Malware Detection Using Machine Learning Models

2020 ◽  
pp. 238-246
Author(s):  
Glaucio H. S. Carvalho ◽  
Isaac Woungang ◽  
Alagan Anpalagan ◽  
Issa Traore ◽  
Leonard Barolli
Keyword(s):  
Machine Learning ◽  
Malware Detection ◽  
Learning Models ◽  
Machine Learning Models

Analysis of machine learning models for malware detection

2020 ◽  
Vol 23 (2) ◽  
pp. 395-407 ◽  
Author(s):  
Rahul ◽  
Priyansh Kedia ◽  
Subrat Sarangi ◽  
Monika
Keyword(s):  
Machine Learning ◽  
Malware Detection ◽  
Learning Models ◽  
Machine Learning Models

A Comparison of State-of-the-Art Machine Learning Models for OpCode-Based IoT Malware Detection

2020 ◽  
pp. 109-120
Author(s):  
William Peters ◽  
Ali Dehghantanha ◽  
Reza M. Parizi ◽  
Gautam Srivastava
Keyword(s):  
Machine Learning ◽  
State Of The Art ◽  
Malware Detection ◽  
Learning Models ◽  
Machine Learning Models

scholarly journals Malware Detection on Byte Streams of PDF Files Using Convolutional Neural Networks

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Young-Seob Jeong ◽  
Jiyoung Woo ◽  
Ah Reum Kang
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Input Data ◽  
Malware Detection ◽  
Learning Models ◽  
High Level ◽  
The Given ◽  
Machine Learning Models

With increasing amount of data, the threat of malware keeps growing recently. The malicious actions embedded in nonexecutable documents especially (e.g., PDF files) can be more dangerous, because it is difficult to detect and most users are not aware of such type of malicious attacks. In this paper, we design a convolutional neural network to tackle the malware detection on the PDF files. We collect malicious and benign PDF files and manually label the byte sequences within the files. We intensively examine the structure of the input data and illustrate how we design the proposed network based on the characteristics of data. The proposed network is designed to interpret high-level patterns among collectable spatial clues, thereby predicting whether the given byte sequence has malicious actions or not. By experimental results, we demonstrate that the proposed network outperform several representative machine-learning models as well as other networks with different settings.

Adversarial Attacks and Defenses in Malware Detection Classifiers

2019 ◽  
pp. 127-150 ◽  
Author(s):  
Teenu S. John ◽  
Tony Thomas
Keyword(s):  
Machine Learning ◽  
Detection System ◽  
Malware Detection ◽  
Threat Detection ◽  
Learning Models ◽  
Research Directions ◽  
Detection Capabilities ◽  
Defensive Mechanisms ◽  
Attacks And Defenses ◽  
Machine Learning Models

Machine learning has found its immense application in various cybersecurity domains owing to its automated threat prediction and detection capabilities. Despite its advantages, attackers can utilize the vulnerabilities of machine learning models for degrading its performance. These attacks called adversarial attacks can perturb the features of the data to induce misclassification. Adversarial attacks are highly destructive in the case of malware detection classifiers, causing a harmful virus or trojan to evade the threat detection system. The feature perturbations carried out by an adversary against malware detection classifiers are different from the conventional attack strategies employed by an adversary against computer vision tasks. This chapter discusses various adversarial attacks launched against malware detection classifiers and the existing defensive mechanisms. The authors also discuss the challenges and the research directions that need to be addressed to develop effective defensive mechanisms against these attacks.

Improving XGBoost with Imagination Sampling

2020 ◽  
Vol 2 (1) ◽  
pp. 3-6
Author(s):  
Eric Holloway
Keyword(s):  
Machine Learning ◽  
General System ◽  
Learning Models ◽  
Starting Point ◽  
Machine Learning Models

Imagination Sampling is the usage of a person as an oracle for generating or improving machine learning models. Previous work demonstrated a general system for using Imagination Sampling for obtaining multibox models. Here, the possibility of importing such models as the starting point for further automatic enhancement is explored.

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