Thanks to the great advancement of cognitive computing, artificial intelligence, big data, and the Internet of Things (IoT) technologies, the fusion of the physical and virtual worlds is changing people's lifestyles. Although the research and deployment of cyber-physical systems (CPSs) are notably promoted by cognitive computing, the reliability and large-scale application of CPSs are still significantly challenged by some security issues. Therefore, it is meaningful to clarify and address the weaknesses of current intrusion detection methods for CPSs and enhance the ability to identify, analyze, and predict to improve the performance of intrusion detection. In this article, we first propose a novel self-learning spatial distribution algorithm, named Euclidean distance-based between-class learning (EBC learning), which improves between-class learning by calculating the Euclidean distance (ED) among k-nearest neighbors of different classes. In addition, a cognitive computing-based intrusion detection method named border-line SMOTE and EBC learning based on random forest (BSBC-RF) is also proposed based on the EBC learning for industrial CPSs. The experimental results over a real industrial traffic dataset show that the proposed EBC learning has strong spatial constraint capability and can improve the prediction and recognition performance. Compared with the eight state-of-the-art methods, the proposed method has an ACC exceeding 99.5%, false alarm rate (FAR) less than 0.06%, and F1 close to 0.99, which is still superior to other ones.

The rapid spread of the COVID-19 has not only affected personal health and economy, but also revolutionized people's lifestyles. As more people turn to work and socialize online, the development of unmanned technologies based on the Internet of Vehicles (IoV), such as unmanned delivery, unmanned vehicles, unmanned transportation, etc., will become an inevitable trend. However, all kinds of intelligent terminals for unmanned equipment require a large amount of data interaction with devices such as cloud servers, mobile terminals, and roadside terminals, which poses cyber security risks. Furthermore, the outbreak of COVID-19 has prompted people to put forward higher demands for the security of network communications. Unfortunately, the current intrusion detection methods based on machine learning still have weaknesses such as low accuracy and low efficiency when faced with unbalanced data distribution. To solve the above problems, we propose a novel Tree-based BLS (TBLS) intrusion detection method according to the idea of ensemble learning and decision tree (CART and J48). The performance of TBLS was tested on the NSL-KDD dataset and the UNSW-NB15 dataset respectively, which contain a variety of malicious traffic types for attacks on the IoV. The results show that our proposed method can achieve higher accuracy rate and lower false alarm rate, compared with the existing 16 solutions.

Many real-world problems can be classified as multimodal optimization problems (MMOPs), which require to locate global optima as more as possible and refine the accuracy of found optima as high as possible. When dealing with MMOPs, how to divide population and obtain effective niches is a key to balance population diversity and convergence during evolution. In this paper, a self-organizing map (SOM) based differential evolution with dynamic selection strategy (SOMDE-DS) is proposed to improve the performance of differential evolution (DE) in solving MMOPs. Firstly, a SOM based method is introduced as a niching technique to divide population reasonably by using the similarity information among different individuals. Secondly, a variable neighborhood search (VNS) strategy is proposed to locate more possible optimal regions by expanding the search space. Thirdly, a dynamic selection (DS) strategy is designed to balance exploration and exploitation of the population by taking advantages of both local search strategy and global search strategy. The proposed SOMDE-DS is compared with several widely used multimodal optimization algorithms on benchmark CEC'2013. The experimental results show that SOMDE-DS is superior or competitive with the compared algorithms.

With the development of the Internet, people have paid more attention to privacy protection, and privacy protection technology is widely used. However, it also breeds the darknet, which has become a tool that criminals can exploit, especially in the fields of economic crime and military intelligence. The darknet detection is becoming increasingly important; however, the darknet traffic is seriously unbalanced. The detection is difficult and the accuracy of the detection methods needs to be improved. To overcome these problems, we first propose a novel learning method. The method is the Chebyshev distance based Between-class learning (CDBC), which can learn the spatial distribution of the darknet dataset, and generate "gap data". The gap data can be adopted to optimize the distribution boundaries of the dataset. Second, a novel darknet traffic detection method is proposed. We test the proposed method on the ISCXTor 2016 dataset and the CIC-Darknet 2020 dataset, and the results show that CDBC can help more than 10 existing methods improve accuracy, even up to 99.99%. Compared with other sampling methods, CDBC can also help the classifiers achieve higher recall.