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android malware detection by Correlated Real Permission Couples Using FP Growth Algorithm and Neural Networks

IEEE ACCESS 2023年 11卷 124996-125010页

作者： Banik, Abhinandan Singh, Jyoti Prakash Natl Inst Technol Patna Patna 800005 India

In the current internet era, where mobile devices are ubiquitous and often hold sensitive personal and corporate data, android malware analysis is crucial to protect against the increasing sophistication and prevalence of mobile-based cyberattacks. This article proposes an innovative approach to android malware detection using real permissions features extracted from android code. This approach is unique among existing literature because, there for most cases, the declared permissions are being used, which is different from the real permission used in android apps. Here, step-by-step guidelines have been elaborated to perform reverse engineering in any android application package (APK) to extract the real permission feature from the android disassembled code. After that, the most frequent & correlated pairs of real permissions were identified using the Frequent Pattern (FP) Growth algorithm. Thereafter, the existence of those identified real permission couples was checked and fed into a multi-layered, K-fold cross-validated neural network model to predict whether an APK is malware or benignware. Simultaneously, five other traditional machine learning models have also been applied to benchmark the results. The outcome of these models is measured with various well-known metrics like Accuracy, Precision, Recall, Loss, Specificity, F1 Score, Receiver Operating Characteristic (ROC) curve, Negative Predictive Value, and Mathew's Correlation Coefficient. The experimental evaluation of the proposed methodology shows better performance on the well-known Drebin dataset as well as on the last 5 years customized dataset with an accuracy of over 96%.

关键词： malware Operating systems Feature extraction Codes Reverse engineering Static analysis Neural networks androids Permission Multi-layer neural network android malware detection reverse engineering android real permissions frequent pattern growth algorithm K-fold cross validation multi-layered neural network

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android malware detection method based on naive Bayes and permission correlation algorithm

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2018年第1期21卷 955-966页

作者： Shang, Fengjun Li, Yalin Deng, Xiaolin He, Dexiang Chongqing Univ Posts & Telecommun Coll Comp Sci & Technol Chongqing 400065 Peoples R China

In order to detect android malware more effectively, an android malware detection model was proposed based on improved naive Bayes classification. Firstly, considering the unknown permission that may be malicious in detection samples, and in order to improve the android detection rate, the algorithm of malware detection is proposed based on improved naive Bayes. Considering the limited training samples, limited permissions, and the new malicious permissions in the test samples, we used the impact of the new malware permissions and training permissions as the weight. The weighted naive Bayesian algorithm improves the android malware detection efficiency. Secondly, taking into account the detection model, we proposed a detection model of permissions and information theory based on the improved naive Bayes algorithm. We analyzed the correlation of the permission. By calculating the Pearson correlation coefficient, we determined the value of Pearson correlation coefficient r, and delete the permissions whose value r is less than the threshold rho and get the new permission set. So, we got the improved detection model by clustering based on information theory. Finally, we detected the 1725 android malware and 945 non malicious application of multiple data sets in the same simulation environment. The detection rate of the improved the naive Bayes algorithm is 86.54%, and the detection rate of the non-malicious application is increased to 97.59%. Based on the improved naive Bayes algorithm, the false detection rate of the improved detection model is reduced by 8.25%.

关键词： Naive Bayes android malware detection Privilege feature Correlation coefficient Information theory

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android malware detection: Investigating the impact of imbalanced data-sets on the performance of machine learning models. 24

Android malware detection: Investigating the impact of imbal...

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24th International Conference on Advanced Communication Technology (ICACT) - Artificial Intelligence Technologies toward Cybersecurity

作者： Sawadogo, Zakaria Mendy, Gervais Dembele, Jean Marie Ouya, Samuel Univ Cheikh Anta Diop Dakar LITA Lab Informat Telecommun & Applicat Dakar Senegal Univ Gaston Berger LANI Lab Anal Numer & Informat Dakar Senegal

ISBN: (纸本)9791188428090

Artificial intelligence has revolutionized many areas of research, including research on malicious application detection and classification. Nowadays, there are many approaches that learn from existing data and predict the classes of new data. Machine learning principles recommend a balance of classes in the training dataset, but the reality in the field is quite different. The majority of datasets used for malicious application detection are imbalanced. Class imbalance degrades classifier performance, so it is a common problem in classification tasks. This observation is much more significant in the area of android malware detection and classification. There are few works to our knowledge on the effects of imbalanced datasets in the field of android malware detection. Our contribution focuses on the impact of imbalanced datasets on the performance of different algorithms and the suitability of using evaluation metrics in android malware detection. We show that for malicious application detection, some classification algorithms are not suitable for unbalanced datasets. We also proved that some of the most used in literature performance evaluation metrics (Accuracy, Precision, Recall) are not very well adapted to imbalanced datasets. On the other hand, the metrics (Balanced accuracy, Geometric mean) are more adapted. These results were obtained by evaluating the performances of eleven classification algorithms and also the adequacy of the different evaluation metrics (Accuracy, Recall, Precision, F1 score, Balanced accuracy, Matthews corrcoef, Geometric mean, Fowlkes mallows).

关键词： imbalanced dataset android malware detection malware classification Artificial intelligence Machine learning

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android malware detection Using Local Binary Pattern and Principal Component Analysis 3rd

Android Malware Detection Using Local Binary Pattern and Pri...

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3rd International Conference of Pioneering Computer Scientists, Engineers and Educators (ICPCSEE)

作者： Wu, Qixin Qin, Zheng Zhang, Jinxin Yin, Hui Yang, Guangyi Hu, Kuangsheng Hunan Univ Coll Comp Sci & Elect Engn Changsha Hunan Peoples R China Hunan Key Lab Big Data Res & Applicat Changsha Hunan Peoples R China Hunan Inst Metrol & Test Changsha Hunan Peoples R China

ISBN: (纸本)9789811063855;9789811063848

Nowadays, analysis methods based on big data have been widely used in malicious software detection. Since android has become the dominator of smartphone operating system market, the number of android malicious applications are increasing rapidly as well, which attracts attention of malware attackers and researchers alike. Due to the endless evolution of the malware, it is critical to apply the analysis methods based on machine learning to detect malwares and stop them from leakaging our privacy information. In this paper, we propose a novel android malware detection method based on binary texture feature recognition by Local Binary Pattern and Principal Component Analysis, which can visualize malware and detect malware accurately. Also, our method analyzes malware binary directly without any decompiler, sandbox or virtual machines, which avoid time and resource consumption caused by decompiler or monitor in this process. Experimentation on 5127 benigns and 5560 malwares shows that we obtain a detection accuracy of 90%.

关键词： android malware detection Binary texture feature Local binary pattern Principal component analysis

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android malware detection: Feature Update Using Incremental Learning Approach: Further Investigation of UFILA 25

Android Malware Detection: Feature Update Using Incremental ...

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25th International Conference on Advanced Communication Technology (ICACT) - New Cyber Security Risks for Enterprise Amidst COVID-19 Pandemic

ISBN: (纸本)9791188428106

Several kinds of mobile applications are available on platforms offering various services to users. Both malware and good applications are found in software repositories, which is a major cybersecurity problem. To address this problem, machine learning approaches have been proposed in the literature for the detection of malware in general and malicious android applications in particular. But obfuscation techniques are used by some developers to hide malicious applications, which implies the need to update android malware detection *** the literature few works are focused on updating features. Our contribution is therefore an incremental learning approach capable of detecting android malware by taking into account both static and dynamic features. We propose through the UFILA approach an updated feature set for android malware detection and classification by adding new features. We evaluated 13 classification algorithms and selected the four most effective algorithms to implement our approach. The results obtained by our approach outperform several malware detection approaches in the literature. Our approach allows to detect malicious android applications based on static and dynamic features with feature updating. It also allows to efficiently classify the different classes.

关键词： imbalanced dataset android malware detection malware classification Artificial intelligence Machine learning

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android malware detection: An in-depth investigation of the impact of the use of imbalance datasets on the efficiency of machine learning models 25

Android malware detection: An in-depth investigation of the ...

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25th International Conference on Advanced Communication Technology (ICACT) - New Cyber Security Risks for Enterprise Amidst COVID-19 Pandemic

作者： Degrees, Zakaria Sawadogo Dembele, Jean-Marie Degrees, Gervais Mendy Ouya, Samuel Cheikh Anta Diop Univ LITA Lab Comp Sci Telecommun & Applicat Dakar Senegal Gaston Berger Univ LANI Lab Numer Anal & Comp Sci Dakar Senegal Gaston Berger Univ Dakar Senegal Univ Cheikh Anta Diop ESP Polytechn Sch Dakar Senegal Gaston Berger Univ Comp Sci Dakar Senegal

ISBN: (纸本)9791188428106

Machine learning techniques have become an essential part of research into the detection and classification of malicious applications. There are several approaches or algorithms that learn from existing data and predict classes. Machine learning principles recommend a balance of classes in the training dataset, but the reality on the ground is quite different. The majority of datasets used for malicious application detection are unbalanced. Class imbalance degrades classifier performance, so it is a common problem in classification tasks. This observation is much more significant in the field of android malware detection and classification. There is little work to our knowledge on the effects of unbalanced datasets in the field of android malware detection. Our contribution focuses on the impact of unbalanced datasets on the performance of different algorithms and the relevance of using evaluation metrics in android malware detection. And the state of the databases from which researchers typically draw datasets. We show that for malicious application detection, some classification algorithms are not suitable for unbalanced datasets. We also prove that some of the most widely used performance evaluation metrics in the literature (Accuracy, Precision, Recall) are not very well suited to unbalanced datasets. On the other hand, the metrics (Balanced Accuracy, Geometric mean) are more suitable. These results were obtained by evaluating the performances of eleven classification algorithms as well as the adequacy of the different evaluation metrics (Accuracy, Recall, Precision, F1_score, Balanced accuracy, Matthews corrcoef, Geometric mean, Fowlkes_mallows). Also not all databases are accessible by researchers and many of these databases are not updated.

关键词： imbalanced dataset android malware detection malware classification Artificial intelligence Machine learning

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android malware detection and identification frameworks by leveraging the machine and deep learning techniques: A comprehensive review

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TELEMATICS AND INFORMATICS REPORTS 2024年 14卷

作者： Smmarwar, Santosh K. Gupta, Govind P. Kumar, Sanjay Natl Inst Technol Dept Informat Technol Raipur 492010 India

The ever-increasing growth of online services and smart connectivity of devices have posed the threat of malware to computer system, android-based smart phones, Internet of Things (IoT)-based systems. The anti-malware software plays an important role in order to safeguard the system resources, data and information against these malware attacks. Nowadays, malware writers used advanced techniques like obfuscation, packing, encoding and encryption to hide the malicious activities. Because of these advanced techniques of malware evasion, traditional malware detection system unable to detect new variants of malware. Cyber security has attracted many researchers in the past for designing of Machine Learning (ML) or Deep Learning (DL) based malware detection models. In this study, we present a comprehensive review of the literature on malware detection approaches. The overall literature of the malware detection is grouped into three categories such as review of feature selection (FS) techniques proposed for malware detection, review of ML-based techniques proposed for malware detection and review of DL-based techniques proposed for malware detection. Based on literature review, we have identified the shortcoming and research gaps along with some future directives to design of an efficient malware detection and identification framework.

关键词： Machine learning Deep learning android malware detection malware detection

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android malware detection using Convolutional Deep Neural Networks 4

Android Malware Detection using Convolutional Deep Neural Ne...

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4th International Conference on Advanced Aspects of Software Engineering (ICAASE)

作者： Bourebaa, Fatima Benmohammed, Mohamed Univ Constantine 2 Abdelhamid Mehri Lire Lab Constantine Algeria

ISBN: (纸本)9781665422314

Deep learning in general and convolutional architectures, in particular, have pushed the limits of the current state of the art in the field of computer vision and the processing of natural languages and speech. Recently, these techniques have been applied to detect mobile malware and have once again shown their ability to remedy this type of problem. However, the most suitable deep network architecture for malware detection remains an open issue. In this paper, we investigate the possibilities of convolutional neural networks for efficient detection of mobile malware. Specifically, we address the impact of using inception based and multichannel architectures on network performance. We achieve an accuracy of 92% using a multichannel model on a set of 50000 malware and 50000 benign applications.

关键词： Deep learning Convolutional neural networks Mobile security android malware detection

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android malware detection BASED ON HETEROGENEOUS INFORMATION NETWORK WITH CROSS-LAYER FEATURES 19

ANDROID MALWARE DETECTION BASED ON HETEROGENEOUS INFORMATION...

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19th IEEE International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)

作者： Ren Xixuan Zhao Lirui Wang Kai Xue Zhixing Hou Anran Shao Qiao Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu 611731 Peoples R China

ISBN: (纸本)9781665493895

As a mature and open mobile operating system, android runs on many IoT devices, which has led to android-based IoT devices have become a hotbed of malware. Existing static detection methods for malware using artificial intelligence algorithms focus only on the java code layer when extracting API features, however there is a lot of malicious behavior involving native layer code. Thus, to make up for the neglect of the native code layer, we propose a heterogeneous information network-based android malware detection method with cross-layer features. We first translate the semantic information of apps and API calls into the form of meta-paths, and construct the adjacency of apps based on API calls, then combine information from different meta-paths using multi-core learning. We implemented our method on the dataset from VirusShare and AndroZoo, and the experimental results show that the accuracy of our method is 93.4%, which is at least 2% higher than other related methods using heterogeneous information networks for malware detection.

关键词： android malware detection Cross-layer features Heterogeneous information network (HIN) Internet of things (IoT) security

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android malware detection based on multi-category and tag level ratio 5

Android malware detection based on multi-category and tag le...

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IEEE 5th Information Technology and Mechatronics Engineering Conference (ITOEC)

作者： Yang, Zongjing Long, Zhaohua Chongqing Univ Posts & Telecommun Sch Comp Sci & Technol Chongqing 400000 Peoples R China

ISBN: (纸本)9781728143231;9781728143224

The arrival of the mobile platforms has brought much convenience to our life, people start to get used to social events, online shopping, and business activities with mobile phones, especially those with the android system. Since the open source of android system, it has rapidly penetrated into various internet ecosystems, a large number of android applications are released to the market every day. However, more and more malicious applications are being developed to steal user privacy and property. In this paper, firstly, based on the androidManifest file structure, a tag level ratio feature is proposed for detecting android malicious applications. Secondly, a new hybrid feature selection strategy is used to preprocessing permission and API features. Finally, through single model and ensemble model, 1100 malicious applications and 1567 nonmalicious applications were detected. The result show that our method has a good accuracy.

关键词： Tag level ratio Multi-category Stacking model android malware detection

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