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18th International Conference on Algorithms and Architectures for Parallel Processing (ICA3PP)

作者： Li, Qun Chen, Zhenxiang Yan, Qiben Wang, Shanshan Ma, Kun Shi, Yuliang Cui, Lizhen Univ Jinan Sch Informat Sci & Engn Jinan 250022 Shandong Peoples R China Shandong Prov Key Lab Network Based Intelligent C Jinan 250022 Shandong Peoples R China Univ Nebraska Lincoln NE USA Shandong Univ Jinan 250101 Shandong Peoples R China

ISBN: (纸本)9783030050634;9783030050627

With the popularization of smartphones, the number of mobile applications has grown substantially. However, many malware are emerging and thus pose a serious threat to the user's mobile phones. malware detection has become a public concern that requires urgent resolution. In this paper, we propose MulAV, a multilevel and explainable detection method with data fusion. Our method obtain information from multiple levels (the APP source code, network traffic, and geospatial information) and combine it with machine learning method to train a model which can identify mobile malware with high accuracy and few false alarms. Experimental result shows that MulAV outperforms other anti-virus scanners and methods and achieves a detection rate of 97.8% with 0.4% false alarms. Furthermore, for the benefit of users, MulAV displays the explanation for each detection, thus revealing relevant properties of the detected malware.

关键词： android malware detection Data fusion Multilevel Result explanation

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RepassDroid: Automatic detection of android malware Based on Essential Permissions and Semantic Features of Sensitive APIs 12

RepassDroid: Automatic Detection of Android Malware Based on...

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International Symposium on Theoretical Aspects of Software Engineering (TASE)

作者： Xie, Niannian Zeng, Fanping Qin, Xiaoxia Zhang, Yu Zhou, Mingsong Lv, Chengcheng Univ Sci & Technol China Sch Comp Sci & Technol Hefei Anhui Peoples R China Anhui Prov Key Lab Software Comp & Commun Hefei Anhui Peoples R China

ISBN: (纸本)9781538673058

Most current literature on android malware pays particular attention to the features of applications. Much of them focus on permissions or APIs, neglecting the behavioral semantics of applications, and the literature considering behavioral semantics is often expensive and weak in extendibility. In this paper, we introduce RepassDroid - a relatively coarse-grained but faster tool for automatic android malware detection. We define Generalized-sensitive API and emphasize on considering if the trigger points of generalized-sensitive APIs are UI-related or not. It analyzes the application by abstracting the generalized sensitive API with its trigger point as the semantic feature, with the addition of Really-essential Permission as the syntax feature. Then it utilizes machine learning to automatically determine whether an application is benign or malicious. We evaluate RepassDroid on 24288 samples in total, 20000 for training and 4288 for test. With the comparative experiments, we find that Random Forest is the optimal classification technique for our feature set, achieving 97.7% accuracy and 0.99 AUC, along with a malware classification precision as high as 99.3%. Our evaluation results confirm that our approach and the feature set are logical and effective for android malware detection.

关键词： android malware detection Static analysis Semantic features Machine learning

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Features Extraction from android Apps Using Reverse Engineering 6th

Features Extraction from Android Apps Using Reverse Enginee...

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6th International Conference on Computational Intelligence in Communications and Business Analytics, CICBA 2024

作者： Banik, Abhinandan Singh, Jyoti Prakash Department of Computer Science and Engineering National Institute of Technology Patna Patna India

ISBN: (纸本)9783031813382

In this ubiquitous age of mobile phones, android malware is a growing threat to mobile users. The most common malware detection technique is analyzing different features that any android app encompasses. However, malware developers have become increasingly sophisticated;hence, these android app features can’t be revealed easily. One way to address this problem is to apply reverse engineering on the android app executable file, which will eventually decompose the app into low-level source codes. After that, these source codes can be analyzed for subsequent identification of features, which will ultimately help to segregate malware from the benignware. In this article, a step-by-step procedure has been demonstrated to extract four important features (declared permission, intent, component, and hardware/software) from the android app using reverse engineering. This will open a path for new researchers in this domain. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： android malware detection Features Extraction Reverse Engineering

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NADM: Neural Network for android detection malware 18

NADM: Neural Network for Android Detection Malware

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9th International Symposium on Information and Communication Technology (SoICT)

作者： Nguyen Viet Duc Pham Thanh Giang Vietnam Acad Sci & Technol Inst Informat Technol Hanoi Vietnam

ISBN: (纸本)9781450365390

Over recent years, android is always captured roughly 80% of the worldwide smartphone volume. Due to its popularity and open characteristic, the android OS is becoming the system platform most targeted from mobile malware. They can cause a lot of damage on android devices such as data loss or sabotage of hardware. According to the predictive characteristics, machine learning is a good approach to deal with the number of new malwares increasing rapidly. In this paper, we propose Neural Network for android detection of malware (NADM). The NADM performs an analysis process to gather features of android applications. Then, these data will be converted into joint vector spaces, which to be input for the training part of deep learning process. Our classifier model can achieve a high accuracy system and has been applied in sProtect [15] on Google Play.

关键词： android malware detection Neural network model Static analysis

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Visualizing android Malicious Applications Using Texture Features

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INTERNATIONAL JOURNAL OF IMAGE AND GRAPHICS 2023年第6期23卷 2350052-2350052页

作者： Sharma, Tejpal Rattan, Dhavleesh Punjabi Univ Patiala Dept Comp Sci & Engn Patiala 147002 Punjab India CGC Coll Engn Dept Comp Sci & Engn Landran Mohali 140307 Punjab India

Context: Due to the change and advancement in technology, day by day the internet service usages are also increasing. Smartphones have become the necessity for every person these days. It is used to perform all basic daily activities such as calling, SMS, banking, gaming, entertainment, education, etc. Therefore, malware authors are developing new variants of malwares or malicious applications especially for monetary benefits. Objective: Objective of this research paper is to develop a technique that can be used to detect malwares or malicious applications on the android devices that will work for all types of packed or encrypted malicious applications, which usually evade decompiling tools. Method: In the proposed approach, visualization method is used for the detection of malware. In the first phase, application files are converted into images and then in second phase, texture feature of images are extracted using Grey Level Co-occurrence Matrix (GLCM). In the last phase, machine learning classification algorithms are used to classify the malicious and benign applications. Results: The proposed approach is rim on different datasets collected from various repositories. Different efficiency parameters are calculated and the proposed approach is compared with the existing approaches. Conclusion: We have proposed a static technique for efficient detection of malwares. The proposed technique performs better than the existing technique.

关键词： android malware detection texture features malware visualization mobile apps GLCM machine learning

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Machine learning approach to detect android malware using feature-selection based on feature importance score

Journal of Engineering Research (Kuwait)

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Journal of Engineering Research (Kuwait) 2024年

作者： Pathak, Amarjyoti Barman, Utpal Kumar, Th. Shanta Research Scholar GIMT Guwahati Under Assam Science and Technology University Jalukbari Assam Guwahati 781017 India Faculty of Computer Technology Assam Down Town University Assam Guwahati 781026 India Department of CSE GIMT Assam Guwahati 781017 India

The hazards posed by malware are proliferating along with technology's rapid advancement and the use of online services. Specifically, attacks on android devices are growing enormously because of the boost in the popularity of Smartphones. Existing research confirms that identifying benign or malware applications on the android platform is possible by analysing the permissions through the machine-learning classifier. There are machine-learning techniques that create models to detect android malware using permission-based attributes. However, further research is still needed to develop effective feature selection strategies for malware detection mechanisms in android. In this study, a machine-learning-based android malware detection mechanism is proposed, and standard machine-learning algorithms are used on multiple permission-based datasets to classify malware. This study suggests a feature selection method that uses the feature importance score computed using Gradient boosting to identify the essential permissions. The proposed methodology decreases the feature vector's dimension, reducing the model's training time. We also compare the classifier's performance with the complete feature set and with the reduced feature set. Examining the results, we notice that the algorithm's execution time improved significantly for all datasets with negligible loss in accuracy. © 2024 The Authors

关键词： android malware detection android permissions Feature importance score Feature selection Machine-learning

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MalDuoNet: A DualNet Framework to Detect android malware 15

MalDuoNet: A DualNet Framework to Detect Android Malware

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15th RIVF International Conference on Computing and Communication Technologies (RIVF)

作者： Palikhe, Aayasha Li, Longzhuang Tian, Feng Kar, Dulal Zhang, Ning Zhang, Wen Texas A&M Univ Corpus Christi Deptt Comp Sci Corpus Christi TX 78412 USA Texas A&M Univ Corpus Christi Dept Comp Sci Corpus Christi TX 78412 USA Xi An Jiao Tong Univ Sch Automat Sci & Engr Xian 710049 Shaanxi Peoples R China Univ Windsor Elect & Comp Engn 401 Sunset Ave Windsor ON Canada

ISBN: (纸本)9781665404358

Today mobile phones provide a wide range of applications that make our daily life easy. With popularity, smartphones have become a target for cybercrime where malicious apps are developed to acquire sensitive information or corrupt data. To mitigate this issue and to improve the security in mobile devices, different techniques have been used. These techniques can be broadly classified as static, dynamic and hybrid approaches. In this paper, a static-based model MalDuoNet is proposed to detect android malwares, which uses a DualNet framework to analyze the features from the API calls. In the MalDuoNet model, one sub-network is focused to learn the features relevant to malicious behavior and the other sub-network is focused to learn the features in general. Thus it enables the model to learn complementary features which in turn helps get richer features for analysis. Then the features from the two sub-networks are combined in the final fused classifier for the final classification. In addition, each of the feature extractors has a separate classifier so that each sub-network can optimize its performance separately. The experimental results demonstrate that the MalDuoNet model outperforms the two baseline models with single network.

关键词： android malware detection Convolution Neural Network (CNN) Application Programming Interface (API) Word Embedding

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GraphEvolveDroid: Mitigate Model Degradation in the Scenario of android Ecosystem Evolution 21

GraphEvolveDroid: Mitigate Model Degradation in the Scenario...

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30th ACM International Conference on Information and Knowledge Management (CIKM)

作者： Gu, Yonghao Li, Liangxun Beijing Univ Posts & Telecommun Beijing Key Lab Intelligent Telecommun Software & Sch Comp Sci Beijing Peoples R China Sun Yat Sen Univ Guangdong Prov Key Lab Informat Secur Technol Guangzhou Peoples R China

ISBN: (纸本)9781450384469

Machine learning-based android malware detection models suffer from model degradation over time due to ecosystem evolution, which means models trained on history data perform poorly on newly arrived data. Existing solutions to handle the above problem focus on sophisticated feature engineering to find stable features, which is labor-intensive. In this paper, we try to mitigate model degradation by substituting the representation paradigm from Euclidean (vector) to non-Euclidean (graph) without changing features and propose a graph-based android malware detection model called GraphEvolveDroid. Specifically, we first construct a directed evolutionary network with the KNN model, where each node represents an APP and the starting APP node of each edge is the ancestor of the ending APP node. Then we use stacked GCN layers to transmit the information of ancestor nodes to child nodes so that the shift of the distribution of child nodes can be suppressed. Experimental results on a large real dataset spanning three years demonstrate that GraphEvolveDroid could significantly mitigate model degradation because of slowing down the shift of data distribution.

关键词： android malware detection model degradation ecosystem evolution graph neural networks

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A feature selection technique based on rough set and improvised PSO algorithm (PSORS-FS) for permission based detection of android malwares

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INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2019年第7期10卷 1893-1907页

作者： Bhattacharya, Abhishek Goswami, Radha Tamal Mukherjee, Kuntal Inst Engn & Management Kolkata W Bengal India Techno India Coll Technol Kolkata W Bengal India Birla Inst Technol Mesra Jharkhand India

The set of permissions required by any android app during installation time is considered as the feature set which are used in permission based detection of android malwares. Those high dimensional feature set should be reduced to minimize computational overhead by choosing anoptimal sub set of features. In recent times, selection of meaningful attributes isan inevitable step for mining of large dimensional data and the application of heuristic feature selection algorithms are the main research directions in this field. Quality of classification measure is inspired by rough set theory and can be combined with bio inspired heuristic search techniques (Particle swarm optimization, Genetic Algorithm etc.) in selecting optimal or near optimal subsets of features. In this work, a feature selection technique based on rough set and improvised particle swarm optimization (PSO) algorithm is proposed for selection of features in thepermission based detection of android malwares. The main contribution of this work is to recommend a new random key encoding method which is used inthe proposed work (PSORS-FS) to convert classical PSO algorithm in discrete domain. It also reduces the issues related to maximum velocity of particles as well as sigmoid function which is related with binary PSO. PSORS-FS ensures diversity in thesearch process and it also reduces the tendency of premature convergence. Datasets of UCI, KEEL machine learning repository and two android permission datasets have been used to evaluate the performance of theproposed method. Better classification performance has been yielded by proposed method over conventional filters and wrapper methods for most of the machine learning classifiers.

关键词： Feature selection Particle swarm optimization (PSO) Rough set android malware detection Classification

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Vulnerability Evaluation of android malware Detectors against Adversarial Examples 25

Vulnerability Evaluation of Android Malware Detectors agains...

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25th KES International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES)

作者： Ijas, A. H. Vinod, P. Zemmari, Akka Harikrishnan Poulose, Godvin Jose, Don Mercaldo, Francesco Martinelli, Fabio Santone, Antonella SCMS Sch Engn & Technol Dept Comp Sci & Engn Ernakulam India Cochin Univ Sci & Technol Dept Comp Applicat Cochin Kerala India Univ Bordeaux LaBRI CNRS Bordeaux France Univ Molise Campobasso Italy IIT CNR Pisa Italy

In this paper, we evaluate the performance of machine learning classifiers (Logistic Regression, CART, Random Forest) by fabricating adversarial examples (malware samples) statistically identical to goodware. To this end, we demonstrate three scenarios, (a) random attribute injection (b) insertion of prominent attributes from legitimate apps and (c) poisoning of class labels, for creating tainted malware samples, to mislead reduce accuracy of classification models. Experiments were conducted on data-set consisting of 15649 android applications comprising 5373 malicious and 10276 legitimate apps. The outcome of investigations demonstrates significant drop in accuracies in the range of 12-50%. However, in the absence of adversarial examples in the test set, the performance of classifiers was observed between 94.8-97.9%. (C) 2021 The Authors. Published by Elsevier B.V.

关键词： Evasion attack label flipping attack classifiers android malware detection adversarial examples

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