scholarly journals Malware: The Never-Ending Arm Race

2021 ◽  
pp. 1-25
Author(s):  
Hector David Menendez
Keyword(s):  
Machine Learning ◽  
Dynamic Analysis ◽  
Detection System ◽  
Vice President ◽  
Historical Background ◽  
Malicious Software ◽  
Static And Dynamic Analysis ◽  
Analysis Techniques ◽  
Single Strategy ◽  
New Strategies

"Antivirus is death"' and probably every detection system that focuses on a single strategy for indicators of compromise. This famous quote that Brian Dye --Symantec's senior vice president-- stated in 2014 is the best representation of the current situation with malware detection and mitigation. Concealment strategies evolved significantly during the last years, not just like the classical ones based on polimorphic and metamorphic methodologies, which killed the signature-based detection that antiviruses use, but also the capabilities to fileless malware, i.e. malware only resident in volatile memory that makes every disk analysis senseless. This review provides a historical background of different concealment strategies introduced to protect malicious --and not necessarily malicious-- software from different detection or analysis techniques. It will cover binary, static and dynamic analysis, and also new strategies based on machine learning from both perspectives, the attackers and the defenders.

scholarly journals Comparison of static and dynamic analysis techniques for longitudinal analysis of amyloid PET

2020 ◽  
Vol 16 (S5) ◽  
Author(s):  
David M Cash ◽  
Pawel J Markiewicz ◽  
Jieqing Jiao ◽  
William Coath ◽  
Marc Modat ◽  
...  
Keyword(s):  
Dynamic Analysis ◽  
Longitudinal Analysis ◽  
Amyloid Pet ◽  
Static And Dynamic Analysis ◽  
Analysis Techniques

scholarly journals The MINESTRONE Architecture Combining Static and Dynamic Analysis Techniques for Software Security

2011 ◽  
Author(s):  
Angelos D. Keromytis ◽  
Salvatore J. Stolfo ◽  
Junfeng Yang ◽  
Angelos Stavrou ◽  
Anup Ghosh ◽  
...  
Keyword(s):  
Dynamic Analysis ◽  
Software Security ◽  
Static And Dynamic Analysis ◽  
Analysis Techniques

A General Framework to Detect Design Patterns by Combining Static and Dynamic Analysis Techniques

2021 ◽  
Vol 31 (01) ◽  
pp. 21-54
Author(s):  
Cong Liu
Keyword(s):  
Dynamic Analysis ◽  
Open Source ◽  
Design Pattern ◽  
Design Patterns ◽  
General Framework ◽  
Process Mining ◽  
Pattern Detection ◽  
Software Systems ◽  
Static And Dynamic Analysis ◽  
Analysis Techniques

Design pattern detection can provide useful insights to support software comprehension. Accurate and complete detection of pattern instances are extremely important to enable software usability improvements. However, existing design pattern detection approaches and tools suffer from the following problems: incomplete description of design pattern instances, inaccurate behavioral constraint checking, and inability to support novel design patterns. This paper presents a general framework to detect design patterns while solving these issues by combining static and dynamic analysis techniques. The framework has been instantiated for typical behavioral and creational patterns, such as the observer pattern, state pattern, strategy pattern, and singleton pattern to demonstrate the applicability. Based on the open-source process mining toolkit ProM, we have developed an integrated tool that supports the whole detection process for these patterns. We applied and evaluated the framework using software execution data containing around 1,000,000 method calls generated from eight synthetic software systems and three open-source software systems. The evaluation results show that our approach can guarantee a higher precision and recall than existing approaches and can distinguish state and strategy patterns that are indistinguishable by the state-of-the-art.

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

scholarly journals Analysis of Android Malware Using Data Replication Features Extracted by Machine Learning Tools

2019 ◽  
pp. 193-201
Author(s):  
Chandrashekhar Uppin ◽  
Gilbert George
Keyword(s):  
Machine Learning ◽  
Operating System ◽  
Dynamic Analysis ◽  
Identity Theft ◽  
Vital Role ◽  
Third Party ◽  
Learning Tools ◽  
Android Malware ◽  
Static And Dynamic Analysis ◽  
Using Data

In this era of technology, Smartphone plays a vital role in individual's life. Now-a-days, we tend to use smartphones for storing critical information like banking details, documents etc. as it makes it portable. Android is the most preferred type of operating system for smartphone as per consumer buying interest. But also, vulnerabilities are mainly targeted in case of android by malwares as android is the most vulnerable because of its third-party customization support, which results in identity theft, Denial of Services (DoS), Ransomware attacks etc. In this work, we present android malware called MysteryBot identification, static and dynamic analysis result. MysteryBot is a banking Trojan. Some recommended steps to make your android device safe from such kind of malwares infections are also explained in this paper.

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.

scholarly journals Building a Malware Detection System Based on a Machine Learning Method

2020 ◽  
Vol 9 (5) ◽  
pp. 1488-1493
Keyword(s):  
Machine Learning ◽  
Supervised Classification ◽  
Detection System ◽  
Malware Detection ◽  
Machine Learning Method ◽  
Clustering Methods ◽  
Unusual Behavior ◽  
Static And Dynamic Analysis ◽  
Malicious Behavior ◽  
Abnormal Behaviors

Malware attacks are dangerous and difficult to detect and prevent. Therefore, the task of detecting signs of malware and alerting it for users or the system is very necessary today. One of the most effective malware detection approaches is applying machine learning or deep learning to analyze its behavior. There have been many studies and recommendations to analyze malicious behavior then combined with some sorting or clustering methods to find their signs. In this paper, we will propose a method to use machine learning to detect malicious signs based on their unusual behavior. Accordingly, in our research, we will conduct malicious analysis using static and dynamic analysis methods to detect abnormal behaviors and combine them with a supervised classification algorithm to the conclusion on malware behavior.

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