Automated multi-level malware detection system based on reconstructed semantic view of executables using machine learning techniques at VMM

2018 ◽  
Vol 79 ◽  
pp. 431-446 ◽  
Author(s):  
Ajay Kumara M.A. ◽  
Jaidhar C.D.
Keyword(s):  
Machine Learning ◽  
Detection System ◽  
Malware Detection ◽  
Machine Learning Techniques ◽  
Semantic View ◽  
Learning Techniques ◽  
Multi Level

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

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
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