat to the security of computer systems in recent years. Traditional signature-based anti-virus systems are not able to detect metamorphic and previously unseen malwares and it inspires people to use machine learning methods such as Naive Bayes and Decision Tree to identity malicious executables. Among these methods, detecting malwares by using Support Vector Machine (SVM) is one of the most effective approaches. However, the parameters of SVM have serious impacts on its classification performance. In order to find the optimal parameter combination and avoid the problem of falling into local optimal solution, many methods based on evolutionary algorithms are proposed, including Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Differential Evolution (DE) and others. But these algorithms still face the problem of being trapped into local solution spaces in different degree. In this paper, an improved fireworks algorithm is presented and applied to search parameters of SVM: penalty factor c and kernel function parameter g. To research the performance of the proposed algorithm, numeric experiments are made and compared with some typical algorithms, the experimental results demonstrate it outperforms other algorithms.

With the continuous improvement of people's safety awareness, infrared products as human motion detection technology have been widely used in the field of security. In order to better apply infrared products to life, improving the performance of infrared products and reducing the cost of products has become the main goal. According to the signal collected by Pyroelectric infrared (PIR) sensor, this paper establishes a database model. According to the data collected, Kalman filter is used to preprocess the data. The validity of the data after preprocessing is judged by the algorithm. The experimental results show that the accuracy of the model can reach 97% by using a support vector machine (SVM) algorithm incorporated with Fast Fourier Transform (FFT). According to the above algorithm flow, a real-time intellectual property (IP) core is designed by using hardware description language, after establishing the data processing algorithm. The interface design, timing design and function design of the IP core are designed. The IP core can be connected to the microcontroller unit (MCU) as an independent peripheral to form a PIR special processor, which can detect the distance of 15 m in real time.

With the development of e-Science and data intensive scientific discovery, it needs to ensure scientific data available for the long-term, with the goal that the valuable scientific data should be discovered and re-used for downstream investigations, either alone, or in combination with newly generated data. As such, the preservation of scientific data enables that not only might experiment be reproducible and verifiable, but also new questions can be raised by other scientists to promote research and innovation. In this paper, we focus on the two main problems of digital preservation that are format migration and preservation metadata. Format migration includes both format verification and object transformation. The system architecture of format migration and preservation metadata is presented, mapping rules of object transformation are analyzed, data fixity and integrity and authenticity, digital signature and so on are discussed and an example is shown in detail.

Style transfer is a research hotspot in computer vision. Up to now, it is still a challenge although many researches have been conducted on it for high quality style transfer. In this work, we propose an algorithm named ASTCNN which is a real-time Arbitrary Style Transfer Convolution Neural Network. The ASTCNN consists of two independent encoders and a decoder. The encoders respectively extract style and content features from style and content and the decoder generates the style transferred image images. Experimental results show that ASTCNN achieves higher quality output image than the state-of-the-art style transfer algorithms and the floating point computation of ASTCNN is 23.3% less than theirs.