Fuzzy-import hashing: A static analysis technique for malware detection

2021 ◽  
Vol 37 ◽  
pp. 301139
Author(s):  
Nitin Naik ◽  
Paul Jenkins ◽  
Nick Savage ◽  
Longzhi Yang ◽  
Tossapon Boongoen ◽  
...  
Keyword(s):  
Static Analysis ◽  
Malware Detection ◽  
Analysis Technique

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.

scholarly journals MALGRA: Machine Learning and N-Gram Malware Feature Extraction and Detection System

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1777
Author(s):  
Muhammad Ali ◽  
Stavros Shiaeles ◽  
Gueltoum Bendiab ◽  
Bogdan Ghita
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Detection System ◽  
Malware Detection ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Detection Methods ◽  
Normal Operation ◽  
Analysis Technique ◽  
N Gram

Detection and mitigation of modern malware are critical for the normal operation of an organisation. Traditional defence mechanisms are becoming increasingly ineffective due to the techniques used by attackers such as code obfuscation, metamorphism, and polymorphism, which strengthen the resilience of malware. In this context, the development of adaptive, more effective malware detection methods has been identified as an urgent requirement for protecting the IT infrastructure against such threats, and for ensuring security. In this paper, we investigate an alternative method for malware detection that is based on N-grams and machine learning. We use a dynamic analysis technique to extract an Indicator of Compromise (IOC) for malicious files, which are represented using N-grams. The paper also proposes TF-IDF as a novel alternative used to identify the most significant N-grams features for training a machine learning algorithm. Finally, the paper evaluates the proposed technique using various supervised machine-learning algorithms. The results show that Logistic Regression, with a score of 98.4%, provides the best classification accuracy when compared to the other classifiers used.

scholarly journals Malware Detection in Android Apps Using Static Analysis

2022 ◽  
Vol 24 (3) ◽  
pp. 1-25
Author(s):  
Nishtha Paul ◽  
Arpita Jadhav Bhatt ◽  
Sakeena Rizvi ◽  
Shubhangi
Keyword(s):  
Machine Learning ◽  
Static Analysis ◽  
Detection Rate ◽  
Empirical Studies ◽  
Malware Detection ◽  
Personal Data ◽  
Data Set ◽  
Android Apps ◽  
Malicious Apps ◽  
Day By Day

Frequency of malware attacks because Android apps are increasing day by day. Current studies have revealed startling facts about data harvesting incidents, where user’s personal data is at stake. To preserve privacy of users, a permission induced risk interface MalApp to identify privacy violations rising from granting permissions during app installation is proposed. It comprises of multi-fold process that performs static analysis based on app’s category. First, concept of reverse engineering is applied to extract app permissions to construct a Boolean-valued permission matrix. Second, ranking of permissions is done to identify the risky permissions across category. Third, machine learning and ensembling techniques have been incorporated to test the efficacy of the proposed approach on a data set of 404 benign and 409 malicious apps. The empirical studies have identified that our proposed algorithm gives a best case malware detection rate of 98.33%. The highlight of interface is that any app can be classified as benign or malicious even before running it using static analysis.

scholarly journals PROUD-MAL: static analysis-based progressive framework for deep unsupervised malware classification of windows portable executable

2021 ◽  
Author(s):  
Syed Khurram Jah Rizvi ◽  
Warda Aslam ◽  
Muhammad Shahzad ◽  
Shahzad Saleem ◽  
Muhammad Moazam Fraz
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Static Analysis ◽  
Deep Neural Network ◽  
Early Stage ◽  
Malware Detection ◽  
Machine Learning Algorithms ◽  
Organizational Network ◽  
Depth Analysis ◽  
Feature Attention

AbstractEnterprises are striving to remain protected against malware-based cyber-attacks on their infrastructure, facilities, networks and systems. Static analysis is an effective approach to detect the malware, i.e., malicious Portable Executable (PE). It performs an in-depth analysis of PE files without executing, which is highly useful to minimize the risk of malicious PE contaminating the system. Yet, instant detection using static analysis has become very difficult due to the exponential rise in volume and variety of malware. The compelling need of early stage detection of malware-based attacks significantly motivates research inclination towards automated malware detection. The recent machine learning aided malware detection approaches using static analysis are mostly supervised. Supervised malware detection using static analysis requires manual labelling and human feedback; therefore, it is less effective in rapidly evolutionary and dynamic threat space. To this end, we propose a progressive deep unsupervised framework with feature attention block for static analysis-based malware detection (PROUD-MAL). The framework is based on cascading blocks of unsupervised clustering and features attention-based deep neural network. The proposed deep neural network embedded with feature attention block is trained on the pseudo labels. To evaluate the proposed unsupervised framework, we collected a real-time malware dataset by deploying low and high interaction honeypots on an enterprise organizational network. Moreover, endpoint security solution is also deployed on an enterprise organizational network to collect malware samples. After post processing and cleaning, the novel dataset consists of 15,457 PE samples comprising 8775 malicious and 6681 benign ones. The proposed PROUD-MAL framework achieved an accuracy of more than 98.09% with better quantitative performance in standard evaluation parameters on collected dataset and outperformed other conventional machine learning algorithms. The implementation and dataset are available at https://bit.ly/35Sne3a.

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.

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