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.

Identification of Cryptographic Vulnerability and Malware Detection in Android

2017 ◽  
Vol 11 (3) ◽  
pp. 15-28 ◽  
Author(s):  
Anjali Kumawat ◽  
Anil Kumar Sharma ◽  
Sunita Kumawat
Keyword(s):  
Dynamic Analysis ◽  
Static Analysis ◽  
Malware Detection ◽  
Android Application ◽  
Malicious Attacks ◽  
Web Page ◽  
Static And Dynamic Analysis ◽  
Android System ◽  
User Friendly ◽  
The Web

Android based Smartphones are nowadays getting more popular. While using Smartphone, user is always concerned about security and malicious attacks, cryptographic vulnerability of the applications. With increase in the number of Android mobiles, Android malwares are also increasing very rapidly. So the authors have proposed the “Identification of cryptographic vulnerability and malware detection in Android” system. They have designed a user friendly android application, through which user and developer can easily test the application whether it is benign or vulnerable. The application will be tested firstly using static analysis and then the dynamic analysis will be carried out. The authors have implemented static and dynamic analysis of android application for vulnerable and malicious app detection. They have also created a web page. User can either use the application or the web page.

Identification of Cryptographic Vulnerability and Malware Detection in Android

2021 ◽  
pp. 58-74
Author(s):  
Anjali Kumawat ◽  
Anil Kumar Sharma ◽  
Sunita Kumawat
Keyword(s):  
Dynamic Analysis ◽  
Static Analysis ◽  
Malware Detection ◽  
Android Application ◽  
Malicious Attacks ◽  
Web Page ◽  
Static And Dynamic Analysis ◽  
Android System ◽  
User Friendly ◽  
The Web

Android based Smartphones are nowadays getting more popular. While using Smartphone, user is always concerned about security and malicious attacks, cryptographic vulnerability of the applications. With increase in the number of Android mobiles, Android malwares are also increasing very rapidly. So the authors have proposed the “Identification of cryptographic vulnerability and malware detection in Android” system. They have designed a user friendly android application, through which user and developer can easily test the application whether it is benign or vulnerable. The application will be tested firstly using static analysis and then the dynamic analysis will be carried out. The authors have implemented static and dynamic analysis of android application for vulnerable and malicious app detection. They have also created a web page. User can either use the application or the web page.

scholarly journals Runtime Detection Framework for Android Malware

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
TaeGuen Kim ◽  
BooJoong Kang ◽  
Eul Gyu Im
Keyword(s):  
Dynamic Analysis ◽  
Static Analysis ◽  
Suffix Tree ◽  
Malware Detection ◽  
Application Programming Interface ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Dynamic Features ◽  
Android Malware ◽  
Android Malware Detection

As the number of Android malware has been increased rapidly over the years, various malware detection methods have been proposed so far. Existing methods can be classified into two categories: static analysis-based methods and dynamic analysis-based methods. Both approaches have some limitations: static analysis-based methods are relatively easy to be avoided through transformation techniques such as junk instruction insertions, code reordering, and so on. However, dynamic analysis-based methods also have some limitations that analysis overheads are relatively high and kernel modification might be required to extract dynamic features. In this paper, we propose a dynamic analysis framework for Android malware detection that overcomes the aforementioned shortcomings. The framework uses a suffix tree that contains API (Application Programming Interface) subtraces and their probabilistic confidence values that are generated using HMMs (Hidden Markov Model) to reduce the malware detection overhead, and we designed the framework with the client-server architecture since the suffix tree is infeasible to be deployed in mobile devices. In addition, an application rewriting technique is used to trace API invocations without any modifications in the Android kernel. In our experiments, we measured the detection accuracy and the computational overheads to evaluate its effectiveness and efficiency of the proposed framework.

scholarly journals Feature extraction for enhanced malware detection using genetic algorithm

2018 ◽  
Vol 7 (2.8) ◽  
pp. 444 ◽  
Author(s):  
Prerna Srivastava ◽  
Mohan Raj
Keyword(s):  
Genetic Algorithm ◽  
Feature Extraction ◽  
Dynamic Analysis ◽  
Static Analysis ◽  
Malware Detection ◽  
Extraction Process ◽  
Hybrid Analysis ◽  
Detection Techniques ◽  
Genetic Process ◽  
Use Of Internet

The use of internet has affected almost every field today. With the increase in use of internet, the number of malwares affecting the systems has also increased to a great deal. A number of techniques have been developed by the researchers in order to detect these malwares. The Malware Detection consists of two parts, the analysis part and the detection part. Malwares analysis can be categorized into Static analysis, Dynamic analysis and Hybrid Analysis. The Detection techniques can broadly be classified into Signature based techniques and Behaviour based techniques. A brief introduction of Malware Detection techniques is addressed here. The process of Feature Extraction plays a very important role in determining the efficiency and accuracy of the Malware Detection process. It aims at determining the subset of features that helps better differentiate between the malicious and benign files. We aim to provide a Feature Extraction process based on Genetic process that can be used for Malware Detection.

Pattern Recognition for Classification and Matching of Car Tires

2005 ◽  
Vol 33 (1) ◽  
pp. 2-17 ◽  
Author(s):  
D. Colbry ◽  
D. Cherba ◽  
J. Luchini
Keyword(s):  
Computer Vision ◽  
Feature Extraction ◽  
Pattern Matching ◽  
Fourier Transforms ◽  
Two Dimensions ◽  
Multiple Sources ◽  
Matching Process ◽  
Visual Searches ◽  
New Feature ◽  
2D Fft

Abstract Commercial databases containing images of tire tread patterns are currently used by product designers, forensic specialists and product application personnel to identify whether a given tread pattern matches an existing tire. Currently, this pattern matching process is almost entirely manual, requiring visual searches of extensive libraries of tire tread patterns. Our work explores a first step toward automating this pattern matching process by building on feature analysis techniques from computer vision and image processing to develop a new method for extracting and classifying features from tire tread patterns and automatically locating candidate matches from a database of existing tread pattern images. Our method begins with a selection of tire tread images obtained from multiple sources (including manufacturers' literature, Web site images, and Tire Guides, Inc.), which are preprocessed and normalized using Two-Dimensional Fast Fourier Transforms (2D-FFT). The results of this preprocessing are feature-rich images that are further analyzed using feature extraction algorithms drawn from research in computer vision. A new, feature extraction algorithm is developed based on the geometry of the 2D-FFT images of the tire. The resulting FFT-based analysis allows independent classification of the tire images along two dimensions, specifically by separating “rib” and “lug” features of the tread pattern. Dimensionality of (0,0) indicates a smooth treaded tire with no pattern; dimensionality of (1,0) and (0,1) are purely rib and lug tires; and dimensionality of (1,1) is an all-season pattern. This analysis technique allows a candidate tire to be classified according to the features of its tread pattern, and other tires with similar features and tread pattern classifications can be automatically retrieved from the database.

scholarly journals Application of Blockchain in Education: GDPR-Compliant and Scalable Certification and Verification of Academic Information

2021 ◽  
Vol 11 (10) ◽  
pp. 4537
Author(s):  
Christian Delgado-von-Eitzen ◽  
Luis Anido-Rifón ◽  
Manuel J. Fernández-Iglesias
Keyword(s):  
Data Protection ◽  
Personal Data ◽  
General Data Protection Regulation ◽  
Certification System ◽  
Proposed Model ◽  
Different Types ◽  
International Regulations ◽  
Innovative Solution ◽  
General Data ◽  
Academic Information

Blockchain technologies are awakening in recent years the interest of different actors in various sectors and, among them, the education field, which is studying the application of these technologies to improve information traceability, accountability, and integrity, while guaranteeing its privacy, transparency, robustness, trustworthiness, and authenticity. Different interesting proposals and projects were launched and are currently being developed. Nevertheless, there are still issues not adequately addressed, such as scalability, privacy, and compliance with international regulations such as the General Data Protection Regulation in Europe. This paper analyzes the application of blockchain technologies and related challenges to issue and verify educational data and proposes an innovative solution to tackle them. The proposed model supports the issuance, storage, and verification of different types of academic information, both formal and informal, and complies with applicable regulations, protecting the privacy of users’ personal data. This proposal also addresses the scalability challenges and paves the way for a global academic certification system.

scholarly journals Hypervisor-assisted dynamic malware analysis

Cybersecurity ◽  
2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Roee S. Leon ◽  
Michael Kiperberg ◽  
Anat Anatey Leon Zabag ◽  
Nezer Jacob Zaidenberg
Keyword(s):  
Dynamic Analysis ◽  
Static Analysis ◽  
Cyber Security ◽  
Malware Analysis ◽  
Analysis Tools ◽  
Significant Performance

AbstractMalware analysis is a task of utmost importance in cyber-security. Two approaches exist for malware analysis: static and dynamic. Modern malware uses an abundance of techniques to evade both dynamic and static analysis tools. Current dynamic analysis solutions either make modifications to the running malware or use a higher privilege component that does the actual analysis. The former can be easily detected by sophisticated malware while the latter often induces a significant performance overhead. We propose a method that performs malware analysis within the context of the OS itself. Furthermore, the analysis component is camouflaged by a hypervisor, which makes it completely transparent to the running OS and its applications. The evaluation of the system’s efficiency suggests that the induced performance overhead is negligible.

An Opcode-Based Malware Detection Model Using Supervised Learning Algorithms

2021 ◽  
Vol 15 (4) ◽  
pp. 18-30
Author(s):  
Om Prakash Samantray ◽  
Satya Narayan Tripathy
Keyword(s):  
Supervised Learning ◽  
Learning Algorithms ◽  
Malware Detection ◽  
Support Vector ◽  
Detection Accuracy ◽  
Detection Techniques ◽  
Detection Model ◽  
Proposed Model ◽  
Tree Classifier ◽  
Supervised Learning Algorithms

There are several malware detection techniques available that are based on a signature-based approach. This approach can detect known malware very effectively but sometimes may fail to detect unknown or zero-day attacks. In this article, the authors have proposed a malware detection model that uses operation codes of malicious and benign executables as the feature. The proposed model uses opcode extract and count (OPEC) algorithm to prepare the opcode feature vector for the experiment. Most relevant features are selected using extra tree classifier feature selection technique and then passed through several supervised learning algorithms like support vector machine, naive bayes, decision tree, random forest, logistic regression, and k-nearest neighbour to build classification models for malware detection. The proposed model has achieved a detection accuracy of 98.7%, which makes this model better than many of the similar works discussed in the literature.

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