Relevant bibliographies by topics / ANDROID MALWARE CLASSIFICATION

Academic literature on the topic 'ANDROID MALWARE CLASSIFICATION'

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Published: 11 September 2023

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Journal articles on the topic "ANDROID MALWARE CLASSIFICATION"

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Pachhala, Nagababu, Subbaiyan Jothilakshmi, and Bhanu Prakash Battula. "Android Malware Classification Using LSTM Model." Revue d'Intelligence Artificielle 36, no. 5 (December 23, 2022): 761–67. http://dx.doi.org/10.18280/ria.360514.

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From last two decades, smartphone use is essentially widespread around the world, and Android is the most popular open-source operating system, with the largest market share and active user population of any open-source operating system. This has resulted in malicious actors turning their attention toward the Android operating system to exploit user reliance and vulnerabilities that exist inside the system. Hackers can take advantage of consumers' sensitive data to engage in advertising, extortion, and theft. Most of the existing anti-malware software’s cannot be able to detect all the malwares because of the intelligent malwares. In this paper we use the deep learning based Long short-term memory (LSTM) network for android malware classification. The model is effective in classification of intelligent malwares. The proposed model is implemented using google colab. The model is archiving more than the 98% accuracy in classification of android malwares.
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Parajuli, Srijana, and Subarna Shakya. "Malware Detection and Classification Using Latent Semantic Indexing." Journal of Advanced College of Engineering and Management 4 (December 31, 2018): 153–61. http://dx.doi.org/10.3126/jacem.v4i0.23205.

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The increasing popularity of smart phones has led to the dramatic growth in mobile malware especially in Android platform. Many aspects of android permission has been studied for malware detection but sufficient attention has not been given to intent. This research work proposes using Latent Semantic Indexing for malware detection and classification with permissions and intents based features. This method analyses the Manifest file of an android application by understanding the risk level of permission and intents and assigning weight score based on their sensitivity. In an experiment conducted using a dataset containing 400 malware samples and 400 normal/benign samples, the results show accuracy of 83.5% using Android Intent against 79.1 % using Android permission. Additionally, experiment on combination of both features results in accuracy of 89.7%. It can be concluded from this research work that dataset with intent based features is able to detect malwares more when compared to permissions based features.
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Afifah, Nurul, and Deris Stiawan. "The Implementation of Deep Neural Networks Algorithm for Malware Classification." Computer Engineering and Applications Journal 8, no. 3 (September 24, 2019): 189–202. http://dx.doi.org/10.18495/comengapp.v8i3.294.

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Malware is very dangerous while attacked a device system. The device that can be attacked by malware is a Mobile Phone such an Android. Antivirus in the Android device is able to detect malware that has existed but antivirus has not been able to detect new malware that attacks an Android device. In this issue, malware detection techniques are needed that can grouping the files between malware or non-malware (benign) to improve the security system of Android devices. Deep Learning is the proposed method for solving problems in malware detection techniques. Deep Learning algorithm such as Deep Neural Network has succeeded in resolving the malware problem by producing an accuracy rate of 99.42%, precision level 99% and recall 99.4%.
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Jiang, Changnan, Kanglong Yin, Chunhe Xia, and Weidong Huang. "FedHGCDroid: An Adaptive Multi-Dimensional Federated Learning for Privacy-Preserving Android Malware Classification." Entropy 24, no. 7 (July 1, 2022): 919. http://dx.doi.org/10.3390/e24070919.

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With the popularity of Android and its open source, the Android platform has become an attractive target for hackers, and the detection and classification of malware has become a research hotspot. Existing malware classification methods rely on complex manual operation or large-volume high-quality training data. However, malware data collected by security providers contains user privacy information, such as user identity and behavior habit information. The increasing concern for user privacy poses a challenge to the current malware classification scheme. Based on this problem, we propose a new android malware classification scheme based on Federated learning, named FedHGCDroid, which classifies malware on Android clients in a privacy-protected manner. Firstly, we use a convolutional neural network and graph neural network to design a novel multi-dimensional malware classification model HGCDroid, which can effectively extract malicious behavior features to classify the malware accurately. Secondly, we introduce an FL framework to enable distributed Android clients to collaboratively train a comprehensive Android malware classification model in a privacy-preserving way. Finally, to adapt to the non-IID distribution of malware on Android clients, we propose a contribution degree-based adaptive classifier training mechanism FedAdapt to improve the adaptability of the malware classifier based on Federated learning. Comprehensive experimental studies on the Androzoo dataset (under different non-IID data settings) show that the FedHGCDroid achieves more adaptability and higher accuracy than the other state-of-the-art methods.
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Mas`ud, Mohd Zaki, Shahrin Sahib, ., Mohd Faizal Abdollah, Siti Rahayu Selamat, and Robiah Yusof. "Android Malware Detection System Classification." Research Journal of Information Technology 6, no. 4 (April 1, 2014): 325–41. http://dx.doi.org/10.3923/rjit.2014.325.341.

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Niu, Weina, Rong Cao, Xiaosong Zhang, Kangyi Ding, Kaimeng Zhang, and Ting Li. "OpCode-Level Function Call Graph Based Android Malware Classification Using Deep Learning." Sensors 20, no. 13 (June 29, 2020): 3645. http://dx.doi.org/10.3390/s20133645.

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Due to the openness of an Android system, many Internet of Things (IoT) devices are running the Android system and Android devices have become a common control terminal for IoT devices because of various sensors on them. With the popularity of IoT devices, malware on Android-based IoT devices is also increasing. People’s lives and privacy security are threatened. To reduce such threat, many researchers have proposed new methods to detect Android malware. Currently, most malware detection products on the market are based on malware signatures, which have a fast detection speed and normally a low false alarm rate for known malware families. However, they cannot detect unknown malware and are easily evaded by malware that is confused or packaged. Many new solutions use syntactic features and machine learning techniques to classify Android malware. It has been known that analysis of the Function Call Graph (FCG) can capture behavioral features of malware well. This paper presents a new approach to classifying Android malware based on deep learning and OpCode-level FCG. The FCG is obtained through static analysis of Operation Code (OpCode), and the deep learning model we used is the Long Short-Term Memory (LSTM). We conducted experiments on a dataset with 1796 Android malware samples classified into two categories (obtained from Virusshare and AndroZoo) and 1000 benign Android apps. Our experimental results showed that our proposed approach with an accuracy of 97 % outperforms the state-of-the-art methods such as those proposed by Nikola et al. and Hou et al. (IJCAI-18) with the accuracy of 97 % and 91 % , respectively. The time consumption of our proposed approach is less than the other two methods.
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Kumar, Ajit, Vinti Agarwal, Shishir Kumar Shandilya, Andrii Shalaginov, Saket Upadhyay, and Bhawna Yadav. "PACER: Platform for Android Malware Classification, Performance Evaluation and Threat Reporting." Future Internet 12, no. 4 (April 12, 2020): 66. http://dx.doi.org/10.3390/fi12040066.

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Android malware has become the topmost threat for the ubiquitous and useful Android ecosystem. Multiple solutions leveraging big data and machine-learning capabilities to detect Android malware are being constantly developed. Too often, these solutions are either limited to research output or remain isolated and incapable of reaching end users or malware researchers. An earlier work named PACE (Platform for Android Malware Classification and Performance Evaluation), was introduced as a unified solution to offer open and easy implementation access to several machine-learning-based Android malware detection techniques, that makes most of the research reproducible in this domain. The benefits of PACE are offered through three interfaces: Representational State Transfer (REST) Application Programming Interface (API), Web Interface, and Android Debug Bridge (ADB) interface. These multiple interfaces enable users with different expertise such as IT administrators, security practitioners, malware researchers, etc. to use their offered services. In this paper, we propose PACER (Platform for Android Malware Classification, Performance Evaluation, and Threat Reporting), which extends PACE by adding threat intelligence and reporting functionality for the end-user device through the ADB interface. A prototype of the proposed platform is introduced, and our vision is that it will help malware analysts and end users to tackle challenges and reduce the amount of manual work.
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Singh, Jaiteg, Deepak Thakur, Farman Ali, Tanya Gera, and Kyung Sup Kwak. "Deep Feature Extraction and Classification of Android Malware Images." Sensors 20, no. 24 (December 8, 2020): 7013. http://dx.doi.org/10.3390/s20247013.

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The Android operating system has gained popularity and evolved rapidly since the previous decade. Traditional approaches such as static and dynamic malware identification techniques require a lot of human intervention and resources to design the malware classification model. The real challenge lies with the fact that inspecting all files of the application structure leads to high processing time, more storage, and manual effort. To solve these problems, optimization algorithms and deep learning has been recently tested for mitigating malware attacks. This manuscript proposes Summing of neurAl aRchitecture and VisualizatiOn Technology for Android Malware identification (SARVOTAM). The system converts the malware non-intuitive features into fingerprint images to extract the quality information. A fine-tuned Convolutional Neural Network (CNN) is used to automatically extract rich features from visualized malware thus eliminating the feature engineering and domain expert cost. The experiments were done using the DREBIN dataset. A total of fifteen different combinations of the Android malware image sections were used to identify and classify Android malware. The softmax layer of CNN was substituted with machine learning algorithms like K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Random Forest (RF) to analyze the grayscale malware images. It observed that CNN-SVM model outperformed original CNN as well as CNN-KNN, and CNN-RF. The classification results showed that our method is able to achieve an accuracy of 92.59% using Android certificates and manifest malware images. This paper reveals the lightweight solution and much precise option for malware identification.
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Gupta, Charu, Rakesh Kumar Singh, Simran Kaur Bhatia, and Amar Kumar Mohapatra. "DecaDroid Classification and Characterization of Malicious Behaviour in Android Applications." International Journal of Information Security and Privacy 14, no. 4 (October 2020): 57–73. http://dx.doi.org/10.4018/ijisp.2020100104.

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Widespread use of Android-based applications on the smartphones has resulted in significant growth of security attack incidents. Malware-based attacks are the most common attacks on Android-based smartphones. To forestall malware from attacking the users, a much better understanding of Android malware and its behaviour is required. In this article, an approach to classify and characterise the malicious behaviour of Android applications using static features, data flow analysis, and machine learning techniques has been proposed. Static features like hardware components, permissions, Android components and inter-component communication along with unique source-sink pairs obtained from data flow analysis have been used to extract the features of the Android applications. Based on the features extracted, the malicious behaviour of the applications has been classified to their respective malware family. The proposed approach has given 95.19% accuracy rate and F1 measure of 92.19302 with the largest number of malware families classified as compared to previous work.
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Jiao, Jian, Qiyuan Liu, Xin Chen, and Hongsheng Cao. "Behavior Intention Derivation of Android Malware Using Ontology Inference." Journal of Electrical and Computer Engineering 2018 (2018): 1–13. http://dx.doi.org/10.1155/2018/9250297.

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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.
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Dissertations / Theses on the topic "ANDROID MALWARE CLASSIFICATION"

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Barton, Daniel John Trevino. "Usable Post-Classification Visualizations for Android Collusion Detection and Inspection." Thesis, Virginia Tech, 2016. http://hdl.handle.net/10919/72286.

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Android malware collusion is a new threat model that occurs when multiple Android apps communicate in order to execute an attack. This threat model threatens all Android users' private information and system resource security. Although recent research has made advances in collusion detection and classification, security analysts still do not have robust tools which allow them to definitively identify colluding Android applications. Specifically, in order to determine whether an alert produced by a tool scanning for Android collusion is a true-positive or a false-positive, the analyst must perform manual analysis of the suspected apps, which is both time consuming and prone to human errors. In this thesis, we present a new approach to definitive Android collusion detection and confirmation by rendering inter-component communications between a set of potentially collusive Android applications. Inter-component communications (abbreviated to ICCs), are a feature of the Android framework that allows components from different applications to communicate with one another. Our approach allows Android security analysts to inspect all ICCs within a set of suspicious Android applications and subsequently identify collusive attacks which utilize ICCs. Furthermore, our approach also visualizes all potentially collusive data-flows within each component within a set of apps. This allows analysts to inspect, step-by-step, the the data-flows that are currently used by collusive attacks, or the data-flows that could be used for future collusive attacks. Our tool effectively visualizes the malicious and benign ICCs in sets of proof-of-concept and real-world colluding applications. We conducted a user study which revealed that our approach allows for accurate and efficient identification of true- and false-positive collusive ICCs while still maintaining usability.
Master of Science
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Alam, Mohammed Shahidul. "An intelligent multi-agent based detection framework for classification of android malware." Thesis, University of British Columbia, 2016. http://hdl.handle.net/2429/59914.

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Smartphones play an important role in our day to day activities. Some of them include monitoring our health such as eating habits, sleep patterns and exercise schedule. The Android mobile operating system developed by Google is currently the most popular operating system for such smart devices. It is also the most vulnerable device due to its open nature of software installation, ability to dynamically load code during runtime, and lack of updates to known vulnerabilities even on popular versions of the system. Thus, securing such devices from malware that targets user privacy and monetary resources is paramount. In this thesis, we developed a context-aware multi-agent based framework targeted towards protecting Android devices. A malware detection technique has to be context-aware due to limited battery resources of mobile devices. In some cases however, battery utilization might become secondary. This includes scenarios where detection accuracy is given a higher priority over battery utilization. Thus, a detection framework has to be intelligent and flexible. To reach this goal, our framework relies on building multiple scalable context based models, and observing the behaviour patterns of Android devices by comparing to relevant pre-built models. We make use of machine learning classifiers that are more scalable to help classify features that could be used to detect malware by behaviour analysis. In this framework, the expensive analysis components utilizing machine learning algorithms are pushed to server side, while agents on the Android client are used mainly for context-aware feature gathering to transmit the information to server side classifiers for analysis, and to receive classification results from the server side agents.
Science, Faculty of
Computer Science, Department of
Graduate
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Kulkarni, Keyur. "Android Malware Detection through Permission and App Component Analysis using Machine Learning Algorithms." University of Toledo / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1525454213460236.

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KUMAR, UPDESH. "ANDROID MALWARE CLASSIFICATION." Thesis, 2017. http://dspace.dtu.ac.in:8080/jspui/handle/repository/15977.

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As indicated by AV merchants vindictive programming has been developing exponentially years ago. One of the principle purposes behind these high volumes is that all together to sidestep discovery, malware creators began utilizing polymorphic and transformative procedures. Therefore, conventional mark based ways to deal with recognize malware are being lacking against new malware and the classification of malware tests had turned out to be basic to know the premise of the conduct of malware and to battle back cybercriminals. Amid the most recent decade, arrangements that battle against pernicious programming had started utilizing machine learning approaches. Tragically, there are few open source datasets accessible for the scholarly group. One of the greatest datasets accessible was discharged a year ago in an opposition facilitated on Kaggle with information gave by Microsoft to the Huge Information Trailblazers Social event (Huge 2015). This proposition presents two novel and adaptable methodologies utilizing Neural Systems (NNs) to dole out malware to its comparing family. On one hand, the principal approach makes utilization of CNNs to take in a include pecking order to segregate among tests of malware spoke to as dark scale pictures. Then again, the second approach utilizes the CNN engineering acquainted by Yoon Kim [12] with order malware tests concurring their x86 guidelines. The proposed strategies accomplished a change of 80.86% and 81.56% as for the equivalent likelihood benchmark.
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MA, SHI-XIANG, and 馬詩翔. "Android Malware Classification based on Radial Basis Function Network." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/smf2qd.

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碩士
國立臺北科技大學
資訊工程系
107
N-Gram is a feature extraction method commonly used in information retrieval, while it can get lots of different combinations of features. For the feature extraction of the program, the usual practice is to use sandbox for execution simulation and construct a data collection environment for dynamic analysis. It usually captures system function calls, to monitor run- time network and system resource usage, etc..., which needs complex environment settings. This paper proposes to use the static analysis method to extract the opcodes through N-Gram and get the frequency of its occurrence for the application programs in the Android operating system. Compared to the dynamic analysis, the application program doesn’t need to be executed. And can be analyzed in its original form. After calculating the frequency of the opcode feature, we use two feature selection methods information gain and the information gain ratio, with two classifiers support vector machine and radial basis function network to evaluate the effectiveness for Android malware classification. The experimental results show that G-Mean 0.96 and MAE 0.0547 can be achieved in 3-Gram when using the information gain with RBF Net. Therefore, the effectiveness of the proposed method in classifying Android malware can be effectively classified.
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Chang, Yu-Ni, and 張育妮. "Three-phase Detection and Classification for Android Malware Based on Common Behaviors." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/74059509958542780235.

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碩士
國立交通大學
資訊科學與工程研究所
101
Android is one of the most popular operating systems adopted in mobile devices. The popularity also turns it an attractive target for attackers. To detect and classify malicious Android applications, we propose an efficient and accurate behavior-based solution with three phases. The first two phases detects malicious applications and the last phase classifies the detected malware. The “faster” first phase quickly filters out applications with their requested permissions judged by the Bayes model and therefore reduces the number of samples passed to the “slower” second phase which detects malicious applications with their system call sequences matched by the longest common substring (LCS) or N-gram algorithm. Finally, we classify a malware into known or unknown type based on cosine similarity of behavior or permission vectors. Our experiments show that the two-phase detection approach works more accurately than a single phase approach. It has a TP rate and a FP rate of 97% and 3%, respectively, with LCS in the second phase. More than 98% of samples can be classified correctly into known or new types based on permission vectors.
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Costa, Sara Silva. "Security threats management in android systems." Master's thesis, 2017. http://hdl.handle.net/1822/55037.

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Dissertação de mestrado integrado em Engenharia Eletrónica Industrial e Computadores
With the exponential use of mobile phones to handle sensitive information, the intrusion systems development has also increased. Malicious software is constantly being developed and the intrusion techniques are increasingly more sophisticated. Security protection systems trying to counteract these intrusions are constantly being improved and updated. Being Android one of the most popular operating systems, it became an intrusion’s methods development target. Developed security solutions constantly monitor their host system and by accessing a set of defined parameters they try to find potentially harmful changes. An important topic when addressing malicious applications detection is the malware identification and characterization. Usually, to separate the normal system behavior from the malicious behavior, security systems employ machine learning or data mining techniques. However, with the constant evolution of malicious applications, such techniques are still far from being capable of completely responding to the market needs. This dissertation aim was to verify if malicious behavior patterns definition is a viable way of addressing this challenge. As part of the proposed research two data mining classification models were built and tested with the collected data, and their performances were compared. the RapidMiner software was used for the proposed model development and testing, and data was collected from the FlowDroid application. To facilitate the understanding of the security potential of the Android framework, research was done on the its architecture, overall structure, and security methods, including its protection mechanisms and breaches. It was also done a study on models threats/attacks’ description, as well as, on the current existing applications for anti-mobile threats, analyzing their strengths and weaknesses.
Com o uso exponencial de telefones para lidar com informações sensíveis, o desenvolvimento de sistemas de intrusão também aumentou. Softwares maliciosos estão constantemente a ser desenvolvidos e as técnicas de intrusão são cada vez mais sofisticadas. Para neutralizar essas intrusões, os sistemas de proteção de segurança precisam constantemente de ser melhorados e atualizados. Sendo o Android um dos sistemas operativos (SO) mais populares, tornou-se também num alvo de desenvolvimento de métodos de intrusão. As soluções de segurança desenvolvidas monitoram constantemente o sistema em que se encontram e acedendo a um o conjunto definido de parâmetros procuram alterações potencialmente prejudiciais. Um tópico importante ao abordar aplicações mal-intencionadas é a identificação e caracterização do malware. Normalmente, para separar o comportamento normal do sistema do comportamento mal-intencionado, os sistemas de segurança empregam técnicas de machine learning ou de data mining. No entanto, com a constante evolução das aplicações maliciosas, tais técnicas ainda estão longe de serem capazes de responder completamente às necessidades do mercado. Esta dissertação teve como objetivo verificar se os padrões de comportamento malicioso são uma forma viável de enfrentar esse desafio. Para responder à pesquisa proposta foram construídos e testados dois modelos de classificação de dados, usando técnicas de data mining, e com os dados recolhidos compararam-se os seus desempenhos. Para o desenvolvimento e teste do modelo proposto foi utilizado o software RapidMiner, e os dados foram recolhidos através do uso da aplicação FlowDroid. Para facilitar a compreensão sobre as potencialidades de segurança da framework do Android, realizou-se uma pesquisa sobre a sua arquitetura, estrutura geral e métodos de segurança, incluindo seus mecanismos de defesa e algumas das suas limitações. Além disso, realizou-se um estudo sobre algumas das atuais aplicações existentes para a defesa contra aplicações maliciosas, analisando os seus pontos fortes e fracos.
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Book chapters on the topic "ANDROID MALWARE CLASSIFICATION"

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Zhang, Mu, and Heng Yin. "Semantics-Aware Android Malware Classification." In SpringerBriefs in Computer Science, 19–43. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-47812-8_3.

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Garg, Shivi, and Niyati Baliyan. "Android Malware Classification using Ensemble Classifiers." In Cloud Security, 133–45. First edition. | Boca Raton : CRC Press, 2021.: CRC Press, 2021. http://dx.doi.org/10.1201/9780367821555-10.

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Li, Dan, Runbang Pan, Ning Lu, and Wenbo Shi. "CAFM: Precise Classification for Android Family Malware." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 382–94. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-96791-8_28.

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Garg, Shivi, and Niyati Baliyan. "Classification of Android Malware Using Ensemble Classifiers." In Mobile OS Vulnerabilities, 101–13. Boca Raton: CRC Press, 2023. http://dx.doi.org/10.1201/9781003354574-5.

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Fournier, Arthur, Franjieh El Khoury, and Samuel Pierre. "Classification Method for Malware Detection on Android Devices." In Advances in Intelligent Systems and Computing, 810–29. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-63092-8_54.

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Aktas, Kursat, and Sevil Sen. "UpDroid: Updated Android Malware and Its Familial Classification." In Secure IT Systems, 352–68. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-03638-6_22.

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Ahmad, Intan Nurfarahin, Farida Ridzuan, Madihah Mohd Saudi, Sakinah Ali Pitchay, Nurlida Basir, and N. F. Nabila. "Android Mobile Malware Classification Using a Tokenization Approach." In Transactions on Engineering Technologies, 271–85. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-2191-7_19.

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Pektaş, Abdurrahman, Mahmut Çavdar, and Tankut Acarman. "Android Malware Classification by Applying Online Machine Learning." In Communications in Computer and Information Science, 72–80. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-47217-1_8.

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Faiz, Md Faiz Iqbal, Md Anwar Hussain, and Ningrinla Marchang. "Android Malware Detection Using Multi-stage Classification Models." In Complex, Intelligent and Software Intensive Systems, 244–54. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-50454-0_23.

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Chaudhuri, Ayushi, Arijit Nandi, and Buddhadeb Pradhan. "A Dynamic Weighted Federated Learning for Android Malware Classification." In Soft Computing: Theories and Applications, 147–59. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-9858-4_13.

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Conference papers on the topic "ANDROID MALWARE CLASSIFICATION"

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Kang, Byeongho, BooJoong Kang, Jungtae Kim, and Eul Gyu Im. "Android malware classification method." In the 2013 Research in Adaptive and Convergent Systems. New York, New York, USA: ACM Press, 2013. http://dx.doi.org/10.1145/2513228.2513295.

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Vinayakumar, R., K. P. Soman, and Prabaharan Poornachandran. "Deep android malware detection and classification." In 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI). IEEE, 2017. http://dx.doi.org/10.1109/icacci.2017.8126084.

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Lee, Yena, Yongmin Kim, Seungyeon Lee, Junyoung Heo, and Jiman Hong. "Machine learning based Android malware classification." In RACS '19: International Conference on Research in Adaptive and Convergent Systems. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3338840.3355693.

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Turker, Sercan, and Ahmet Burak Can. "AndMFC: Android Malware Family Classification Framework." In 2019 IEEE 30th International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC Workshops). IEEE, 2019. http://dx.doi.org/10.1109/pimrcw.2019.8880840.

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Bovenzi, Giampaolo, Valerio Persico, Antonio Pescapé, Anna Piscitelli, and Vincenzo Spadari. "Hierarchical Classification of Android Malware Traffic." In 2022 IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom). IEEE, 2022. http://dx.doi.org/10.1109/trustcom56396.2022.00191.

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Amenova, Shakhnaz, Cemil Turan, and Dinara Zharkynbek. "Android Malware Classification by CNN-LSTM." In 2022 International Conference on Smart Information Systems and Technologies (SIST). IEEE, 2022. http://dx.doi.org/10.1109/sist54437.2022.9945816.

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Mercaldo, Francesco, and Andrea Saracino. "Not so Crisp, Malware! Fuzzy Classification of Android Malware Classes." In 2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2018. http://dx.doi.org/10.1109/fuzz-ieee.2018.8491521.

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Alam, Mohammed S., and Son T. Vuong. "Random Forest Classification for Detecting Android Malware." In 2013 IEEE International Conference on Green Computing and Communications (GreenCom) and IEEE Internet of Things(iThings) and IEEE Cyber, Physical and Social Computing(CPSCom). IEEE, 2013. http://dx.doi.org/10.1109/greencom-ithings-cpscom.2013.122.

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Kim, Hye Min, Hyun Min Song, Jae Woo Seo, and Huy Kang Kim. "Andro-Simnet: Android Malware Family Classification using Social Network Analysis." In 2018 16th Annual Conference on Privacy, Security and Trust (PST). IEEE, 2018. http://dx.doi.org/10.1109/pst.2018.8514216.

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Murtaz, Muhammad, Hassan Azwar, Syed Baqir Ali, and Saad Rehman. "A framework for Android Malware detection and classification." In 2018 IEEE 5th International Conference on Engineering Technologies and Applied Sciences (ICETAS). IEEE, 2018. http://dx.doi.org/10.1109/icetas.2018.8629270.

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