In the process of power transmission and distribution, non-technical losses are usually caused by users’ abnormal power consumption behavior. It will not only affect the dispatch and operation of the distribution network, bring hidden dangers to the security of the power grid, but also damage the operating costs of power companies and disrupt the operation of the power market. Aiming at users’ abnormal electricity consumption behavior, this paper proposes a model based on particle swarm optimization and long-short term memory with the attention mechanism (PSO-Attention-LSTM). Firstly, according to the actual electricity theft behavior, six typical electricity theft modes are summarized, and 4 composite modes are obtained by combining them, so as to comprehensively test the detection performance of the model for various electricity theft behaviors. Secondly, a detection model based on PSO-Attention-LSTM is proposed, and the model is built using the TensorFlow framework. The model uses the attention mechanism to give different weights to the hidden state of LSTM, which reduces the loss of historical information, strengthens important information and suppresses useless information. Use PSO to solve the difficult problem of model parameter selection, and optimize the hyperparameters to improve the model performance. Finally, the data set of the University of Massachusetts was used for simulation and compared with convolutional neural network-long short term memory (CNN-LSTM), attention mechanism-based long short term memory (Attention-LSTM), LSTM, gated recurrent unit (GRU), support vector regression (SVR), random forest (RF) and linear regression (LR) to verify the effectiveness and accuracy of the method used in this article. In this paper, Matlab software is used to analyze and visualize the detection result data.

/pdf/abnormal-detection-of-electricity-consumption-of-user-based-3caylyyuue.pdf

Abnormal Detection of Electricity Consumption of User Based on Particle Swarm Optimization and Long Short Term Memory With the Attention Mechanism

At present, the explosive growth of data and the mass storage state have brought many problems such as computational complexity and insufficient computational power to clustering research. The distributed computing platform through load balancing dynamically configures a large number of virtual computing resources, effectively breaking through the bottleneck of time and energy consumption, and embodies its unique advantages in massive data mining. This paper studies the parallel k-means extensively. This article first initializes random sampling and second parallelizes the distance calculation process that provides independence between the data objects to perform cluster analysis in parallel. After the parallel processing of the MapReduce, we use many nodes to calculate distance, which speeds up the efficiency of the algorithm. Finally, the clustering of data objects is parallelized. Results show that our method can provide services efficiently and stably and have good convergence.

/pdf/k-means-clustering-algorithm-and-its-simulation-based-on-2rl1dbmbo1.pdf

K -Means Clustering Algorithm and Its Simulation Based on Distributed Computing Platform

Data deduplication technique could greatly save the storage overhead of the cloud by eliminating duplicated data and retaining one copy. In order to ensure the privacy of the data against an untrusted cloud, many cryptographic schemes have been proposed to make deduplication feasible in ciphertext. A typical scheme is Message-Locked Encryption (MLE), which takes cryptographic hash values of messages as encryption keys. However, MLE is vulnerable to side-channel attacks. Current solutions trying to mitigate these attacks raise either expensive overhead or security drawbacks. In this paper, we propose a secure data deduplication system against an untrusted cloud with resistance to two typical side-channel attacks, namely probe attack and key-cache attack. Our system uses fog computing to devise two new techniques to solve the two side-channel attacks with new security and efficiency trade-offs. The analysis and evaluation show that our system achieves better trade-offs compared with previous works. 

Secure Deduplication-Based Storage Systems With Resistance to Side-Channel Attacks via Fog Computing

Distributed energy balance routing algorithm for wireless sensor network based on multi-attribute decision-making

As the number of smart cars grows rapidly, vehicular crowdsensing (VCS) becomes gradually popular. In a VCS infrastructure, sensing devices and computing units hold on smart cars as well as cloud server form an IoT-edge-cloud continuum to perform real-time sensing tasks. In order to encourage the smart cars to participate the real-time VCS process, blockchain technology can be combined with VCS to provide automated incentive for VCS data trading without relying on trusted third parties. However, directly using blockchain to enforce VCS data trading process incurs expensive service fees and participants still can conduct various misbehavior. In this paper, we propose a secure blockchain-based data trading system for VCS named BTT system to address the above issues. In particular, we first integrate the blockchain-based data trading process with a lightweight privacy-preserving truth discovery algorithm to ensure the accuracy of sensing data while preserving data privacy. we then propose a gas-aware optimization mechanism to minimize the gas consumption of the data trading process. Finally, we carefully design a distributed judgment mechanism to regulate all participants to behave correctly in the data trading process. To demonstrate the practicability of our design, we implement a prototype of BTT system deployed on Ethereum test network and conduct extensive simulations.

https://dl.acm.org/doi/pdf/10.1145/3582008

Secure and Lightweight Blockchain-based Truthful Data Trading for Real-Time Vehicular Crowdsensing

Radio Frequency Identiocation (RFID) is a key emerging technology to improve data sharing in item distribution systems. By attaching RFID tags to items, item related data can be bound to items and participants involved in an item distribution system can directly store, access and update the data by interrogating the tags. Such a iexible data access manner of RFID technology, however, raises privacy and security concerns. In this paper, we focus on a special item distribution system named RFID-enabled Third-party Distribution (RTD) system and identify two inherent security and privacy requirements. We further design a Secure RTD system called Ants, which uses cryptography to protect item messages carried by tags to satisfy both of the requirements while preserving the iexible data access manner of RFID technology. Ants introduces two new techniques namedcommitmentaccumulationandselectivemessageprooffor memory-constrained tags to carry long crypto-item messages. We conduct theoretical analysis and experiments to demonstrate the security and efficiency of Ants.

Ants can Carry Cheese: Secure and Private RFID-Enabled Third-Party Distribution

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Ants can Carry Cheese: Secure and Private RFID-Enabled Third-Party Distribution