With the widely deployment of wireless sensor networks in smart industrial systems, lots of unauthorized attacking from the adversary is greatly threating the security and privacy of the entire industrial systems, of which node replication attacks can hardly be defended since it is conducted in the physical layer. To solve this problem, we propose a secure random key distribution scheme, called SRKD, which provides a new method for the defense against the attack. Specifically, we combine a localized algorithm with a voting mechanism to support the detection and revocation of malicious nodes. We further change the meaning of the parameter s to help prevent the replication attack. Furthermore, the experimental results show that the detection ratio of replicate nodes exceeds 90% when number of network nodes reaches 200, which demonstrates the security and effectiveness of our scheme. Compared with existing state-of-the-art schemes, SRKD also has good storage and communication efficiency.

It is non-trivial to predict the prices of precious metals since a number of factors can affect the fluctuations of precious metal prices. Either parametric models or machine learning models cannot accurately forecast the precious metal prices. Though deep learning approaches show their strengths in extracting key features from complicated data, they have the limitations of learning localization and losing some temporal and spatial features. The recent advances in attention mechanisms bring the opportunities to overcome the limitation of deep learning models. In this paper, we originally propose a Regularization Self-Attention Regression Model for precious metal price prediction. In particular, the proposed RSAR model consists of convolutional neural network (CNN) component and Long Short-Term Memory Neural Networks (LSTM) component. Integrating with self-attention mechanism, this model can extract both spatial and temporal features from precious metal price data. Meanwhile, the proper configuration of regularization functions can also lead to the further performance improvement. Extensive experiments on realistic precious metal price dataset show that our proposed approach outperforms other conventional machine learning and deep learning methods.

A vulnerability or error in a smart contract will lead to serious consequences including loss of assets and leakage of user privacy. Established smart contract vulnerability detection tools define vulnerabilities through symbolic execution, fuzz testing, and other methods requiring extremely specialized security knowledge. Even so, with the development of vulnerability exploitation techniques, vulnerability detection tools customized by experts cannot cope with the deformation of existing vulnerabilities or unknown vulnerabilities. The vulnerability detection based on machine learning developed in recent years studies vulnerabilities from different dimensions and designs corresponding models to achieve a high detection rate. However, these methods usually only focus on some features of smart contracts, or the model itself does not have universality. Experimental results on the publicly large-scale dataset SmartBugs-Wild demonstrate that this paper's method not only outperforms existing methods in several metrics, but also is scalable, general, and requires less domain knowledge, providing a new idea for the development of smart contract vulnerability detection.

As a distributed communication and storage system, blockchain forms a Computing Power Blockchain Network (CPBN) by integrating computing nodes and network resources. However, its open architecture faces major security threats such as Sybil attacks, computational fraud, and DDoS attacks. Traditional detection methods often fail in dynamic environments with scarce domain data. To address this, we developed a lightweight blockchain simulator to generate Sybil and DDoS attack scenarios, constructing a 14-dimensional feature dataset. To address Sybil attacks and distributed denial-of-service attack scenarios, this paper proposes an adaptive attention random forest convolutional neural network anomaly detection method (CAA-RF). Our approach uses multi-layer convolutional operations to capture high-order data correlations, combines attention mechanisms for global dependency modeling, and employs random forest for robust anomaly detection, enabling effective real-time security protection for blockchain systems.