The spatial features of emitted wireless signals are the basis of location distinction and determination for wireless indoor localization. Available in mainstream wireless signal measurements, the Received Signal Strength Indicator (RSSI) has been adopted in vast indoor localization systems. However, it suffers from dramatic performance degradation in complex situations due to multipath fading and temporal dynamics. Break-through techniques resort to finer-grained wireless channel measurement than RSSI. Different from RSSI, the PHY layer power feature, channel response, is able to discriminate multipath characteristics, and thus holds the potential for the convergence of accurate and pervasive indoor localization. Channel State Information (CSI, reflecting channel response in 802.11 a/g/n) has attracted many research efforts and some pioneer works have demonstrated submeter or even centimeter-level accuracy. In this article, we survey this new trend of channel response in localization. The differences between CSI and RSSI are highlighted with respect to network layering, time resolution, frequency resolution, stability, and accessibility. Furthermore, we investigate a large body of recent works and classify them overall into three categories according to how to use CSI. For each category, we emphasize the basic principles and address future directions of research in this new and largely open area.

From RSSI to CSI: Indoor Localization via Channel Response, A survey on indoor localization using PHY-layer information

Aiming at the dynamic uncertainty and prediction accuracy of security situation prediction in complex cloud network environment, a prediction model of cloud security situation based on evolutionary functional network is proposed. Firstly, the evolutionary functional network model is constructed by combining the evolutionary algorithm with the functional network, which solves the problem of basis function selection and basis function coefficient correction of the prediction model. Secondly, the stochastic approximation algorithm is used to process and comprehend the cloud security situation elements, and the computational complexity of the prediction model is reduced by the dimensionality reduction method. Finally, by constructing the credibility matrix of the uncertain influence relationship of security situation elements, we use the multivariate non-linear regression algorithm to predict the cloud security situation. The simulation results show that compared with BP model and RAN-RBF model, the prediction accuracy of the proposed model is improved by 8.2% and 6.9% respectively, and the convergence efficiency is improved by 12.3% and 10.8% respectively.

A prediction model of cloud security situation based on evolutionary functional network

Existing network security situational awareness assessment and prediction are not fully considered network defense utility, time factor and other indicators, and the randomness of attacks and the accuracy of the prediction of attack intentions and methods may appear both in active and passive defense this situation leads to uncertainty in attack prediction, which makes it impossible to discover defects in the network system in a timely manner, and there is a lack of effective emergency response strategies for impending or already occurring network attacks. Therefore, this paper proposes a network behavior calculation model based on the Lanchester equation of time action factor is proposed. This paper uses smooth differential manifold homeomorphic transformation to define network behavior, defines the calculation method of behavior utility based on the principle of differential geometry, combines the second linear law of the Lanchester equation and the square law, and uses the time action factor to defend the active defense. The simulation results show that the model can be used to analyze the network offensive and defensive process, and can effectively predict the network offensive and defensive results under both active and passive defense.

Network Security Situation Prediction of Improved Lanchester Equation Based on Time Action Factor

Real-Time Machine Learning-based fault Detection, Classification, and locating in large scale solar Energy-Based Systems: Digital twin simulation

To address the situation prediction problem in the network security situation awareness, this paper presents a prediction method of network security situation based on the algorithm of IHS_LSSVR. An improved Harmony Search(IHS)algorithm is proposed since the principle of the Harmony Search(HS)algorithm is studied. This method embeds the Least Squares Support Vector machine for Regression(LSSVR)in the process of the objective function calculation of the improved harmony search algorithm, and takes advantage of the global searching ability of the IHS algorithm to optimize the parameters of the LSSVR. To some extent, this enhances the learning ability and generalization ability of the LSSVR. Simulation experiments show that this method has better prediction affection in comparison with other existing prediction methods.

Method of network security situation prediction based on IHS_LSSVR

The Internet of Things (IoT) is a new technology rapidly developed in various fields in recent years. With the continuous application of the IoT technology in production and life, the network security problem of IoT is increasingly prominent. In order to meet the challenges brought by the development of IoT technology, this paper focuses on network security situational awareness. The network security situation awareness is basic of IoT network security. Situation prediction of network security is a kind of time series forecasting problem in essence. So it is necessary to construct a modification function that is suitable for time series data to revise the kernel function of traditional support vector machine (SVM). An improved network security situation awareness model for IoT is proposed in this paper. The sequence kernel support vector machine is obtained and the particle swarm optimization (PSO) method is used to optimize related parameters. It proves that the method is feasible by collecting the boundary data of a university campus IoT network. Finally, a comparison with the PSO-SVM is made to prove the effectiveness of this method in improving the accuracy of network security situation prediction of IoT. The experimental results show that PSO-time series kernel support vector machine is better than the PSO-Gauss kernel support vector machine in network security situation prediction. The application of the Hadoop platform also enhances the efficiency of data processing.

/pdf/situation-prediction-of-large-scale-internet-of-things-4tw7jkta8f.pdf

Situation prediction of large-scale Internet of Things network security

With the popularity of mobile computing, WiFi has become one of the essential technologies for people to access the Internet, and WiFi security has also become a major threat for mobile computing. The Evil-Twin attack can steal a large amount of private data by forging the same SSID as the real Access Point. This paper proposes a passive Evil-Twin attack detection scheme through CSI in physical layer. First of all, we propose a location model based on the edge of landmark area. In this model, the improved MUSIC algorithm is used to calculate each AP’s AoA by CSI phase. Secondly, it proposes an algorithm for simplifying the generation of location model files, which is the dataset of a small number of AoA and RSSI samples. Finally, according to location model, attack detection algorithm combines a large number of crowd sensing data to determine whether it is a malicious AP. Experiments show that our attack detection system achieves a higher detection rate.

Detecting Evil-Twin Attack with the Crowd Sensing of Landmark in Physical Layer

The invention provides an archive management system based on blue light storage and an ant colony search algorithm thereof, comprising an archive collection system, an archive storage server, an optical disk library management server and an optical disk library Specifically, electronic archive files are assembled, compressed and indexed by the archive collection system in the form of data streams The ant colony search algorithm has the following specific steps: 1 Constructing a file retrieval path; 2 Updating that pheromone of archives retrieval; 3 Optimizing evaporation factor of searchpath The algorithm can automatically adjust the information volatility factor, analyze the keywords, and call the relevant persistence layer data through the algorithm The archives management systemadopts the optimized retrieval arithmetic, According to the characteristics of archives, ACA algorithm is used, its process and algorithm are analyzed, by combining the pheromone volatility, the effect of the pheromone volatility factor on the efficiency of algorithm retrieval is improved, and by combining differential evolution processing, efficient retrieval efficiency of massive data is realized

A archive management system based on blue light storage and its ant colony search algorithm

With the development of the Internet, online shopping, online movies, online music and other online services are greatly convenient for people’s life. However, due to the widespread false information in these websites, the interests of users and privacy are threatened. To address this problem, we designed a trustworthy evaluation system based on blockchain named TESB2. TESB2 uses blockchain technology to effectively protect the data and privacy of users. It effectively guides the evaluation behavior of users and ensures the fairness and credibility of evaluation information by combining the evaluation behavior of users with reputation rewards and punishments. And we innovatively propose a malicious user detection method by combining blockchain technology and machine learning algorithm. Finally, we showed TESB2 through the movie scenario, and its performance was satisfactory.

Xiuliang Mo

Papers

Situation prediction of large-scale Internet of Things network security

Detecting Evil-Twin Attack with the Crowd Sensing of Landmark in Physical Layer

A archive management system based on blue light storage and its ant colony search algorithm

A Trustworthy Evaluation System Based on Blockchain