Anomaly detection systems can accurately identify malicious network traffic, providing network security. With the development of internet technology, network attacks are becoming more and more sourced and complicated, making it difficult for traditional anomaly detection systems to effectively analyze and identify abnormal traffic. At present, deep neural network (DNN) technology achieved great results in terms of anomaly detection, and it can achieve automatic detection. However, there still exists misclassified traffic in the prediction results of deep neural networks, resulting in redundant alarm information. This paper designs a two-level anomaly detection system based on deep neural network and association analysis. We made a comprehensive evaluation of experiments using DNNs and other neural networks based on publicly available datasets. Through the experiments, we chose DNN-4 as an important part of our system, which has high precision and accuracy in identifying malicious traffic. The Apriori algorithm can mine rules between various discretized features and normal labels, which can be used to filter the classified traffic and reduce the false positive rate. Finally, we designed an intrusion detection system based on DNN-4 and association rules. We conducted experiments on the public training set NSL-KDD, which is considered as a modified dataset for the KDDCup 1999. The results show that our detection system has great precision in malicious traffic detection, and it achieves the effect of reducing the number of false alarms.

Algorithms used to detect Wi-Fi transmitter transients are discussed in this paper, and the start of a Wi-Fi transmitter transient, in our opinion, has a new definition. Current algorithms, namely Variance Fractal Dimension Threshold Detection, Bayesian Step Change Detection and Phase Detection are analyzed at the beginning of this article. According to the disadvantages such as complexity, accuracy and a threshold needed in the final determination of these traditional methods, an improved algorithm based on Mean Change Point Detection is put forward. Threshold for determination and nonparametric estimation for hypothesis test are not needed in our improved approach, it detects the start of transient only by calculating the maximum of the difference of statistic. Moreover, the experimental results show that this improved method outperforms the other three methods, particularly in the case of low SNR.

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Herein, we describe the copper-catalyzed arylalkylation of activated alkenes via hydrogen-atom transfer and aryl migration strategy. The reaction was carried out through a radical-mediated continuous migration pathway using N-fluorosulfonamides as the alkyl source. The primary, secondary, and tertiary alkyl radicals formed by intramolecular hydrogen-atom transfer proceeded smoothly. This methodology is an efficient approach for the synthesis of various amide derivatives possessing a quaternary carbon center with good yields and high regioselectivity.

Research on intercultural couplehood often relies on cultures and 'solid identities' to explain the couples' experiences. By looking at the media construction of an intercultural couple on Chinese television, we are interested in how they are presented and co-constructed through different perspectives, especially if and how (cultural) differences are used by the participants to enact the couple's identification. We take a dynamic 'liquid' approach to linguistic discourse analysis, through dialogism and utterance theory, to identify the voices and the related discursive strategies that contribute to create and 'imagine' the couple.