Abstract
Abstract In order to correct the deficiencies of intrusion detection technology, the entire computer and network security system are needed to be more perfect. This work proposes an improved k-means algorithm and an improved Apriori algorithm applied in data mining technology to detect network intrusion and security maintenance. The classical KDDCUP99 dataset has been utilized in this work for performing the experimentation with the improved algorithms. The algorithm’s detection rate and false alarm rate are compared with the experimental data before the improvement. The outcomes of proposed algorithms are analyzed in terms of various simulation parameters like average time, false alarm rate, absolute error as well as accuracy value. The results show that the improved algorithm advances the detection efficiency and accuracy using the designed detection model. The improved and tested detection model is then applied to a new intrusion detection system. After intrusion detection experiments, the experimental results show that the proposed system improves detection accuracy and reduces the false alarm rate. A significant improvement of 90.57% can be seen in detecting new attack type intrusion detection using the proposed algorithm.
Highlights
With the development of the Internet, the Internet has become an important part of human work and life
After the realization of globalization and informatization, many enterprises, government agencies, and individuals carry out various businesses and operations on the open Internet, such as Financial companies carry out online banking, etc
Data mining is basically about discovering the hidden and unpredictable relationships among the data by the detection of data patterns, knowledge extraction, and revealing the unknown information. The insight of these data mining strategies can be used to evaluate the probability of future events which can be used in various fields of marketing, scientific discovery, fraud and intrusion detection, etc
Summary
With the development of the Internet, the Internet has become an important part of human work and life. Data mining technology in network intrusion and security maintenance 665 same time it brings hidden dangers to the security of the Internet itself [2]. Intrusion Detection System (IDS) is one of the technologies to improve network security. Data mining is basically about discovering the hidden and unpredictable relationships among the data by the detection of data patterns, knowledge extraction, and revealing the unknown information The insight of these data mining strategies can be used to evaluate the probability of future events which can be used in various fields of marketing, scientific discovery, fraud and intrusion detection, etc. This work proposes an improved k-means algorithm and an improved Apriori algorithm which are applied in the data mining technology for the detection of network intrusion and security maintenance.
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