Analisis Data Log IDS Snort dengan Algoritma Clustering Fuzzy C-Means

Snort is one of open source IDS to detect intrusion or potentially malicious activity on network traffic. Snort will give alert for every detected intrusion and write the alerts in log. Log data in IDS Snort will help network administrator to analyze the vulnerability of network security system. Clustering algorithm such as FCM can be used to analyze the log data of IDS Snort. Implementation of the algorithm is based on Python 3 and aims to cluster alerts in log data into 4 risk categories, such as low, medium, high, and critical. The outcome of this analysis is to show cluster results of FCM and to visualize the types of attacks that IDS Snort has successfully detected. Evaluation process is done by using Modified Partition Coefficient (MPC) to determine the validity of FCM.

Data-Driven Treatment Pathways Mining for Early Breast Cancer Using cSPADE Algorithm and System Clustering

Background: Due to the multidimensional, multilayered, and chronological order of the cancer data in this study, it was challenging for us to extract treatment paths. Therefore, it was necessary to design a new data mining scheme to effectively extract the treatment path of breast cancer. To determine whether the cSPADE algorithm and system clustering proposed in this study can effectively identify the treatment pathways for early breast cancer. Methods: We applied data mining technology to the electronic medical records of 6891 early breast cancer patients to mine treatment pathways. We provided a method of extracting data from EMR and performed three-stage mining: determining the treatment stage through the cSPADE algorithm → system clustering for treatment plan extraction → cSPADE mining sequence pattern for treatment. The Kolmogorov-Smirnov test and correlation analysis were used to cross-validate the sequence rules of early breast cancer treatment pathways.Results: We unearthed 55 sequence rules for early breast cancer treatment, 3 preoperative neoadjuvant chemotherapy regimens, 3 postoperative chemotherapy regimens, and 2 chemotherapy regimens for patients without surgery. Through 5-fold cross-validation, Pearson and Spearman correlation tests were performed. At the significance level of P <0.05, all correlation coefficients of support, confidence and lift were greater than 0.89. Using the Kolmogorov-Smirnov test, we found no significant differences between the sequence distributions.Conclusions: The cSPADE algorithm combined with system clustering can achieve hierarchical and vertical mining of breast cancer treatment models. By uncovering the treatment pathways of early breast cancer patients by this method, the real-world breast cancer treatment behavior model can be evaluated, and it can provide a reference for the redesign and optimization of the treatment pathways.

K medoid Clustering in Wireless Sensor Network using a Constant Threshold

wireless sensor network (WSN) is system which comprises of countless little sensors called node which has lowcontrol handset that utilized device for get-together information in an assortment of situations dependent on system arrangement. The correspondence or message transitory procedure intended to preserve restricted energy assets of sensors for information handling is fundamental undertaking of WSN. To achieve this assignment, we are presenting another idea for sparing energy& upgrading the system lifetime of the system. Clustering is inventive in a few territories which incorporates abstain from execution clustering in every round, presenting secure limit, utilizing various calculations to do Clustering & using multijump directing by thinking about reasonable center node to transfer information as of every cluster to base station (BS). Residual energy, no. of nodes & separation of every node are measured measures to choose cluster head (CH) utilizing consistent edge has been contrasted with different calculations as far as parameters, for example, organize era, dead nodes in each round, main node kick bucket, & half beyond words last amazing.

Pengembangan Sistem Pengelompokan Belajar Mahasiswa pada Matakuliah Struktur Data dengan Metode K-Means

The development of science and technology requires tertiary institutions as formal education institutions, to be able to produce qualified and competent graduates. Learning about higher education needs to be more innovative and creative in producing learning and responsive to labor needs. "Successful constraints of lecturers in teaching Data Structure subjects do not have learning models that approach students with abstract theories that are difficult for students to understand, to overcome these conflicts. learn with the Application of Cooperative Oriented Problems. However, in terms of grouping learning with the application of this method, it still takes a relatively long time to do individual testing several times to find a suitable group, so that the learning grouping is less than optimal. The method used in this study is K-Means Clustering, from the software that was built to help instructors in the subject of data structure in the process of grouping tutoring students. Grouping methods can be implemented to build valid student guidance grouping software. Keywords: Learning Grouping System, Clustering, K-Means

An improved clustering method for uncertain system

Uncertain system clustering is an important issue which is the base for mining real-world data in many fields. In this paper, we investigate the clustering problem of uncertain system, and propose an improved clustering algorithm. Here, our improved algorithm considers not only distance but also spatial direction in vector space of data points. The aim is to improve the accuracy of uncertain system clustering, especially when there exists overlap among border region of groups in vector space. Experiment results show that for uncertain physics systems, the improved algorithm can well increase the accuracy of uncertain system clustering compared with the traditional clustering method which is based on distance similarity. Maximum value of accuracy and [Formula: see text]-measure are increased by 21.1% and 13.3%, respectively. Moreover, the proposed algorithm has high robustness for noise.