scholarly journals Network Software Vulnerability Identifier using J48 decision tree algorithm

Author(s):  
Shankar Murthy J

Abstract: Software vulnerabilities are the primary causes of different security issues in the modern era. When vulnerability is exploited by malicious assaults, it substantially jeopardizes the system's security and may potentially result in catastrophic losses. As a result, automatic classification methods are useful for successfully managing software vulnerabilities, improving system security performance, and lowering the chance of the system being attacked and destroyed. In the software industry and in the field of cyber security, the ever-increasing number of publicly reported security flaws has become a major source of concern. Because software security flaws play such a significant part in cyber security attacks, relevant security experts are conducting an increasing number of vulnerability classification studies, this project can predict the software vulnerability means the software's in the device are authorized or not and who scan the system multiple times, to identify the vulnerability j48 decision tree algorithm was used. Keywords: Malicious assaults, catastrophic losses, Security flaws, Cyber security, Vulnerability Classifications.

2017 ◽  
Vol 7 (1.3) ◽  
pp. 28
Author(s):  
S. Gomathi ◽  
V. Narayani

The objective of the paper is to propose an enhanced algorithm for the prediction of chronic, autoimmune disease called Systemic Lupus Erythematosus (SLE). The Hybrid K-means J48 Decision Tree algorithm (HKMJDT) has been proposed for the effective and early prediction of the SLE. The reason for combining both the clustering and classification algorithms is to obtain the best accuracy and to predict the disease in the early stage. The performance of algorithms such as Naïve Bayes, decision tree, random forest, J48 and Hoeffding tree has been combined with K-means clustering algorithm and compared in an effort to find the best algorithm for diagnosing SLE disease. The results of the statistical evaluation with the comparative study show that the effectiveness of different classification techniques depends on the nature and intricacy of the dataset used. K-means combined with J48 algorithm shows the best accuracy rate of 82.14% on the pre-processed data. The work-flow has been proposed to show the execution of the algorithm.


2019 ◽  
Vol 1 (3) ◽  
pp. 244-253
Author(s):  
Nur Mishbah Hayat ◽  
Agung Budi Prasetijo ◽  
Risma Septiana

The problem that is often faced by investors in selling / buying stocks is the difficulty in analyzing a dataset of stock prices in large quantities.This analysis aims to predict the rise or fall of stock prices based on data obtained. To assist investors in determining buying / selling decisions on stock analysis based on technical and equipped with classification techniques in data mining. This study analyzes the performance of the J48 Decision Tree algorithm in the Waikato Environmental Software for Knowledge Analysis (WEKA) version 3.8.2 for PT. Harum Energi Tbk. (HRUM). The results showed in the testing data, the percentage of testing on data without normalization was higher by 87.3 (non-aggressive) and 88.8 (aggressive) compared to normalized data 84.2 (non-aggressive) and 85 (aggressive ). The biggest stock profit generated is in non-aggressive type data without normalized by 48.75 or Rp. 48,750.00.


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