Littérature scientifique sur le sujet « ANDROID MALWARE CLASSIFICATION »
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Articles de revues sur le sujet "ANDROID MALWARE CLASSIFICATION"
Pachhala, Nagababu, Subbaiyan Jothilakshmi et Bhanu Prakash Battula. « Android Malware Classification Using LSTM Model ». Revue d'Intelligence Artificielle 36, no 5 (23 décembre 2022) : 761–67. http://dx.doi.org/10.18280/ria.360514.
Texte intégralParajuli, Srijana, et Subarna Shakya. « Malware Detection and Classification Using Latent Semantic Indexing ». Journal of Advanced College of Engineering and Management 4 (31 décembre 2018) : 153–61. http://dx.doi.org/10.3126/jacem.v4i0.23205.
Texte intégralAfifah, Nurul, et Deris Stiawan. « The Implementation of Deep Neural Networks Algorithm for Malware Classification ». Computer Engineering and Applications Journal 8, no 3 (24 septembre 2019) : 189–202. http://dx.doi.org/10.18495/comengapp.v8i3.294.
Texte intégralJiang, Changnan, Kanglong Yin, Chunhe Xia et Weidong Huang. « FedHGCDroid : An Adaptive Multi-Dimensional Federated Learning for Privacy-Preserving Android Malware Classification ». Entropy 24, no 7 (1 juillet 2022) : 919. http://dx.doi.org/10.3390/e24070919.
Texte intégralMas`ud, Mohd Zaki, Shahrin Sahib, ., Mohd Faizal Abdollah, Siti Rahayu Selamat et Robiah Yusof. « Android Malware Detection System Classification ». Research Journal of Information Technology 6, no 4 (1 avril 2014) : 325–41. http://dx.doi.org/10.3923/rjit.2014.325.341.
Texte intégralNiu, Weina, Rong Cao, Xiaosong Zhang, Kangyi Ding, Kaimeng Zhang et Ting Li. « OpCode-Level Function Call Graph Based Android Malware Classification Using Deep Learning ». Sensors 20, no 13 (29 juin 2020) : 3645. http://dx.doi.org/10.3390/s20133645.
Texte intégralKumar, Ajit, Vinti Agarwal, Shishir Kumar Shandilya, Andrii Shalaginov, Saket Upadhyay et Bhawna Yadav. « PACER : Platform for Android Malware Classification, Performance Evaluation and Threat Reporting ». Future Internet 12, no 4 (12 avril 2020) : 66. http://dx.doi.org/10.3390/fi12040066.
Texte intégralSingh, Jaiteg, Deepak Thakur, Farman Ali, Tanya Gera et Kyung Sup Kwak. « Deep Feature Extraction and Classification of Android Malware Images ». Sensors 20, no 24 (8 décembre 2020) : 7013. http://dx.doi.org/10.3390/s20247013.
Texte intégralGupta, Charu, Rakesh Kumar Singh, Simran Kaur Bhatia et Amar Kumar Mohapatra. « DecaDroid Classification and Characterization of Malicious Behaviour in Android Applications ». International Journal of Information Security and Privacy 14, no 4 (octobre 2020) : 57–73. http://dx.doi.org/10.4018/ijisp.2020100104.
Texte intégralJiao, Jian, Qiyuan Liu, Xin Chen et 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.
Texte intégralThèses sur le sujet "ANDROID MALWARE CLASSIFICATION"
Barton, Daniel John Trevino. « Usable Post-Classification Visualizations for Android Collusion Detection and Inspection ». Thesis, Virginia Tech, 2016. http://hdl.handle.net/10919/72286.
Texte intégralMaster of Science
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.
Texte intégralScience, Faculty of
Computer Science, Department of
Graduate
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.
Texte intégralKUMAR, UPDESH. « ANDROID MALWARE CLASSIFICATION ». Thesis, 2017. http://dspace.dtu.ac.in:8080/jspui/handle/repository/15977.
Texte intégralMA, SHI-XIANG, et 馬詩翔. « Android Malware Classification based on Radial Basis Function Network ». Thesis, 2018. http://ndltd.ncl.edu.tw/handle/smf2qd.
Texte intégral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.
Chang, Yu-Ni, et 張育妮. « Three-phase Detection and Classification for Android Malware Based on Common Behaviors ». Thesis, 2013. http://ndltd.ncl.edu.tw/handle/74059509958542780235.
Texte intégral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.
Costa, Sara Silva. « Security threats management in android systems ». Master's thesis, 2017. http://hdl.handle.net/1822/55037.
Texte intégralWith 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.
Chapitres de livres sur le sujet "ANDROID MALWARE CLASSIFICATION"
Zhang, Mu, et Heng Yin. « Semantics-Aware Android Malware Classification ». Dans SpringerBriefs in Computer Science, 19–43. Cham : Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-47812-8_3.
Texte intégralGarg, Shivi, et Niyati Baliyan. « Android Malware Classification using Ensemble Classifiers ». Dans Cloud Security, 133–45. First edition. | Boca Raton : CRC Press, 2021. : CRC Press, 2021. http://dx.doi.org/10.1201/9780367821555-10.
Texte intégralLi, Dan, Runbang Pan, Ning Lu et Wenbo Shi. « CAFM : Precise Classification for Android Family Malware ». Dans 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.
Texte intégralGarg, Shivi, et Niyati Baliyan. « Classification of Android Malware Using Ensemble Classifiers ». Dans Mobile OS Vulnerabilities, 101–13. Boca Raton : CRC Press, 2023. http://dx.doi.org/10.1201/9781003354574-5.
Texte intégralFournier, Arthur, Franjieh El Khoury et Samuel Pierre. « Classification Method for Malware Detection on Android Devices ». Dans 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.
Texte intégralAktas, Kursat, et Sevil Sen. « UpDroid : Updated Android Malware and Its Familial Classification ». Dans Secure IT Systems, 352–68. Cham : Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-03638-6_22.
Texte intégralAhmad, Intan Nurfarahin, Farida Ridzuan, Madihah Mohd Saudi, Sakinah Ali Pitchay, Nurlida Basir et N. F. Nabila. « Android Mobile Malware Classification Using a Tokenization Approach ». Dans Transactions on Engineering Technologies, 271–85. Singapore : Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-2191-7_19.
Texte intégralPektaş, Abdurrahman, Mahmut Çavdar et Tankut Acarman. « Android Malware Classification by Applying Online Machine Learning ». Dans 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.
Texte intégralFaiz, Md Faiz Iqbal, Md Anwar Hussain et Ningrinla Marchang. « Android Malware Detection Using Multi-stage Classification Models ». Dans 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.
Texte intégralChaudhuri, Ayushi, Arijit Nandi et Buddhadeb Pradhan. « A Dynamic Weighted Federated Learning for Android Malware Classification ». Dans Soft Computing : Theories and Applications, 147–59. Singapore : Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-9858-4_13.
Texte intégralActes de conférences sur le sujet "ANDROID MALWARE CLASSIFICATION"
Kang, Byeongho, BooJoong Kang, Jungtae Kim et Eul Gyu Im. « Android malware classification method ». Dans the 2013 Research in Adaptive and Convergent Systems. New York, New York, USA : ACM Press, 2013. http://dx.doi.org/10.1145/2513228.2513295.
Texte intégralVinayakumar, R., K. P. Soman et Prabaharan Poornachandran. « Deep android malware detection and classification ». Dans 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI). IEEE, 2017. http://dx.doi.org/10.1109/icacci.2017.8126084.
Texte intégralLee, Yena, Yongmin Kim, Seungyeon Lee, Junyoung Heo et Jiman Hong. « Machine learning based Android malware classification ». Dans 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.
Texte intégralTurker, Sercan, et Ahmet Burak Can. « AndMFC : Android Malware Family Classification Framework ». Dans 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.
Texte intégralBovenzi, Giampaolo, Valerio Persico, Antonio Pescapé, Anna Piscitelli et Vincenzo Spadari. « Hierarchical Classification of Android Malware Traffic ». Dans 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.
Texte intégralAmenova, Shakhnaz, Cemil Turan et Dinara Zharkynbek. « Android Malware Classification by CNN-LSTM ». Dans 2022 International Conference on Smart Information Systems and Technologies (SIST). IEEE, 2022. http://dx.doi.org/10.1109/sist54437.2022.9945816.
Texte intégralMercaldo, Francesco, et Andrea Saracino. « Not so Crisp, Malware ! Fuzzy Classification of Android Malware Classes ». Dans 2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2018. http://dx.doi.org/10.1109/fuzz-ieee.2018.8491521.
Texte intégralAlam, Mohammed S., et Son T. Vuong. « Random Forest Classification for Detecting Android Malware ». Dans 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.
Texte intégralKim, Hye Min, Hyun Min Song, Jae Woo Seo et Huy Kang Kim. « Andro-Simnet : Android Malware Family Classification using Social Network Analysis ». Dans 2018 16th Annual Conference on Privacy, Security and Trust (PST). IEEE, 2018. http://dx.doi.org/10.1109/pst.2018.8514216.
Texte intégralMurtaz, Muhammad, Hassan Azwar, Syed Baqir Ali et Saad Rehman. « A framework for Android Malware detection and classification ». Dans 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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