Biblio

In this paper, differential privacy protection method is applied to matrix factorization method that used to solve the recommendation problem. For centralized recommendation scenarios, a collaborative filtering recommendation model based on matrix factorization is established, and a matrix factorization mechanism satisfying ε-differential privacy is proposed. Firstly, the potential characteristic matrix of users and projects is constructed. Secondly, noise is added to the matrix by the method of target disturbance, which satisfies the differential privacy constraint, then the noise matrix factorization model is obtained. The parameters of the model are obtained by the stochastic gradient descent algorithm. Finally, the differential privacy matrix factorization model is used for score prediction. The effectiveness of the algorithm is evaluated on the public datasets including Movielens and Netflix. The experimental results show that compared with the existing typical recommendation methods, the new matrix factorization method with privacy protection can recommend within a certain range of recommendation accuracy loss while protecting the users' privacy information.

With the popularization and application of Internet of Things technology, the degree of intelligence of the home system is getting higher and higher. As an important part of the smart home, the security system plays an important role in protecting against accidents such as flammable gas leakage, fire, and burglary that may occur in the home environment. This design focuses on sensor signal acquisition and processing, wireless access, and cloud applications, and integrates Cypress’s new generation of PSoC 6 MCU, CYW4343W Wi-Fi and Bluetooth dual-module chips, and Amazon’s AWS cloud into smart home security System designing. First, through the designed air conditioning and refrigeration module, fire warning processing module, lighting control module, ventilation fan control module, combustible gas and smoke detection and warning module, important parameter information in the home environment is obtained. Then, the hardware system is connected to the AWS cloud platform through Wi-Fi; finally, a WEB interface is built in the AWS cloud to realize remote monitoring of the smart home environment. This design has a good reference for the design of future smart home security systems.

With increasing demand for travelling, high-quality network service is important to people in vehicle cabins. Visible light communication (VLC) system is more appropriate than wireless local area network considering the security, communication speed, and narrow shape of the cabin. However, VLC exhibits technical limitations, such as uneven distribution of optical signals. In this regard, we propose a novel weight search bat algorithm (WSBA) to calculate a set of optimal power adjustment factors to reduce fluctuation in signal distributions. Simulation results show that the fairness of signal distribution in the cabin optimized by WSBA is better than that of the non-optimized signal distribution. Moreover, the coverage rate of WSBA is higher than that of genetic algorithm and particle swarm optimization.

Distributed Denial of Service (DDoS) attacks are some of the most persistent threats on the Internet today. The evolution of DDoS attacks calls for an in-depth analysis of those attacks. A better understanding of the attackers' behavior can provide insights to unveil patterns and strategies utilized by attackers. The prior art on the attackers' behavior analysis often falls in two aspects: it assumes that adversaries are static, and makes certain simplifying assumptions on their behavior, which often are not supported by real attack data. In this paper, we take a data-driven approach to designing and validating three DDoS attack models from temporal (e.g., attack magnitudes), spatial (e.g., attacker origin), and spatiotemporal (e.g., attack inter-launching time) perspectives. We design these models based on the analysis of traces consisting of more than 50,000 verified DDoS attacks from industrial mitigation operations. Each model is also validated by testing its effectiveness in accurately predicting future DDoS attacks. Comparisons against simple intuitive models further show that our models can more accurately capture the essential features of DDoS attacks.

A successful Smart Grid system requires purpose-built security architecture which is explicitly designed to protect customer data confidentiality. In addition to the investment on electric power infrastructure for protecting the privacy of Smart Grid-related data, entities need to actively participate in the NIST interoperability framework process; establish policies and oversight structure for the enforcement of cyber security controls of the data through adoption of security best practices, personnel training, cyber vulnerability assessments, and consumer privacy audits.