scholarly journals Android malware family classification based on resource consumption over time

2017 ◽  
Author(s):  
Luca Massarelli ◽  
Leonardo Aniello ◽  
Claudio Ciccotelli ◽  
Leonardo Querzoni ◽  
Daniele Ucci ◽  
...  
Keyword(s):  
Resource Consumption ◽  
Android Malware ◽  
Family Classification ◽  
Over Time

Enhanced Android Malware Detection and Family Classification, using Conversation-level Network Traffic Features

2020 ◽  
Vol 17 (4A) ◽  
pp. 607-614
Author(s):  
Mohammad Abuthawabeh ◽  
Khaled Mahmoud
Keyword(s):  
Real World ◽  
Network Traffic ◽  
Malware Detection ◽  
The Other ◽  
Android Malware ◽  
Detection Algorithms ◽  
Android Malware Detection ◽  
Learning Technique ◽  
Massive Number ◽  
Family Classification

Signature-based malware detection algorithms are facing challenges to cope with the massive number of threats in the Android environment. In this paper, conversation-level network traffic features are extracted and used in a supervised-based model. This model was used to enhance the process of Android malware detection, categorization, and family classification. The model employs the ensemble learning technique in order to select the most useful features among the extracted features. A real-world dataset called CICAndMal2017 was used in this paper. The results show that Extra-trees classifier had achieved the highest weighted accuracy percentage among the other classifiers by 87.75%, 79.97%, and 66.71%for malware detection, malware categorization, and malware family classification respectively. A comparison with another study that uses the same dataset was made. This study has achieved a significant enhancement in malware family classification and malware categorization. For malware family classification, the enhancement was 39.71% for precision and 41.09% for recall. The rate of enhancement for the Android malware categorization was 30.2% and 31.14‬% for precision and recall, respectively

scholarly journals Behavior Intention Derivation of Android Malware Using Ontology Inference

2018 ◽  
Vol 2018 ◽  
pp. 1-13
Author(s):  
Jian Jiao ◽  
Qiyuan Liu ◽  
Xin Chen ◽  
Hongsheng Cao
Keyword(s):  
Reverse Engineering ◽  
Semantic Relation ◽  
Inference System ◽  
Android Malware ◽  
Behavior Intention ◽  
Multiple Behaviors ◽  
And Behavior ◽  
Ontology Inference ◽  
Ultimate Purpose ◽  
Family Classification

Previous researches on Android malware mainly focus on malware detection, and malware’s evolution makes the process face certain hysteresis. The information presented by these detected results (malice judgment, family classification, and behavior characterization) is limited for analysts. Therefore, a method is needed to restore the intention of malware, which reflects the relation between multiple behaviors of complex malware and its ultimate purpose. This paper proposes a novel description and derivation model of Android malware intention based on the theory of intention and malware reverse engineering. This approach creates ontology for malware intention to model the semantic relation between behaviors and its objects and automates the process of intention derivation by using SWRL rules transformed from intention model and Jess inference engine. Experiments on 75 typical samples show that the inference system can perform derivation of malware intention effectively, and 89.3% of the inference results are consistent with artificial analysis, which proves the feasibility and effectiveness of our theory and inference system.

scholarly journals Machine-Learning-Based Android Malware Family Classification Using Built-In and Custom Permissions

2021 ◽  
Vol 11 (21) ◽  
pp. 10244
Author(s):  
Minki Kim ◽  
Daehan Kim ◽  
Changha Hwang ◽  
Seongje Cho ◽  
Sangchul Han ◽  
...  
Keyword(s):  
Machine Learning ◽  
Positive Impact ◽  
Imbalanced Data ◽  
Classification Performance ◽  
Malware Analysis ◽  
Learning Approaches ◽  
Android Malware ◽  
Open Questions ◽  
Imbalanced Data Classification ◽  
Family Classification

Malware family classification is grouping malware samples that have the same or similar characteristics into the same family. It plays a crucial role in understanding notable malicious patterns and recovering from malware infections. Although many machine learning approaches have been devised for this problem, there are still several open questions including, “Which features, classifiers, and evaluation metrics are better for malware familial classification”? In this paper, we propose a machine learning approach to Android malware family classification using built-in and custom permissions. Each Android app must declare proper permissions to access restricted resources or to perform restricted actions. Permission declaration is an efficient and obfuscation-resilient feature for malware analysis. We developed a malware family classification technique using permissions and conducted extensive experiments with several classifiers on a well-known dataset, DREBIN. We then evaluated the classifiers in terms of four metrics: macrolevel F1-score, accuracy, balanced accuracy (BAC), and the Matthews correlation coefficient (MCC). BAC and the MCC are known to be appropriate for evaluating imbalanced data classification. Our experimental results showed that: (i) custom permissions had a positive impact on classification performance; (ii) even when the same classifier and the same feature information were used, there was a difference up to 3.67% between accuracy and BAC; (iii) LightGBM and AdaBoost performed better than other classifiers we considered.

Android Malware Family Classification using Images from Dex Files

2020 ◽  
Author(s):  
Munyeong Kang ◽  
Jihyeo Park ◽  
Seonghyun Park ◽  
Seong-je Cho ◽  
Minkyu Park
Keyword(s):  
Android Malware ◽  
Family Classification

Pixel-based Feature for Android Malware Family Classification using Machine Learning Algorithms

2021 ◽  
Author(s):  
Mohd Zamri Osman ◽  
Ahmad Firdaus Zainal Abidin ◽  
Rahiwan Nazar Romli ◽  
Mohd Faaizie Darmawan
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Android Malware ◽  
Family Classification

Green Charcoal

2020 ◽  
pp. 132-159
Author(s):  
Shreyas More ◽  
Meenal Sutaria
Keyword(s):  
Competitive Advantage ◽  
Applied Research ◽  
Industrial Revolution ◽  
High Growth ◽  
Resource Consumption ◽  
Natural Habitats ◽  
Material Resources ◽  
Fourth Industrial Revolution ◽  
Building Systems ◽  
Over Time

The two main challenges that future cities will face are the unavailability of material resources and the waste generated as a result of resource consumption. The chapter exhibits applied research into green charcoal that addresses the crisis of the fourth industrial revolution through the development of a biomaterial consisting of luffa, charcoal, and soil. It justifies that building materiality must be intentionally designed to transform over time and support an ecosystem of plants, insects, and birds to create self-sustaining natural habitats for all lifeforms. The approach to building materiality and building systems is performance-based, circular, and net positive, thus representing a departure from conventional architectural practices. It provides a framework for high-growth countries like India to reverse the resource crisis and achieve a competitive advantage over mature economies through such initiatives.

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