scholarly journals Malware Detection Using Machine Learning Algorithms Based on Hardware Performance Counters: Analysis and Simulation

2021 ◽  
Vol 1962 (1) ◽  
pp. 012010
Author(s):  
Omar Bawazeer ◽  
Tarek Helmy ◽  
Suheer Al-hadhrami
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Malware Detection ◽  
Machine Learning Algorithms ◽  
Performance Counters ◽  
Hardware Performance Counters ◽  
Hardware Performance

scholarly journals Techniques for Detecting Malware Traffic: A Comprehensive Approach to Feature Selection and Classification

2021 ◽  
Vol 9 (12) ◽  
pp. 1-10
Author(s):  
Harsha A K
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Random Forest ◽  
Learning Algorithms ◽  
Malware Detection ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Support Vector ◽  
Steady Increase ◽  
Extreme Gradient Boosting

Abstract: Since the advent of encryption, there has been a steady increase in malware being transmitted over encrypted networks. Traditional approaches to detect malware like packet content analysis are inefficient in dealing with encrypted data. In the absence of actual packet contents, we can make use of other features like packet size, arrival time, source and destination addresses and other such metadata to detect malware. Such information can be used to train machine learning classifiers in order to classify malicious and benign packets. In this paper, we offer an efficient malware detection approach using classification algorithms in machine learning such as support vector machine, random forest and extreme gradient boosting. We employ an extensive feature selection process to reduce the dimensionality of the chosen dataset. The dataset is then split into training and testing sets. Machine learning algorithms are trained using the training set. These models are then evaluated against the testing set in order to assess their respective performances. We further attempt to tune the hyper parameters of the algorithms, in order to achieve better results. Random forest and extreme gradient boosting algorithms performed exceptionally well in our experiments, resulting in area under the curve values of 0.9928 and 0.9998 respectively. Our work demonstrates that malware traffic can be effectively classified using conventional machine learning algorithms and also shows the importance of dimensionality reduction in such classification problems. Keywords: Malware Detection, Extreme Gradient Boosting, Random Forest, Feature Selection.

Automatic Malware Detection with Machine Learning Algorithms in a Smart Office Environment

2018 ◽  
Vol 7 (4) ◽  
pp. 313-320
Author(s):  
Minsoo Yeo ◽  
Ilsub Bang ◽  
Donghyun Kim ◽  
Abbas Ahmad ◽  
Hamza Baqa ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Malware Detection ◽  
Machine Learning Algorithms ◽  
Office Environment

scholarly journals Robust Malware Detection using Residual Attention Network

2020 ◽  
Author(s):  
Shamika Ganesan ◽  
vinayakumar R ◽  
Moez Krichen ◽  
Sowmya V ◽  
Roobaea Alroobaea ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Learning Algorithms ◽  
Malware Detection ◽  
Machine Learning Algorithms ◽  
Detection Methods ◽  
Attention Network ◽  
Conventional Machine

In this paper, we explore the use of an attention based mechanism known as Residual Attention for malware detection and compare this with existing CNN based methods and conventional Machine Learning algorithms with the help of GIST features. The proposed method outperformed traditional malware detection methods which use Machine Learning and CNN based Deep Learning algorithms, by demonstrating an accuracy of 99.25%.

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