Image anomaly detection is a popular task in computer graphics, which is widely used in industrial fields. Previous works that address this problem often train CNN-based (e.g. Auto-Encoder, GANs) models to reconstruct covered parts of input images and calculate the difference between the input and the reconstructed image. However, convolutional operations are good at extracting local features making it difficult to identify larger image anomalies. To this end, we propose a transformer architecture based on mutual attention for image anomaly separation. This architecture can capture long-term dependencies and fuse local features with global features to facilitate better image anomaly detection. Our method was extensively evaluated on several benchmarks, and experimental results showed that it improved detection capability by 3.1% and localization capability by 1.0% compared with state-of-the-art reconstruction-based methods.

Communication intrusion detection in Advanced Metering Infrastructure (AMI) is an eminent security technology to ensure the stable operation of the Smart Grid. However, methods based on traditional machine learning are not appropriate for learning high-dimensional features and dealing with the data imbalance of communication traffic in AMI. To solve the above problems, we propose an intrusion detection scheme by combining feature dimensionality reduction and improved Long Short-Term Memory (LSTM). The Stacked Autoencoder (SAE) has shown excellent performance in feature dimensionality reduction. We compress high-dimensional feature input into low-dimensional feature output through SAE, narrowing the complexity of the model. Methods based on LSTM have a superior ability to detect abnormal traffic but cannot extract bidirectional structural features. We designed a Bi-directional Long Short-Term Memory (BiLSTM) model that added an Attention Mechanism. It can determine the criticality of the dimensionality and improve the accuracy of the classification model. Finally, we conduct experiments on the UNSW-NB15 dataset and the NSL-KDD dataset. The proposed scheme has obvious advantages in performance metrics such as accuracy and False Alarm Rate (FAR). The experimental results demonstrate that it can effectively identify the intrusion attack of communication in AMI.

The text classification of power equipment defect is of great significance to equipment health condition evaluation and power equipment maintenance decisions. Most of the existing classification methods do not sufficiently consider the semantic relation between words in the same sentence and cannot extract deep semantic features. To tackle those problems, this article proposes a novel classification method by combining the self-attention mechanism and multi-channel pyramid convolution neural networks. We utilize the bidirectional gated recurrent unit to model the text sequence and, on this basis, improve self-attention layer to dot multiplication on the forward and backward features to obtain the global attention score. Thereby, effective features are enhanced, invalid features are weakened, and important text representation vectors are obtained. To solve the problem that the shallow network structure cannot extract deep semantic features, we design a multi-channel pyramid convolution network, which first extracts deep text features from the channels of different windows and then fuses the text features of each channel. By comparing with the state-of-the-art methods, the model in this article has better performance in text classification of power equipment defects.

Image forgery poses a serious threat in electric power, medicine and other fields. Relevant departments need to pay a great price to identify the authenticity of the image. For traditional copy‐move forgery image detection, the existing methods have at least two problems: low robustness and poor matching caused by a low number of feature points. Here, a novel similarity metric combining cosine and Jaccard is proposed to improve feature matching, which combines with oriented features from accelerated segment test and rotated binary robust independent elementary features (ORB) feature extraction to realise effective and fast image forgery detection. First, the image is divided into overlapping blocks, and ORB is used to extract the feature points of each image block to obtain the text information. Second, the novel similarity metric is used to calculate similarity and match the text. Finally, two image blocks with the highest similarity are located. The experimental results show that, on the one hand, ORB can greatly lessen detection time. On the other hand, the novel similarity metric can improve the poor matching caused by the small number of feature points. Combining the two methods can exhibit high robustness to translation, rotation, noise, illumination and JPEG compression.

Outsourcing keys (including passwords and data encryption keys) to professional password managers (honest-butcurious service providers) is attracting more and more attention from the researchers and users in the era of cloud computing. However, existing solutions in traditional data outsourcing scenario are unable to simultaneously meet the following three security requirements for keys outsourcing: (1) Confidentiality and privacy of keys; (2) Search privacy on identity attributes tied to keys; (3) Owner controllable authorization over his/her shared keys. In this paper, we propose CloudKeyBank, the first unified key management framework that addresses all the three goals above. To implement CloudKeyBank efficiently, we propose a new cryptographic primitive named Searchable Conditional Proxy Re-Encryption (SC-PRE) which combines the techniques of Hidden Vector Encryption (HVE) and Proxy Re-Encryption (PRE) seamlessly.

The Hyperlink-Induced Topic Search (HITS) algorithm developed by Jon Kleinberg made use of the link structure of the web pages on the Web in order to discover and rank web pages being relevant to a particular topic. However it only took account of the hyperlink structure, while completely excluded contents of web pages, and it ignored the fact that degrees of the importance of many hyperlinks on the Web may be different. In this paper, to overcome the topic drifts, we proposed a novel page ranking algorithm combining the hyperlink with the triadic closure theory by considering fully the Vector Space Model (VSM) and the TrustRank algorithm. The method firstly computed the relevance between two randomly arbitrary web pages based on web page topic similarity and common reference degree. Then, by using that model as a point of reference, a new adjacency matrix was constructed to iteratively calculate the authority and hub values of web pages. Next, we calculated the trust-degree for each web page in the basic set by the trust-score algorithm. Finally, the score for each web page is computed by linearly merging the authority and the trust-degree. In our experiments, we used five classic HITS-based algorithms to compare with our proposed page ranking algorithm-PCTHITS (Web Page Topic Similarity, Common Reference Degree, Trust-degree) algorithm. The experimental results demonstrated that our proposed algorithm outperform the other four classic improved algorithms and HITS algorithm.

Abstract Database as a service (DaaS), a new paradigm of software as a service based on cloud computing, is attracting more and more enterprises (data owners) to delegate their database management to a professional third party (database service provider) such as Amazon Web Services and Rackspace. Data owners in DaaS lose control of their sensitive data, which are stored in the delegated database and managed by the untrusted database service provider. Therefore, many encryption‐based approaches including attribute‐based encryption were proposed to implement fine‐grained access control in DaaS scenarios. However, most of the proposed access control enforcement approaches only support one or two of the following privacy guarantees: data privacy, policy privacy and key privacy. In this paper, we first propose a novel concept of DualAcE: a flexible fine‐grained dual access control enforcement mechanism in DaaS by efficiently combining the ciphertext‐policy attribute‐set‐based encryption with database service provider re‐encryption into a DaaS paradigm. The proposed mechanism has implemented dual access control enforcement with multi‐privacy guarantee: data privacy in delegated database, policy privacy in delegated authorization table and key privacy in key distribution process.We describe the security and efficiency analysis through cryptography theory and experimental results. Copyright © 2014 John Wiley & Sons, Ltd.

Explosive growth in the number of passwords for Web based applications and encryption keys for outsourced data storage well exceeds the management limit of users. Therefore, outsourcing keys (including passwords and data encryption keys) to professional password managers (honest-but-curious service providers) is attracting the attention of many users. However, existing solutions in a traditional data outsourcing scenario are unable to simultaneously meet the following three security requirements for keys outsourcing: (1) Confidentiality and privacy of keys; (2) Search privacy on identity attributes tied to keys; (3) Owner controllable authorization over his/her shared keys. In this paper, we propose CloudKeyBank, the first unified key management framework that addresses all the three goals above. Under our framework, the key owner can perform privacy and controllable authorization enforced encryption with minimum information leakage. To implement CloudKeyBank efficiently, we propose a new cryptographic primitive named Searchable Conditional Proxy Re-Encryption (SC-PRE) which combines the techniques of Hidden Vector Encryption (HVE) and Proxy Re-Encryption (PRE) seamlessly, and propose a concrete SC-PRE scheme based on existing HVE and PRE schemes. Our experimental results and security analysis show the efficiency and security goals are well achieved.

With the convenient connection to network, more and more individual information including sensitive information, such as contact list in Mobile Phone or PDA, can be delegated to the professional third service provider to manage and maintain. The benefit of this paradigm is, on one hand to avoid the sensitive information leakage when individual devices failed or lost, on the other hand to make only the authorized users access and share the delegated information online anytime and anywhere. However, in this paradigm the critical problems to be resolved are to guarantee both the privacy of delegated individual information and the privacy of authorized users, and what is more important to afford the owners of communication devices to have high level of control and power to create their own particular access control policies. In this paper, we present an approach to implement the personalized access control at third service provider in a privacy preserving way. Our approach implements the critical problems above in this paradigm by using selective encryption, blind signature and the combination of role based access control and discretionary access control.

Database as a Service (DaaS) is a practical and useful paradigm, in which the Database Service Provider (DSP) hosts the delegated database generated from the Source DB of Data Owner (DO). Due to the untrusted DSP, most of the proposed approaches were concentrated on using encryption to guarantee the privacy of delegated database and using partition based index to speed up the query. However, few papers were proposed to guarantee the privacy of delegated access control policies. Therefore in order to improve the usability of delegated database and guarantee the privacy of delegated access control policies, a critical problem to be addressed in DaaS is to make the DSP enforce the delegated selective authorization policies correctly, but know nothing about the privacy of users or the privacy of delegated authorization policies. In this paper, we present a privacy preserving selective authorization enforcement approach to resolve the critical problem above. By using selective encryption, Pedersen commitment and access control policy polynomial, the privacy of delegated access control policies and the privacy of users can be efficiently guaranteed. Finally we analyze the security properties of our approach from different aspects.

With the popular use of service-oriented technologies, database as a service (DaaS) as a new paradigm is becoming a more practical and useful model for those enterprises who can't afford the expensive DBMS products. However access control management by the database service provider (DSP) in the DaaS context is challenging because the DSP may be untrusted for the delegated data contents. In this paper, we first present an approach to implement the flexible access control enforcement management by applying a DSP re-encryption mechanism. Our approach not only can implement the selective access control of the encrypted data by the DSP, but also can relieve the users from the complex key derivation procedure. The underlying idea of our approach is that the DSP uses different re-encryption keys for users of the system to implement flexible access control enforcement management under the DSP re-encryption mechanism. We demonstrate the usefulness and security property of our flexible access control enforcement management, finally we analyze and resolve the possible attacks and information disclosure.