Mobile-Sandbox: combining static and dynamic analysis with machine-learning techniques

2014 ◽  
Vol 14 (2) ◽  
pp. 141-153 ◽  
Author(s):  
Michael Spreitzenbarth ◽  
Thomas Schreck ◽  
Florian Echtler ◽  
Daniel Arp ◽  
Johannes Hoffmann
Keyword(s):  
Machine Learning ◽  
Dynamic Analysis ◽  
Machine Learning Techniques ◽  
Static And Dynamic Analysis ◽  
Learning Techniques

Vulnerability Assessment and Malware Analysis of Android Apps Using Machine Learning

2021 ◽  
pp. 255-277
Author(s):  
Pallavi Khatri ◽  
Animesh Kumar Agrawal ◽  
Aman Sharma ◽  
Navpreet Pannu ◽  
Sumitra Ranjan Sinha
Keyword(s):  
Machine Learning ◽  
Dynamic Analysis ◽  
Mobile Devices ◽  
Personal Information ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Malware Analysis ◽  
Static And Dynamic Analysis ◽  
Android Apps ◽  
Learning Techniques

Mobile devices and their use are rapidly growing to the zenith in the market. Android devices are the most popular and handy when it comes to the mobile devices. With the rapid increase in the use of Android phones, more applications are available for users. Through these alluring multi-functional applications, cyber criminals are stealing personal information and tracking the activities of users. This chapter presents a two-way approach for finding malicious Android packages (APKs) by using different Android applications through static and dynamic analysis. Three cases are considered depending upon the severity level of APK, permission-based protection level, and dynamic analysis of APK for creating the dataset for further analysis. Subsequently, supervised machine learning techniques such as naive Bayes multinomial text, REPtree, voted perceptron, and SGD text are applied to the dataset to classify the selected APKs as malicious, benign, or suspicious.

Two-Stage Ransomware Detection Using Dynamic Analysis and Machine Learning Techniques

2020 ◽  
Vol 112 (4) ◽  
pp. 2597-2609 ◽  
Author(s):  
Jinsoo Hwang ◽  
Jeankyung Kim ◽  
Seunghwan Lee ◽  
Kichang Kim
Keyword(s):  
Machine Learning ◽  
Dynamic Analysis ◽  
Machine Learning Techniques ◽  
Two Stage ◽  
Learning Techniques

Digital Transformation and Cybersecurity Challenges

2021 ◽  
pp. 203-226
Author(s):  
Fatimah Al Obaidan ◽  
Saqib Saeed
Keyword(s):  
Machine Learning ◽  
Communication Systems ◽  
Human Life ◽  
Malware Detection ◽  
Digital Transformation ◽  
Machine Learning Techniques ◽  
Sensitive Information ◽  
Static And Dynamic Analysis ◽  
Learning Techniques

Digital transformation has revolutionized human life but also brought many cybersecurity challenges for users and enterprises. The major threats that affect computers and communication systems by damaging devices and stealing sensitive information are malicious attacks. Traditional anti-virus software fails to detect advanced kind of malware. Current research focuses on developing machine learning techniques for malware detection to respond in a timely manner. Many systems have been evolved and improved to distinguish the malware based on analysis behavior. The analysis behavior is considered a robust technique to detect, analyze, and classify malware, categorized into two models: a static and dynamic analysis. Both types of previous analysis have advantages and limitations. Therefore, the hybrid method combines the strength of static and dynamic analyses. This chapter conducted a systematic literature review (SLR) to summarize and analyze the quality of published studies in malware detection using machine learning techniques and hybrid analysis that range from 2016 to 2021.

Using machine learning techniques to reduce data annotation time

2006 ◽  
Author(s):  
Christopher Schreiner ◽  
Kari Torkkola ◽  
Mike Gardner ◽  
Keshu Zhang
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Data Annotation ◽  
Learning Techniques

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

389-P: Ability for Detecting or Predicting Hypoglycemia with the Aid of Machine Learning Techniques: A Meta-analysis

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 389-P
Author(s):  
SATORU KODAMA ◽  
MAYUKO H. YAMADA ◽  
YUTA YAGUCHI ◽  
MASARU KITAZAWA ◽  
MASANORI KANEKO ◽  
...  
Keyword(s):  
Machine Learning ◽  
Meta Analysis ◽  
Machine Learning Techniques ◽  
Learning Techniques

A Comparative Study of Different Machine Learning Algorithms for Disease Prediction

2017 ◽  
Vol 7 (7) ◽  
pp. 172
Author(s):  
Anantvir Singh Romana
Keyword(s):  
Machine Learning ◽  
Subsequent Treatment ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Disease Prediction ◽  
Classification Problems ◽  
Learning Techniques ◽  
Neural Network Classifiers ◽  
Diagnostic Detection

Accurate diagnostic detection of the disease in a patient is critical and may alter the subsequent treatment and increase the chances of survival rate. Machine learning techniques have been instrumental in disease detection and are currently being used in various classification problems due to their accurate prediction performance. Various techniques may provide different desired accuracies and it is therefore imperative to use the most suitable method which provides the best desired results. This research seeks to provide comparative analysis of Support Vector Machine, Naïve bayes, J48 Decision Tree and neural network classifiers breast cancer and diabetes datsets.

A Brief Survey on Text Classification Using Various Machine Learning Techniques

2018 ◽  
Vol 8 (1) ◽  
pp. 14
Author(s):  
Padmavathi .S ◽  
M. Chidambaram
Keyword(s):  
Machine Learning ◽  
Text Classification ◽  
Fixed Number ◽  
Machine Learning Techniques ◽  
Online Information ◽  
Rule Based ◽  
Learning Techniques ◽  
Machine Learning Approach ◽  
Rule Based Approach

Text classification has grown into more significant in managing and organizing the text data due to tremendous growth of online information. It does classification of documents in to fixed number of predefined categories. Rule based approach and Machine learning approach are the two ways of text classification. In rule based approach, classification of documents is done based on manually defined rules. In Machine learning based approach, classification rules or classifier are defined automatically using example documents. It has higher recall and quick process. This paper shows an investigation on text classification utilizing different machine learning techniques.

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