Reviewerly/exHarmony huggingface dataset:
An Effective Low-Dimensional Software Code Representation using BERT and ELMo

Contextualised word representations (e.g., ELMo and BERT) have been shown to outperform static representations (e.g., Word2vec, Fasttext, and GloVe) for many NLP tasks. In this paper, we investigate the use of contextualised embeddings for code search and classification, an area receiving less attention. We construct CodeELMo by training ELMo from scratch and fine tuning CodeBERT embeddings using masked language modeling based on natural language (NL) texts related to software development concepts and programming language (PL) texts consisting of method comment pairs from open source code bases. The dimensionality of the Finetuned Code BERT embeddings is reduced using linear transformations and augmented with a CodeELMo representation to develop CodeELBE – a lowdimensional contextualised software code representation. Results for binary classification and retrieval tasks show that CodeELBE <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> considerably improves retrieval performance on standard deep code search datasets compared to CodeBERT and baseline BERT models.
Image Encryption Algorithm based on Convolutional Neural Network and Four Square Matrix Encoding

To balance the performance of chaos and the algorithm’s efficiency, a Henon-Modulated-Iterative (HMI) map is constructed in this paper. And the chaotic characteristics of the HMI map are analyzed from three aspects: bifurcation map, Lyapunov exponent and balance degree. On this basis, an image encryption algorithm based on a convolutional neural network (CNN) and four square matrix encoding is proposed. Then, the random sequence generation module is designed using CNN, and the generated random sequence is used to construct the scrambling coordinate matrix and modify the position of the pixel values. Finally, the scrambled image is diffused by four square matrix encoding so that the pixel values are evenly distributed, while non-sequential diffusion using hexadecimal addition and subtraction rules improves the security of the encryption algorithm. Experimental results show that the encryption algorithm proposed in this paper has a good encryption effect and can resist various attacks.
An Attribute-attack-proof Watermarking Technique for Relational Database

Proving ownership rights on relational databases is an important issue. The robust watermarking technique could claim ownership by insertion information about the data owner. Hence, it is vital to improving the robustness of watermarking technique in that intruders could launch types of attacks to corrupt the inserted watermark. Furthermore, attributes are explicit and operable objectives to destroy the watermark. To my knowledge, there does not exist a comprehensive solution to resist attribute attack. In this paper, we propose a robust watermarking technique that is robust against subset and attribute attacks. The novelties lie in several points: applying the classifier to reorder watermarked attributes, designing a secret sharing mechanism to duplicate watermark independently on each attribute, and proposing twice majority voting to correct errors caused by attacks for improving the accuracy of watermark detection. In addition, our technique has features of blind, key-based, incrementally updatable, and low false hit rate. Experiments show that our algorithm is robust against subset and attribute attacks compared with AHK, DEW, and KSF algorithms. Moreover, it is efficient with running time in both insertion and detection phases.
A Faster Blockchain Sharding Protocol for Decentralized Ledger

We propose FBSChain, a faster blockchain sharding protocol based on quorum-based BFT algorithm (running as sharding consensus) and use a vertical committee structure to assist in the decoupling of transaction confirmation and block generation. We utilize a set of clients to drive all these related processes. In addition, we deploy an improved common-coin scheme without any private setup to generate the randomness for reconfiguration. The protocol can perform better with less computational and communication costs and improve system scalability without compromising security performance.
Privacy-preserving Decision Making Based on Q-Learning in Cloud Computing

People encounter a variety of continuous decision-making (DM) problems in the real world. Reinforcement learning (RL) is a promising technique to solve these problems. This paper proposes a privacy-preserving Q-learning decision-making scheme (PQDM). Based on distributed homomorphic encryption (HE), we design several secure protocols to implement the under-lying nonlinear operations such as comparing, maximizing, and maximizing parameter solving. Based on the designed security protocols, we propose a secure decision-making protocol in cloud computing, which enables the cloud server to perform element selection and Q-learning functions on ciphertext data. During the entire process, the cloud server does not need to know the actual state, thus guaranteeing the security of the original state information. We analyze the security and complexity of the whole scheme theoretically. Our experimental results show our proposed scheme’s effectiveness and good spatio-temporal performance.
A Blockchain-based Privacy-Preserving Scheme for Cross-domain Authentication

With the demand for security, the Internet companies and institutions have formed independent trust domains by utilizing different cryptographic settings, which brings up the problem of cross-domain authentication. To solve the cross-domain authentication problem, researchers have proposed many schemes that can be divided into two categories: centralized and blockchain-based. However, the challenges of privacy leakage, low efficiency, and incomplete cross-domain have not been effectively overcome in existing schemes. In this paper, we propose a Blockchain-based Privacy-preserving scheme for Cross-Domain Authentication (BPCDA). To realize complete cross-domain, the blockchain is introduced for building trust between different domains. For lightweight management, the Pedersen Commitment is adopted for identity voucher generation. In addition, the Non-Interactive Zero-Knowledge (NIZK) proof algorithm is utilized for mutual and anonymous authentication. The security analysis and the performance evaluation demonstrate that the proposed BPCDA is secure, privacy-preserving, and efficient.
Can PoW Consensus Protocol Resist the Whale Attack?

Proof of Work (PoW) is the most widely used consensus protocol. However, due to the hash rate competition mechanism, longest chain principle, and transaction fee mechanism of the PoW consensus protocol, malicious nodes can launch attacks to obtain more relative revenue than honest mining, which will discourage honest miners from packing transactions into blocks and verifying blocks. As a result, the speed of the nodes reaching consensus in the network is slowed down, or even consensus cannot be reached, which ultimately affects the security of the PoW consensus protocol.In this paper, the Markov Decision Process (MDP) is used to simulate the whale attack launched by malicious nodes, and evaluate the capability of PoW consensus protocol against the whale attack. The experimental results show that the PoW consensus protocol is secure in the Bitcoin network when the transaction fee is set in the range of 0.002-0.3 block rewards and the transaction volume should not exceed 21.09 block rewards. In addition, the PoW consensus protocol will be more secure with the adjustment of parameters such as the number of block confirmations, block generation interval and block size.
Anomaly Detection in Encrypted Identity Resolution Traffic based on Machine Learning

Identity resolution is an emerging network resource widely applied in Industrial Internet of Things. Although encryption improves the privacy of identity resolution, it also challenges DPI-based anomaly detection. Therefore, it is imperative to recognize and supplement the encrypted information of IDS. In this paper, we design a machine learning-based framework to automatically extract critical information of identity resolution system from network traffic. According to the characteristics of traffic, we use the hybrid feature of statistics and sequences to describe encrypted traffic. Besides, a supervised classification algorithm is applied to explore the effective classification of two communication processes, which are service attribution information for node addressing and operation behavior for data management. We tested this method based on the encrypted traffic collected from a realistic identity resolution system. The results indicate that our approach exhibits good performance, outperforms related works, and can be applied in resource-constrained industrial scenario. This is the first work analysing the identity resolution system from the perspective of traffic analysis.
Vehicle Classification System with Mobile Edge Computing Based on Broad Learning

Recently, vehicle classification is becoming increasingly important with the development of automated driving technology. In particular, it can provide the basis and prerequisites for autonomous vehicles to make decisions in terms of improving driving safety. However, the current mainstream vehicle classification methods are deep learning algorithms based on Convolutional Neural Networks (CNN), which are mainly focused on the cloud, and these algorithms have complex models and large training parameters. In addition, for computationally intensive and urgent tasks, the poor computational power and low storage capabilities of edge nodes cannot support CNN-based vehicle classification algorithms for model updating. In this paper, we propose a lightweight vehicle classification method with mobile edge computing based on Broad Learning System (BLS). On the one hand, the vehicle can serve as a mobile edge computing node to provide computing and storage resources to ensure that classification tasks are performed locally and quickly, avoiding the bandwidth congestion caused by uploading to the cloud. On the other hand, we use broad learning method to perform incremental training on the data, which is more suitable for computing at the edge, because it can support incremental updates to the model on the vehicular edge nodes without retraining the whole model. Experiments are conducted on a Raspberry Pi system to simulate edge nodes, the results show with a similar performance, the training speed of our vehicle classification system can be increased by 10 times compared with the other CNN-based algorithms.
MGF-GAN: Multi Granularity Text Feature Fusion for Text-guided-Image Synthesis

We have made research achievements worth sharing on the complicated topic of text-to-image synthesis. Our analysis of popular articles shows that they often use stacked structures to construct and generate confrontation network models and usually introduce multiple sets of generators and discriminator pairs. The entanglement between different generators affects the quality of the final synthesized image. Some researchers have proposed a single-stage network model to avoid traps between multiple generators, But it lacks the use of unstructured natural language information with different granularity. To correct this serious defect, we propose a multi-granularity feature network MGF-GAN, which plays the role of text information with different granularity based on the advantages of the single-stage network. Specifically, we input the three granularity features of the text, including sentences, aspect words, and single words of text, into different stages of the model through spatial attention and channel attention mechanisms to gradually refine the synthetic image from global and local perspectives. In addition, we reconstruct the loss function based on the contrast concept to stabilize the training and ensure that the visual meaning between the synthesized image and the natural language is consistent. We conducted validity experiments on CUB bird and COCO. The significant effect is sufficient to prove the effectiveness and advancement of our MGF-GAN.
Dynamic and Diverse Transformations for Defending Against Adversarial Examples

It is demonstrated that deep neural networks can be easily fooled by adversarial examples. To improve the robustness of neural networks against adversarial attacks, substantial research on adversarial defenses is being carried out, of which input transformation is a typical category of defenses. However, because the transformation also has an impact on the accuracy of clean examples, the existing transformation-based defenses usually adopt minor transformations such as shift and scaling, which limits the defense effect of the transformation to some extent. To this end, we propose a method by using dynamic and diverse transformations for defending against adversarial attacks. Firstly, we constructed a transformation pool that contains both minor and major transformations (e.g., flip, rotate). Secondly, we retrained the model with the data transformed by major transformations to ensure that the performance of model itself is not affected. Finally, we dynamically select transformations to preprocess the input of the model to defend against adversarial examples. We conducted extensive experiments on MNIST and CIFAR-10 datasets and compared our method with the state-of-the-art adversarial training and transformation-based defenses. The experimental results show that our proposed method outperforms the existing methods, improving the robustness of the model against adversarial examples greatly while maintaining high accuracy on clean examples. Our code is available at https://github.com/byerose/DynamicDiverseTransformations.
CPGBERT: An Effective Model for Defect Detection by Learning Program Semantics via Code Property Graph

With the increasing complexity of software composition, code defects have become a long-term problem in software security. Traditional static analysis techniques cannot exhaustively enumerate all unsafe modes, and problems such as low path coverage rate brought by dynamic detection techniques make software security vulnerability detection inefficient. Methods based on Natural Language Processing have promoted the research of code defect detection tasks; however, there are problems of insufficient code semantic learning and limited data processing by pre-trained models. To solve these problems, from the perspective of enriching model input semantics and improving the model’s ability to process data, based on the Transformer model, we propose a hierarchical compression encoder model CPGBERT to detect whether the target function has defects. By using the regularity of the program context and structure, the program code is sliced for the input-output variables related to the objective function and dependencies on the codes’ propagation paths. Extract multiple code property graph information on rich semantics from the sliced program code for graph fusion, and embed the fused code property graph into the model by grouping. During the learning process, the independent hidden layer features are compressed and aggregated to make the model focus on the deep semantic learning of the objective function. The experiment uses the CodeXGLUE benchmark dataset and compares 6 kinds of code defect detection models having better performance to perform defect detection and effect evaluation on actual engineering code. The results show that the accuracy of the CPGBERT detection model is 67.97%, which is 5.89% higher than the CodeBERT model proposed by Microsoft and 1.35% higher than the state-of-the-art model CoTexT.
tTree+: A Threat Tree Model for Representing Complex Semantics

Threat tree model is widely used for risk analysis for software assurance. This model is convenient to convey the logical relations between various threat conditions that will result in a threat iteratively. However, tree representation imposes a limitation in that threat semantics can only present in a solely layered style in a tree structure, e.g., conditions represented by children nodes in the threat tree with edge relations in terms of "and" or "or" will result in the threat represented by the father node. It may not be sufficient for representing certain special cases such as cross-layer conditions, e.g., the nodes at different layers (a child and a uncle) may compose threat conditions, which cannot be represented in a regular tree style. In other words, the conditions represented by a child and a uncle may lead to a threat, either new or not. To extend the semantics of current threat tree model, in this paper, we propose a new threat tree model called tTree+, by extending and converting a non-layered tree representation to a layered tree presentation. Some algorithms are proposed by adding a duplicated node and rebuilding related layers, so that original algorithms for tree operations can be remained without any amendment. This extended tree model can extend the outreach of semantics for further threat analysis, so that the more complex threat can be described and analyzed.
Utility-Aware Privacy-Preserving Federated Learning through Information Bottleneck

Federated learning (FL) as a privacy-preserving machine learning (ML) algorithm provides an efficient distributed training paradigm. Existing FL frameworks still suffer from privacy leakage hazards such as membership inference attacks. The current popular defense approaches are mainly based on differential privacy (DP) strategy. However, privacy preservation is undertaken with an inevitable loss of model utility in DP. As a result, it performs miserably in practical deployments. To solve this problem, we modify the FL framework through the information bottleneck (IB) method to attain a trade-off between privacy protection and model utility. Firstly, we adapt the training process on client side by applying IB in the local training. It is intended to squeeze out privacy through the bottleneck. Secondly, we further modify the training process on server side. A validation process is used to evaluate whether the IB-based local training is squeezing out privacy. Clients that extrude the right information will occupy an important place in aggregation phase. Extensive experiments on classic datasets demonstrate the superiority of the proposed scheme in terms of privacy preservation and model utility.
An Improved Actor-Critic Method for Auto-Combating In Infantry Vehicles

Conventional decision systems, when applied to policy selection in autonomous driving, can introduce unsafe factors due to uncertainty. Thus, in this paper, we introduce and implement an improved algorithm Advantage Actor-Critic Method of Actor-Critic Method to improve the safety of autonomous driving policy selection, and give a specific push-to process, including the combination of advantage function with Reinforce with Baseline algorithm, we applied the algorithm to the agent of the scenario designed in this paper, and militarily expanded the design based on the autopilot scenario, the agent will be trained by the A2C policy-value network learning the rules of the designed scenario for adversarial gaming, and the value of the loss function is reduced from about 4.5 to about 1.7; and also with the game tree related algorithm for gaming in the pentad scenario, the win rate are above 60% Finally, the algorithm has better performance in strategy selection from the game results.
One-Time Rewritable Blockchain with Traitor Tracing and Bilateral Access Control

One of the solutions for rewriting blockchain data is to replace the standard hash function with a chameleon hash function. This enables users with the trapdoor to generate hash collision for rewriting operation. However, such rewritable blockchain solution may allow users to have unlimited rewriting permissions. This is undesirable as rewritable blockchains should still maintain a certain degree of immutability. In this paper, we propose a fine-grained one-time rewritable blockchain scheme that supports traitor tracing and bilateral access control. The proposed solution is the first that combines one-time chameleon hash function and traceable attribute-based encryption to realize one-time rewriting blockchain at the same time tracking for malicious users who leak the private key. Furthermore, our solution offers bilateral access control by integrating with fog computing. We also verify the performance of the proposed scheme with experimental analysis.
A Secure Remote Password Protocol From The Learning With Errors Problem

Secure Remote Password (SRP) protocol is an essential password-authenticated key exchange (PAKE) protocol based on the discrete logarithm problem (DLP). The protocol is specifically designed to obtain a session key and it has been widely used in various scenarios due to its attractive security features. In the SRP protocol, the server is not required to save any data directly associated with passwords. And this makes attackers who manage to corrupt the server fail to impersonate the client unless performing a brute-force search for the password. However, the development of quantum computing has potentially made classic DLP-based public-key cryptography schemes not secure, including the SRP protocol. So it is significant to design a quantum-resistant SRP protocol. In this paper, based on the original scheme, we propose a post-quantum SRP protocol from the learning with errors (LWE) problem. And we give rigorous proof and analyses on the correctness and security of the scheme. Besides being resistant to known quantum attacks, it maintains the various secure qualities of the original protocol.
Smart Grid Data Aggregation Scheme Based on Local Differential Privacy

With the development of IoT technology, smart grid has gradually replaced the traditional grid. Smart grid is convenient and fast. It can provide real-time residential electricity monitoring and forecasting, give users better electricity guidance and save a lot of labor costs. Smart meters send customers’ electricity consumption data to the gateway, which aggregates the data and then sends it to the electricity consumption control center. But in this process, there will be a security problem of leakage of customer’s electricity consumption data. Most of the current user data privacy protection collection schemes use homomorphic encryption and randomization techniques. However, some of these schemes require a trusted third-party entity, and some may cause significant computational overhead. Due to the limited computational resources of smart meters, these techniques may be impractical. In this paper, we propose a local differential privacy data aggregation protection scheme based on the idea of grouping perturbation of electricity consumption data according to data domains. Experiments show that our scheme can provide statistical estimates of electricity in the region while satisfying the privacy protection of customers’ electricity consumption data. Moreover, our scheme has small computational and communication overheads, which can meet the application requirements in practical scenarios.