Network Intrusion Detection Systems Analysis using Frequent Item Set Mining Algorithm FP-Max and Apriori

Hidayanto, Bekti Cahyo; Muhammad, Rowi Fajar; Kusumawardani, Lita Heni; Syafaat, Achmad

doi:10.1016/j.procs.2017.12.214

Cited by 16 publications

(8 citation statements)

References 2 publications

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“…The Apriori algorithm is the most classic algorithm for mining frequent item sets, which can extract association rules from large data sets (Zhang, 2016;Hidayanto et al, 2017). Algorithm steps (Han and Kamber, 2001;Yu, 2004;Li et al, 2020) are shown in Figure 2 below.…”

Section: Establishment Of Weight Model Based On Apriori Algorithmmentioning

confidence: 99%

Research on weighting method of geological hazard susceptibility evaluation index based on apriori Algorithm

Chen

Wei

et al. 2023

Front. Earth Sci.

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Given the inconsistency between the information value and the weight value in the weighted information value model, a weight model based on the Apriori algorithm is established in this paper to analyze the correlation between the second-level intervals of disaster factors and the susceptibility of geological disasters. The objective weight of the second-level intervals of each index factor is calculated through the mining of association rules by the Apriori algorithm. The subjective uncertainty of the existing second-level factor weighting method is eliminated. Taking the geological disaster data of Xiangtan urban area as an example, 10 evaluation indexes were selected to establish the entropy weight method-information value (EWM-IV) model and the entropy weight method-Apriori algorithm-information value (EWM-Apriori-IV) model to evaluate the geological disaster susceptibility, and the disaster area ratio and the receiver operating characteristic curve (ROC) verification method were used to test and analyze the evaluation results. The results showed that compared with the EWM-IV model, the EWM-Apriori-IV model is used to evaluate the disaster area ratio of high-prone area increased by 58.3%, and the disaster area ratio of low-prone area decreased by 43.1%, the area under the curve (AUC) increased by 7.4%, and the evaluation accuracy was relatively improved compared with the former. This paper proves the rationality and practicability of the weighting method of the geological hazard susceptibility evaluation index based on the Apriori algorithm.

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Section: Establishment Of Weight Model Based On Apriori Algorithmmentioning

confidence: 99%

Research on weighting method of geological hazard susceptibility evaluation index based on apriori Algorithm

Chen

Wei

et al. 2023

Front. Earth Sci.

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show abstract

“…In addition, Equation (6) shows an equation for calculating the Fmeasure using accuracy and recall. The performance evaluation uses the proposed miningbased mutual information (MbMI), existing mining-based word frequency (MbWF) [37,38], word concurrence frequency (WCoF) [39,40] in the document to find the relationship between words. It performs performance evaluation while repeatedly changing minimum support.…”

Section: B Performance Evaluationmentioning

confidence: 99%

Multi-Level Health Knowledge Mining Process in P2P Edge Network

Baek

Chung

2021

IEEE Access

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Chronic diseases are increasing due to westernized eating habits and everyday life changes, and healthcare and disease prevention should be managed based on constant interest. Users, who are not health professionals, have difficulty in obtaining accurate information related to healthcare due to noise problems such as subjective opinions, distorted information, and exaggerated information. There is a need for a method that enables users to obtain meaningful information for healthcare and disease prevention in real-time among the vast amounts of data collected through search. In this study, we propose a multi-level health knowledge mining process in a P2P edge network. The proposed method suggests a P2P edge network to solve the overload problem of P2P networking, the noise problem, and the security problem of cloud computing and mines the health knowledge through the mutual information according to the association rules. In addition, the results of health knowledge mining are visualized to propose a method by which users can easily receive relevant health information. As a result of the performance evaluation, the F-measure using recall and precision is 83%, 79%, 75%, 74%, and 73% of the support ratings of 10%, 20%, 30%, 40%, and 50% Was derived. Accordingly, it is possible to process and analyze healthcare-related information in realtime through a multi-level based health knowledge tree based on the association of data collected by P2P edge computing. In addition, by visualizing meaningful information to the user through the embedding network structure, it provides personalized information for intuitive understanding.

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“…Since different subsystem implements variable functions, they have unique rules of their own. The computational load is closely related to the size of the database [42]. Thus, each subsystem can manipulate its own database and mine association rules individually.…”

Section: The Detection Model Based On Device Statesmentioning

confidence: 99%

A Novel Intrusion Detection Model Using a Fusion of Network and Device States for Communication-Based Train Control Systems

Song

Zhu

2020

Electronics

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Security is crucial in cyber-physical systems (CPS). As a typical CPS, the communication-based train control (CBTC) system is facing increasingly serious cyber-attacks. Intrusion detection systems (IDSs) are vital to protect the system against cyber-attacks. The traditional IDS cannot distinguish between cyber-attacks and system faults. Furthermore, the design of the traditional IDS does not take the principles of CBTC systems into consideration. When deployed, it cannot effectively detect cyber-attacks against CBTC systems. In this paper, we propose a novel intrusion detection method that considers both the status of the networks and those of the equipment to identify if the abnormality is caused by cyber-attacks or by system faults. The proposed method is verified on a hardware-in-the-loop simulation platform of CBTC systems. Simulation results indicate that the proposed method has achieved 97.64% true positive rate, which can significantly improve the security protection level of CBTC systems.

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Network Intrusion Detection Systems Analysis using Frequent Item Set Mining Algorithm FP-Max and Apriori

Cited by 16 publications

References 2 publications

Research on weighting method of geological hazard susceptibility evaluation index based on apriori Algorithm

Research on weighting method of geological hazard susceptibility evaluation index based on apriori Algorithm

Multi-Level Health Knowledge Mining Process in P2P Edge Network

A Novel Intrusion Detection Model Using a Fusion of Network and Device States for Communication-Based Train Control Systems

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