scholarly journals Development and Experimental Evaluation of Machine-Learning Techniques for an Intelligent Hairy Scalp Detection System

2018 ◽  
Vol 8 (6) ◽  
pp. 853 ◽  
Author(s):  
Wei-Chien Wang ◽  
Liang-Bi Chen ◽  
Wan-Jung Chang
Keyword(s):  
Machine Learning ◽  
Experimental Evaluation ◽  
Detection System ◽  
Machine Learning Techniques ◽  
Learning Techniques

Anomaly Activities Detection System in Critical Water SCADA Infrastructure Using Machine Learning Techniques

2019 ◽  
Vol 28 (1) ◽  
pp. 343-384 ◽  
Author(s):  
Gamal Eldin I. Selim ◽  
EZZ El-Din Hemdan ◽  
Ahmed M. Shehata ◽  
Nawal A. El-Fishawy
Keyword(s):  
Machine Learning ◽  
Detection System ◽  
Machine Learning Techniques ◽  
Learning Techniques

scholarly journals Decision Tree: A Machine Learning for Intrusion Detection

2019 ◽  
Vol 8 (6S4) ◽  
pp. 1126-1130
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Detection System ◽  
Research Work ◽  
Machine Learning Techniques ◽  
Data Sets ◽  
Legitimate User ◽  
Learning Techniques ◽  
Three Stages

The Intrusion is a major threat to unauthorized data or legal network using the legitimate user identity or any of the back doors and vulnerabilities in the network. IDS mechanisms are developed to detect the intrusions at various levels. The objective of the research work is to improve the Intrusion Detection System performance by applying machine learning techniques based on decision trees for detection and classification of attacks. The methodology adapted will process the datasets in three stages. The experimentation is conducted on KDDCUP99 data sets based on number of features. The Bayesian three modes are analyzed for different sized data sets based upon total number of attacks. The time consumed by the classifier to build the model is analyzed and the accuracy is done.

scholarly journals Abnormal Human Activity Detection using Unsupervised Machine Learning Techniques

2020 ◽  
Vol 8 (6) ◽  
pp. 3949-3953
Keyword(s):  
Machine Learning ◽  
Human Activity ◽  
Detection System ◽  
Machine Learning Techniques ◽  
Abnormal Behavior ◽  
Activity Detection ◽  
Research Attention ◽  
Learning Techniques ◽  
Abnormal Behavior Detection ◽  
Human Activity Detection

Nowadays there is a significant study effort due to the popularity of CCTV to enhance analysis methods for surveillance videos and video-based images in conjunction with machine learning techniques for the purpose of independent assessment of such information sources. Although recognition of human intervention in computer vision is extremely attained subject, abnormal behavior detection is lately attracting more research attention. In this paper, we are interested in the studying the two main steps that compose abnormal human activity detection system which are the behavior representation and modelling. And we use different techniques, related to feature extraction and description for behavior representation as well as unsupervised classification methods for behavior modelling. In addition, available datasets and metrics for performance evaluation will be presented. Finally, this paper will be aimed to detect abnormal behaved object in crowd, such as fast motion in a crowd of walking people

A robust intrusion detection system using machine learning techniques for MANET

2020 ◽  
Vol 24 (3) ◽  
pp. 253-260
Author(s):  
N. Ravi ◽  
G. Ramachandran
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Mobile Computing ◽  
Mobile Networks ◽  
Intrusion Detection System ◽  
Detection System ◽  
Mobile Network ◽  
Machine Learning Techniques ◽  
Test Bed ◽  
Learning Techniques

Recent advancement in technologies such as Cloud, Internet of Things etc., leads to the increase usage of mobile computing. Present day mobile computing are too sophisticated and advancement are reaching great heights. Moreover, the present day mobile network suffers due to external and internal intrusions within and outside networks. The existing security systems to protect the mobile networks are incapable to detect the recent attacks. Further, the existing security system completely depends on the traditional signature and rule based approaches. Recent attacks have the property of not fluctuating its behaviour during attack. Hence, a robust Intrusion Detection System (IDS) is desirable. In order to address the above mentioned issue, this paper proposed a robust IDS using Machine Learning Techniques (MLT). The key of using MLT is to utilize the power of ensembles. The ensembles of classifier used in this paper are Random Forest (RF), KNN, Naïve Bayes (NB), etc. The proposed IDS is experimentally tested and validated using a secure test bed. The experimental results also confirms that the proposed IDS is robust enough to withstand and detect any form of intrusions and it is also noted that the proposed IDS outperforms the state of the art IDS with more than 95% accuracy.

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