This work proposes a population evolution algorithm to deal with optimization problems based on the evolution characteristics of the Phasmatodea (stick insect) population, called the Phasmatodea population evolution algorithm (PPE). The PPE imitates the characteristics of convergent evolution, path dependence, population growth and competition in the evolution of the stick insect population in nature. The stick insect population tends to be the nearest dominant population in the evolution process, and the favorable evolution trend is more likely to be inherited by the next generation. This work combines population growth and competition models to achieve the above process. The implemented PPE has been tested and analyzed on 30 benchmark functions, and it has better performance than similar algorithms. This work uses several engineering optimization problems to test the algorithm and obtains good results.

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Simplified Phasmatodea population evolution algorithm for optimization

Aiming at the intrusion detection problem of the wireless sensor network (WSN), considering the combined characteristics of the wireless sensor network, we consider setting up a corresponding intrusion detection system on the edge side through edge computing. An intrusion detection system (IDS), as a proactive network security protection technology, provides an effective defense system for the WSN. In this paper, we propose a WSN intelligent intrusion detection model, through the introduction of the k-Nearest Neighbor algorithm (kNN) in machine learning and the introduction of the arithmetic optimization algorithm (AOA) in evolutionary calculation, to form an edge intelligence framework that specifically performs the intrusion detection when the WSN encounters a DoS attack. In order to enhance the accuracy of the model, we use a parallel strategy to enhance the communication between the populations and use the Lévy flight strategy to adjust the optimization. The proposed PL-AOA algorithm performs well in the benchmark function test and effectively guarantees the improvement of the kNN classifier. We use Matlab2018b to conduct simulation experiments based on the WSN-DS data set and our model achieves 99% ACC, with a nearly 10% improvement compared with the original kNN when performing DoS intrusion detection. The experimental results show that the proposed intrusion detection model has good effects and practical application significance.

/pdf/an-enhanced-intrusion-detection-model-based-on-improved-knn-18lk5j1y.pdf

An Enhanced Intrusion Detection Model Based on Improved kNN in WSNs

A novel trust-based security and privacy model for Internet of Vehicles using encryption and steganography

Embellishment of Upconversion Nanoparticles with Ultrasmall Perovskite Quantum Dots for Full-Color Tunable, Dual-Modal Luminescence Anticounterfeiting

The wireless sensor network is a network composed of sensor nodes self-organizing through the application of wireless communication technology. The application of wireless sensor networks (WSNs) requires high security, but the transmission of sensitive data may be exposed to the adversary. Therefore, to guarantee the security of information transmission, researchers propose numerous security authentication protocols. Recently, Wu et al. proposed a new three-factor authentication protocol for WSNs. However, we find that their protocol cannot resist key compromise impersonation attacks and known session-specific temporary information attacks. Meanwhile, it also violates perfect forward secrecy and anonymity. To overcome the proposed attacks, this paper proposes an enhanced protocol in which the security is verified by the formal analysis and informal analysis, Burross-Abadii-Needham (BAN) logic, and ProVerif tools. The comparison of security and performance proves that our protocol has higher security and lower computational overhead.

/pdf/a-provably-secure-three-factor-authentication-protocol-for-i6h92g6di3.pdf

Bin Yan

Papers

Digital watermarking with improved SMS applied for QR code