Boosting is an ensemble learning method that combines a set of weak learners into a strong learner to minimize training errors. AdaBoost algorithm, as a typical boosting algorithm, transforms weak learners or predictors to strong predictors in order to solve problems of classification. With remarkable usability and effectiveness, AdaBoost algorithm has been widely used in many fields, such as face recognition, speech enhancement, natural language processing, and network intrusion detection. In the large-scale enterprise network environment, more and more companies have begun to build trustworthy networks to effectively defend against hacker attacks. However, since trustworthy networks use trusted flags to verify the legitimacy of network requests, it cannot effectively identify abnormal behaviors in network data packets. This paper applies Adaboost algorithm in trustworthy network for anomaly intrusion detection to improve the defense capability against network attacks. This method uses a simple decision tree as the base weak learner, and uses AdaBoost algorithm to combine multiple weak learners into a strong learner by re-weighting the samples. This paper uses the real data of trustworthy network for experimental verification. The experimental results show that the average precision of network anomaly detection method based on AdaBoost algorithm is more than 0.999, indicating that it has a significant detection effect on abnormal network attacks and normal network access. Therefore, the proposed method can effectively improve the security of trustworthy networks.

Whitelisting is a widely used method in the security field. However, due to the rapid development of the Internet, the traditional whitelisting method cannot promote the security of increasing Internet access. In recent years, with the success of machine learning in different areas, many researchers focus on the security of Internet access through machine learning methods. The most common form of machine learning is supervised learning. Supervised learning requires a large number of labeled samples, but it is difficult to obtain labeled samples in practical applications. This paper introduced an unsupervised deep learning algorithm based on seq2seq, which combined with the recurrent neural network and the autoencoder structure to realize an intelligent boundary security control mechanism. The main methods proposed in this paper are divided into two parts: data processing and modeling. In the phase of data processing, the access text table was coded with dicts, and all sequences were padded to the maximum. In the modeling phase, the network was optimized according to the principle of minimizing the reconstruction error. From the comparative experiments, the proposed method’s AUC on the public data set reached 0.99, and its performance is better than several classical supervised learning algorithms, proving that the proposed method has an efficient defense against abnormal network access.

In the era of big data, protecting the privacy of smart grid data is critical in ensuring the integrity and confidentiality of that data. Utilizing large amounts of energy data to gain insight into electricity consumers’ consumption patterns helps develop power supply strategies. This article presents a Big Data-assisted Joint learning process (BDA-JLP), taking data security issues posed by big data in the electric power industry into consideration for privacy protection using K-anonymity and L-diversity as a foundation. A blockchain with JLP electric utility investigation is being conducted, part of the existing trading model split into phases. To begin, an attribute is chosen to categorize the input database. The comparable class number K and sensitive attribute value category L are limited by the number of original predecessors in the source data table, simplifying the calculation. A mathematical equation is then developed to determine the distance between first cousins multiplied by their combined weight. Linear and clustering with binary K are used to categorize data tables. Cluster and generalize initial data sets, considering how the attribute values’ internal range changes. The asymmetric encryption method uses two distinct keys for encryption and decryption ensuring that the blockchain system is completely secure. Simulated data show that the BDA-JLP mechanism proposed here has a privacy ratio of 98.3 percent, scalability of 97.0%, improved data management and data protection ratio of 98.2 percent, customer satisfaction ratio of 98.4 percent, and a low energy consumption ratio of 23.9% when compared to other methods currently available.