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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.

Through big data technology, we analyze the effect of functional fitness training on the physical quality of cross-country skiers and explore a scientific training method suitable for cross-country skiers. The MCDM-Apriori algorithm is proposed based on matrix compression, partitioning, and subsumption to solve the problem that the Apriori algorithm continuously generates the candidate set of intermediate processes during the operation and scans the original database several times, which causes huge consumption to the computer. The HDWA-Kmeans algorithm was used to analyze the effect of experimental training on the physical quality of athletes before and after the training, and the MCDM-Apriori algorithm was used to analyze the quality of functional movements to demonstrate the effect of functional training on the physical quality of cross-country skiers. In the physical quality comparison, the increase of 15 quality indexes in the experimental group was greater than that in the control group except for the push-up strength exhaustion, in which the increase of 49.91% and 54.05% in the experimental group of single-leg squat left and single-leg squat right, respectively. The increase in the quality of movement screening indexes compared with the experimental group, except for the deep squat, all other movements were increased to varying degrees, including a 20.77% increase in quadriceps rotation stability, while the increase in the control group was much worse than the experimental group. The results indicate that the functional training method and the training intensity and volume are consistent with and adapted to the physical training needs and physical characteristics of the athletes in the experimental group.

This paper analyzes the basic customer management process and chooses the Apriori algorithm to build a CRM model based on data mining. In addition, this paper designs a customer relationship management system based on big data. The system is divided into three layers: data source, batch processing, and real-time processing. In the part of constructing the system architecture, this paper adopts the Hadoop platform. The batch processing layer, it has consisted of four parts, which include No SQL database, Oracle database, ETL architecture, and Hadoop platform. This paper gives the logical architecture design diagram. The real-time processing layer mainly includes a real-time decision engine and service bus. The key part of this layer is the real-time decision engine, in the design of which the Bayesian algorithm and product recommendation prediction model are used. Finally, this paper takes K company as an example to demonstrate the model and management system. After applying the analytical model and management system, the sales of K company keep increasing.

Based on the special nature of language subjects, if the teaching methods adopted by teachers in the language classroom are not appropriate, it is easy to make the classroom atmosphere boring and depressing, which is not conducive to mobilizing students’ learning initiative. The use of digital media technology is more likely to increase students’ enthusiasm for learning and enhance the teaching impact. To address language education’s shortcomings, this paper utilizes digital media technology to enhance language education through media integration. In order to evaluate the teaching effect of integrating digital media in language teaching, this paper combines the Apriori algorithm and C4.5 algorithm to analyze the knowledge and information about the teaching law and teaching situation from a large number of teaching evaluation results, which is used to judge the effect of the application of digital media in language teaching, after comparing the combination algorithm with the traditional rule-mining algorithm. The Apriori algorithm is applied to secondary school language teaching, incorporating digital media for correlation analysis, and the pre-processed dataset is mined and the quality of language teaching in digital media has a great relationship with the quality of teaching and the content of teaching. In this paper, based on the Apriori-C4.5 mining algorithm, we can analyze the factors associated with teaching effectiveness and propose strategies for digital media application in language education innovation.

With the acceleration of the application of information technology in Colleges and universities in China, the efficiency of higher education is constantly improving. The focus of teaching management in Colleges and universities is to continuously improve the teaching level of colleges and universities, and the key is to strengthen the management of students’ performance. Performance warning is a form of student performance management. In recent years, data mining technology is more and more mature, and its application is also very wide. Many students have applied data mining technology to university management. In this paper, we apply data mining technology to college students’ performance early warning, and use Apriori algorithm in association rules to design and build college students’ performance early warning platform, and select two classes of students as the research object to verify. In this study, we choose the English course scores of two classes as the test data, and define the performance warning, which is based on the score below 60. The results show that six students in class a will be subject to performance warning, while seven students in class B will be subject to performance warning. In addition, the performance early warning platform designed by this method, the early warning accuracy rate is as high as 92.85%, the accuracy rate is high, has certain application advantages.

To reveal complex causes of aircraft events, this paper aims to mine association rules between the trigger probability and relative strength via a modified Apriori algorithm. Clustering is adopted for data preprocessing and TF–IDF value calculation. Causative item sets of aircraft events are obtained based on the accident causation 2–4 model and are coded to establish code indicators. By avoiding the use of statistical methodologies to resolve not-a-number (NaN) values for altering the interrelations among causes, an enhancement in the Apriori algorithm is proposed by considering frequent items. By extracting frequent patterns, in this paper, all the association rules that satisfy three perspectives (support, confidence and lift) are determined by constantly generating and pruning candidate item sets. A network graph is used to visualize the association rules between different unsafe events and all types of causes. Finally, 9835 representative pieces of data, including general unsafe events, general incidents and serious incidents from the Southwest Air Traffic Management Bureau, are selected for analysis. The results show that improper energy allocation, poor conflict resolution ability, inadequate onsite management duties, adoption of a luck mentality, and occurrence of controller oversight are highly correlated with general unsafe events, and failure to rectify incorrect recitation is notably correlated with general incidents, while inadequate manual promotion, lack of conflict judgement and insufficient safety management are strongly correlated with serious incidents. This study quantitatively reveals the potential patterns and characteristics of mutual interactions among various types of historical aircraft events and highlights directions for controllable prevention and prediction of aircraft events.

Nowadays, supermarkets and retail stores all use software systems with databases to store customer transactions. Over time, the volume of data is also increasing and it contributes a lot of hidden value in this data warehouse, mining data from historical transactions will find out the buying patterns and behavior of consumers, which can assist in improving sales by reaching customers more precisely. Data-mining techniques allow us to exploit synthetic information in many aspects, such as association rules for statistics and decision support in many fields. Most users of e-commerce systems or web platforms are concerned about privacy protection, such as privacy requirements for name, occupation, age, interests, residence, or sales transactions on the e-commerce system. Therefore, protecting the privacy of electronic service users in data mining is also an important factor that needs to be considered. For those important reasons, the Apriori algorithm was researched and extrapolated into a new S-Apriori algorithm for the concept of seasonal shopping. This paper applied the S-Apriori, ORM model, SQL language, and C# to build the libraries for the forecast of Seasonal Consumption Behavior of Consumers. Also, a new Thanh and Huh Cryptography algorithm for privacy-preserving filters is proposed for data-mining processing privacy protection. The paper experimented on two datasets based on a small dataset with 37 records and the Adventure large dataset of Microsoft with 172,459 records, while the software provides association rules with the corresponding confidence ratio for users to easily make decisions. In addition, the model will be packaged and published to the Microsoft Nuget ecosystem, developers and researchers can use it to develop association rule mining systems or further extend it based on the new S-Apriori model.

The emergence of mental health problems of college students is mainly closely related to a variety of factors, and it is crucial to conduct in-depth research and provide scientific and effective mental health services to maintain the physical and mental health of college students. Under the framework of new qualitative productivity, data mining technology is utilized to obtain mental health education data, item set is set for its dataset, and Apriori algorithm is utilized to define the support degree, confidence degree, and strong association rules. Using the association rule model, the current situation of mental health education in colleges and universities is explored, and the corresponding optimization path is proposed. Among all the itemsets, {Obsessive Compulsive, Anxiety}→{Study Stress} has the highest confidence level, with a value of 0.9031, and its corresponding support level is 0.1888, which means that obsessive-compulsive disorder and anxiety are the most important reasons leading to students’ study stress in order to cultivate students’ healthy psychology.

The confidential data is mainly managed by creating passwords, tokens, and unique identifiers in an authorized manner. These records must be kept in a safe location away from the reach of unauthorized third parties. Both the client and server sides must be encrypted using the two-fish algorithm, which secures the distinction of private data. By gaining access to the user's information, a data miner may be able to steal it. To avoid such situations, both the data miner and the server must be encrypted. Further, the previous techniques faced several shortcomings in case of higher computational overhead, poor resource utilization, prone to single point failure, lower accuracy, noise, poor security, higher distortion, etc. In this study, both the client and server sides are encrypted using a two-fish algorithm to avoid information loss while transferring data to overcome these problems. The way the state-of-art techniques handled the privacy preservation issue often leads to privacy violations. This paper focuses on mining frequent itemsets present in the medical data by also ensuring privacy. Frequent itemset mining mainly aims to extract highly correlated items from the database and to achieve this novel fruitfly whale optimization algorithm (FWOA) combined with the Apriori algorithm. The Apriori heuristic and bio-inspired algorithms are integrated to solve the frequent itemset problem by reducing the low runtime performance when handling large datasets and also offering high-quality solutions. The adaptive k-anonymity approach is used for preserving data privacy by transforming the original data into an encrypted mode and offering privacy to the top-k frequent itemsets mining. The main advantage of the adaptive k-anonymity approach is that the confidential information disclosed by an individual user cannot be identified from at least k − 1 individuals. We ensure that the proposed methodology can offer data privacy in real time by the experiments conducted in a medical dataset. The experimental results obtained highlight the robustness of this scheme.