An Efficient DoS Attacks Detection Method Based on Data Mining Scheme

2011 ◽  
Vol 267 ◽  
pp. 302-307
Author(s):  
Xiang Chen
Keyword(s):  
Anomaly Detection ◽  
Detection Method ◽  
Nearest Neighbors ◽  
Selection Method ◽  
Instance Selection ◽  
Dos Attacks ◽  
K Nearest Neighbors ◽  
Detection Mechanism ◽  
Mining Scheme ◽  
Network Anomaly Detection

To defend against DoS attacks and ensure QoS of web server, we first propose an efficient network anomaly detection method based on TCM-KNN (Transductive Confidence Machines for K-Nearest Neighbors) algorithm. Secondly, we integrate a lot of objective and efficient DoS impact metrics from the perceptions of the end users into TCM-KNN algorithm to build a robust anomaly detection mechanism. Finally, Genetic Algorithm (GA) based instance selection method is introduced to boost the real-time detection performance of our method.

scholarly journals An Entropy-Based Network Anomaly Detection Method

Entropy ◽  
2015 ◽  
Vol 17 (4) ◽  
pp. 2367-2408 ◽  
Author(s):  
Przemysław Bereziński ◽  
Bartosz Jasiul ◽  
Marcin Szpyrka
Keyword(s):  
Anomaly Detection ◽  
Detection Method ◽  
Network Anomaly Detection

Network anomaly detection based on signal processing techniques

2013 ◽  
Vol 18 (1) ◽  
pp. 15-21
Author(s):  
Tomasz Andrysiak ◽  
Łukasz Saganowski ◽  
Mirosław Maszewski
Keyword(s):  
Signal Processing ◽  
Anomaly Detection ◽  
Network Traffic ◽  
Detection Method ◽  
Matching Pursuit ◽  
Matching Pursuit Decomposition ◽  
Network Anomaly Detection ◽  
Processing Techniques ◽  
Signal Processing Techniques

Abstract The article depicts possibility of using Matching Pursuit decomposition in order to recognize unspecified hazards in network traffic. Furthermore, the work aims to present feasible enhancements to the anomaly detection method, as well as their efficiency on the basis of a wide collection of pattern test traces.

scholarly journals Towards Optimization of Malware Detection using Chi-square Feature Selection on Ensemble Classifiers

2021 ◽  
Vol 10 (4) ◽  
pp. 254-262
Author(s):  
*Fadare Oluwaseun Gbenga ◽  
Adetunmbi Adebayo Olusola ◽  
(Mrs) Oyinloye Oghenerukevwe Eloho ◽  
Mogaji Stephen Alaba
Keyword(s):  
Feature Selection ◽  
Malware Detection ◽  
Feature Selection Method ◽  
Ensemble Methods ◽  
Nearest Neighbors ◽  
Selection Method ◽  
Gradient Boosting ◽  
K Nearest Neighbors ◽  
Chi Square ◽  
Extreme Gradient Boosting

The multiplication of malware variations is probably the greatest problem in PC security and the protection of information in form of source code against unauthorized access is a central issue in computer security. In recent times, machine learning has been extensively researched for malware detection and ensemble technique has been established to be highly effective in terms of detection accuracy. This paper proposes a framework that combines combining the exploit of both Chi-square as the feature selection method and eight ensemble learning classifiers on five base learners- K-Nearest Neighbors, Naïve Bayes, Support Vector Machine, Decision Trees, and Logistic Regression. K-Nearest Neighbors returns the highest accuracy of 95.37%, 87.89% on chi-square, and without feature selection respectively. Extreme Gradient Boosting Classifier ensemble accuracy is the highest with 97.407%, 91.72% with Chi-square as feature selection, and ensemble methods without feature selection respectively. Extreme Gradient Boosting Classifier and Random Forest are leading in the seven evaluative measures of chi-square as a feature selection method and ensemble methods without feature selection respectively. The study results show that the tree-based ensemble model is compelling for malware classification.

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