Learning Representations Using RNN Encoder-Decoder for Edge Security Control

Abstract

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.

Recommendations for Responding to System Security Incidents Using Knowledge Graph Embedding

“…Knowledge-based security technologies formally define entities, such as attack types, vulnerabilities, and attackers, and their relationships. Consequently, the system can gain a deeper understanding of security events and quickly identify the overall scenario or pattern of an attack by linking various elements of threat information [4]. By understanding the knowledge of specific security threats and the attack situation, accurate and effective response strategies can be established.…”

Retracted: Learning Representations Using RNN Encoder‐Decoder for Edge Security Control

“…Tis article has been retracted by Hindawi, as publisher, following an investigation undertaken by the publisher [1]. Tis investigation has uncovered evidence of systematic manipulation of the publication and peer-review process.…”