Multi-Pattern Matching Based Dynamic Malware Detection in Smart Phones

2019 ◽  
pp. 421-441
Author(s):  
V. S. Devi ◽  
S. Roopak ◽  
Tony Thomas ◽  
Md. Meraj Uddin
Keyword(s):  
Pattern Matching ◽  
Malware Detection ◽  
Smart Phones

A Hybrid Approach to Detect the Malicious Applications in Android-Based Smartphones Using Deep Learning

2021 ◽  
pp. 626-644
Author(s):  
Manokaran Newlin Rajkumar ◽  
Varadhan Venkatesa Kumar ◽  
Ramachandhiran Vijayabhasker
Keyword(s):  
Deep Learning ◽  
Mobile Application ◽  
Hybrid Approach ◽  
Malware Detection ◽  
Control Flow ◽  
Smart Phones ◽  
The People ◽  
Flow Information ◽  
Android Applications ◽  
Modern Era

This modern era of technological advancements facilitates the people to possess high-end smart phones with incredible features. With the increase in the number of mobile applications, we are witnessing the humongous increase in the malicious applications. Since most of the Android applications are available open source and used frequently in the smart phones, they are more vulnerable. Statistical and dynamical-based malware detection approaches are available to verify whether the mobile application is a genuine one, but only to a certain extent, as the level of mobile application scanning done by the said approaches are in general routine or a common, pre-specified pattern using the structure of control flow, information flow, API call, etc. A hybrid method based on deep learning methodology is proposed to identify the malicious applications in Android-based smart phones in this chapter, which embeds the possible merits of both the statistical-based malware detection approaches and dynamical-based malware detection approaches and minimizes the demerits of them.

A Hybrid Approach to Detect the Malicious Applications in Android-Based Smartphones Using Deep Learning

2020 ◽  
pp. 176-194
Author(s):  
Manokaran Newlin Rajkumar ◽  
Varadhan Venkatesa Kumar ◽  
Ramachandhiran Vijayabhasker
Keyword(s):  
Deep Learning ◽  
Mobile Application ◽  
Hybrid Approach ◽  
Malware Detection ◽  
Control Flow ◽  
Smart Phones ◽  
The People ◽  
Flow Information ◽  
Android Applications ◽  
Modern Era

This modern era of technological advancements facilitates the people to possess high-end smart phones with incredible features. With the increase in the number of mobile applications, we are witnessing the humongous increase in the malicious applications. Since most of the Android applications are available open source and used frequently in the smart phones, they are more vulnerable. Statistical and dynamical-based malware detection approaches are available to verify whether the mobile application is a genuine one, but only to a certain extent, as the level of mobile application scanning done by the said approaches are in general routine or a common, pre-specified pattern using the structure of control flow, information flow, API call, etc. A hybrid method based on deep learning methodology is proposed to identify the malicious applications in Android-based smart phones in this chapter, which embeds the possible merits of both the statistical-based malware detection approaches and dynamical-based malware detection approaches and minimizes the demerits of them.

scholarly journals DETECTION OF HIDDEN FUNCTIONALITIES OF SMART PHONE MALWARE APP USING PATTERN-MATCHING TECHNIQUES

2020 ◽  
Vol 5 (11) ◽  
pp. 107-116
Author(s):  
M. Kathirvelu
Keyword(s):  
Dynamic Analysis ◽  
Static Analysis ◽  
Pattern Matching ◽  
Malware Detection ◽  
Smart Phone ◽  
Behavioral Differences ◽  
Matching Process ◽  
Proposed Model ◽  
Different Types ◽  
Matching Techniques

Malware developers are progressively using advanced techniques to defeat malware detection tools. One such technique commonly observed in recent malware samples consists of hiding and obfuscating modules containing malicious functionality in places that static analysis tools overlook. In this paper, we describe a dynamic analysis approach for detecting such hidden or obfuscated malware components distributed as parts of an app package. The key idea is behavioral differences between the original app and a number of automatically generated versions of it, where a number of modifications (faults) have been carefully injected. The differential signature is analyzed through a pattern-matching process driven by rules that relate different types of hidden functionalities with patterns found in the signature. A thorough justification and a description of the proposed model are provided.

scholarly journals Cryptojacking Malware Detection using the Bayesian Consensus Clustering with Large Iterative Multi-Tier Ensemble in the Cryptocurrency in the Cloud

2019 ◽  
Vol 8 (3) ◽  
pp. 4256-4264
Keyword(s):  
Malware Detection ◽  
Smart Phones ◽  
Consensus Clustering ◽  
Online Data ◽  
Virtual Currency ◽  
Malicious Software ◽  
Security Breaches ◽  
Major Threat ◽  
Digital Currency ◽  
Blockchain Technology

Virtual Currencies and cryptocurrency are a trending digital currency method which uses the Blockchain technology. Cryptocurrency is a digital method designed to exchange the asset between the users based on a powerful cryptography which ensures the transaction are safe and controllable. We have various legal areas identified while using the cryptocurrency, as being the virtual currency, the amount of assets used by the users increases rapidly. With the increase in the asset the security breaches are one of the key vulnerable areas to focus. Cryptocurrency mining malware or Cryptojacking remains a trending terminology which identifies the malicious software or malware developed to use the data from the smart phones and computers. The major threat of the Cryptojacking is cryptocurrency mining without user’s approval. This article implemented based on our CCEC Framework method published for Malware detection in SMS’s for the Smartphone users. The article explains about how the Malware detected using the CCEC Framework. Malwares created in various format so identifying the Malware takes time before which user assets remains vulnerable. So, the proposed method ensures we have a reduction in time by using various online data sources to identify the Cryptojacking malware.

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