Application of Data Mining Technology in Software Intrusion Detection and Information Processing

Zhao, Xuemin

doi:10.1155/2022/3829160

Cited by 5 publications

(3 citation statements)

References 22 publications

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“…Utilizing machine learning, exploitation information theory, mathematical models are built that can be used for inference. [2] Machine learning techniques are suitable in situations where there is no prior knowledge about data patterns, that's why sometimes these methods are also called bottom-up. The important advantage of this method is that there is usually no need for expert people to determine the desired requirements in order to detect intrusion, for this reason they act very quickly and are affordable.…”

Section: Introductionmentioning

confidence: 99%

Intrusion Detection System into computer networks by K-means and XGBoost clustering algorithm

Razavi,

khadem

2023

Preprint

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These days, identifying malicious networks has been a subject of study for decades, and since the volume of network traffic is increasing day by day, there is a need for a successful intrusion-detection system that can make the identification process easier during attacks. It is felt to take decisions more accurately and in real time and faster processing. The purpose of this research is to detect intrusion into computer networks by combining K-means and XGboost clustering algorithms. The proposed method is performed in two stages. In the first stage, the pre-processing is done by normalizing and digitizing the data set, as well as removing outliers based on two PCA methods and reducing the dimensions of the feature, then using the learner. We use the k-means algorithm to find the optimal number of clusters, and finally we use the Elbow method to find the optimum number of clusters. The second stage consists of classifying malicious and normal network traffic from each other by combining K-means and XG-Boost algorithms on computing platforms. The experiments in this article were done using the NSLKDD data set and its implementation in the knime emulator platform the final evaluation results show the superiority of the error detection rate, and the accuracy and correctness of the proposed algorithm compared to other similar methods.

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Section: Introductionmentioning

confidence: 99%

Intrusion Detection System into computer networks by K-means and XGBoost clustering algorithm

Razavi,

khadem

2023

Preprint

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“…1. The mechanism of data mining technology in intrusion detection [7] The following are some uses of data mining that may be used for the identification of intrusions: -The purpose of intrusion detection is to find holes or lapses in the security of information systems. A passive approach to security, intrusion detection involves the monitoring of information systems and the generation of alerts in the event that a security breach is discovered.…”

Section: Introductionmentioning

confidence: 99%

Introduction to The Data Mining Techniques in Cybersecurity

2022

MJBD

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As a result of the evolution of the Internet and the massive amount of data that is transmitted every second, as well as the methods for protecting and preserving it and distinguishing those who are authorized to view it, the role of cyber security has evolved to provide the best protection for information over the network. In this paper, the researcher discusses the role of data mining methods in cyber security. Data mining has several uses in security, including national security (for example, surveillance) and cyber security (e.g., virus detection). Attacks against buildings and the destruction of key infrastructure, such as power grids and telecommunications networks, are examples of national security concerns. Cybersecurity is concerned with safeguarding computer and network systems from harmful malware such as Trojan horses and viruses. In addition, data mining is being used to deliver solutions such as intrusion detection and auditing.

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“…This article has been retracted by Hindawi following an investigation undertaken by the publisher [1]. This investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process:…”

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confidence: 99%

Retracted: Application of Data Mining Technology in Software Intrusion Detection and Information Processing

Computing¹

2023

Wireless Communications and Mobile Computing

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Application of Data Mining Technology in Software Intrusion Detection and Information Processing

Cited by 5 publications

References 22 publications

Intrusion Detection System into computer networks by K-means and XGBoost clustering algorithm

Intrusion Detection System into computer networks by K-means and XGBoost clustering algorithm

Introduction to The Data Mining Techniques in Cybersecurity

Retracted: Application of Data Mining Technology in Software Intrusion Detection and Information Processing

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