Výsledky vyhledávání - Characterization and Detection of Android Malware

A Novel Machine Learning Approach for Android Malware Detection Based on the Co-Existence of Features

Autoři: Esraa Odat Qussai Yaseen

Zdroj: IEEE Access, Vol 11, Pp 15471-15484 (2023)

Témata: Artificial intelligence, Computer Networks and Communications, Android malware, co-existence, 02 engineering and technology, Malware, Characterization and Detection of Android Malware, Android, Android (operating system), Machine learning, 0202 electrical engineering, electronic engineering, information engineering, Data mining, Automated Software Testing Techniques, malware, 4. Education, Android Malware, Computer science, TK1-9971, Intrusion Detection, 3. Good health, Detection, Operating system, machine learning, Permission Analysis, Signal Processing, Computer Science, Physical Sciences, Network Intrusion Detection and Defense Mechanisms, Electrical engineering. Electronics. Nuclear engineering, Botnet Detection, Classifier (UML), Software, FP-growth, Random forest

Přístupová URL adresa: https://doaj.org/article/29f7debbd43b4736aa9cfd68010abe73

A Method for Automatic Android Malware Detection Based on Static Analysis and Deep Learning

Autoři: Mulhem Ibrahim Bayan Issa Muhammed Basheer Jasser

Zdroj: IEEE Access, Vol 10, Pp 117334-117352 (2022)

Témata: Artificial intelligence, Computer Networks and Communications, Android malware, 02 engineering and technology, Malware, Characterization and Detection of Android Malware, Android, Android (operating system), Computer security, Machine learning, 0202 electrical engineering, electronic engineering, information engineering, Automated Software Testing Techniques, malware, deep learning, Static analysis, Android Malware, Computer science, TK1-9971, Programming language, 3. Good health, Operating system, static analysis, mobile security, Signal Processing, Computer Science, Physical Sciences, Network Intrusion Detection and Defense Mechanisms, Security Analysis, System call, Electrical engineering. Electronics. Nuclear engineering, Botnet Detection, Software, The Internet

Přístupová URL adresa: https://doaj.org/article/46d2c68f989e4c70868cf0ff24cb486a

Securing Mobile Devices from Malware: A Faceoff Between Federated Learning and Deep Learning Models for Android Malware Classification

Autoři: Nachiappan Subramanian Logesh Ravi Mithin Jain Shaan a další

Zdroj: Journal of Computer Science. 20:254-264

Témata: FOS: Computer and information sciences, Artificial intelligence, Computer Networks and Communications, Android malware, Ransomware, Mobile device, Malware, 7. Clean energy, Characterization and Detection of Android Malware, Android (operating system), Computer security, Digital Forensics Research and Challenges, 9. Industry and infrastructure, 4. Education, Deep learning, Android Malware, Computer science, 3. Good health, Cryptovirology, World Wide Web, Operating system, Signal Processing, Computer Science, Physical Sciences, Network Intrusion Detection and Defense Mechanisms, Internet privacy, Mobile malware, Information Systems

Malware Attacks Detection in IoT Using Recurrent Neural Network (RNN)

Autoři: Abeer Abdullah Alsadhan Abdullah A. Al‐Atawi Hanen Karamti a další

Zdroj: Intelligent Automation & Soft Computing. 39:135-155

Témata: Artificial neural network, Artificial intelligence, IoT Security, Computer Networks and Communications, Internet of Things, Recurrent neural network, Android Malware, Malware, Computer science, Intrusion Detection, 3. Good health, Network Security, Characterization and Detection of Android Malware, Computer security, Signal Processing, Computer Science, Physical Sciences, Machine learning, Network Intrusion Detection and Defense Mechanisms, Botnet Detection

Android Malware Detection System Based on Ensemble Learning

Autoři: Orieb AbuAlghanam Hadeel Alazzam Mohammad Qatawneh a další

Témata: FOS: Computer and information sciences, Artificial intelligence, Support vector machine, Computer Networks and Communications, Android malware, Malware, Characterization and Detection of Android Malware, Android (operating system), Computer security, Ensemble learning, Machine learning, Data mining, Geography, Mobile Application Development and Cross-Platform Solutions, Linguistics, Static analysis, Android Malware, Computer science, Intrusion Detection, FOS: Philosophy, ethics and religion, Programming language, 3. Good health, Detection, Operating system, Philosophy, Mobile Application Development, Signal Processing, Computer Science, Physical Sciences, Network Intrusion Detection and Defense Mechanisms, FOS: Languages and literature, Covert, Botnet Detection, Benchmark (surveying), Classifier (UML), Geodesy, Information Systems

Investigation of Android Malware Using Deep Learning Approach

Autoři: V. Joseph Raymond R. Jeberson Retna Raj

Zdroj: Intelligent Automation & Soft Computing. 35:2413-2429

Témata: FOS: Computer and information sciences, Exploit, Artificial neural network, Artificial intelligence, Support vector machine, Backdoor, Computer Networks and Communications, 02 engineering and technology, Malware, 7. Clean energy, Detection and Prevention of Phishing Attacks, Characterization and Detection of Android Malware, Android (operating system), Computer security, Machine learning, 0202 electrical engineering, electronic engineering, information engineering, Multilayer perceptron, Perceptron, Naive Bayes classifier, Android Malware, Computer science, 3. Good health, Operating system, Signal Processing, Computer Science, Physical Sciences, Feature selection, Network Intrusion Detection and Defense Mechanisms, Security Analysis, Information Systems

Investigation of Android Malware with Machine Learning Classifiers using Enhanced PCA Algorithm

Autoři: V. Joseph Raymond R. Jeberson Retna Raj

Zdroj: Computer Systems Science and Engineering. 44:2147-2163

Témata: Exploit, Artificial intelligence, Computer Networks and Communications, Android malware, Opcode, 02 engineering and technology, Malware, FOS: Economics and business, Characterization and Detection of Android Malware, Android (operating system), Computer security, Machine learning, 0202 electrical engineering, electronic engineering, information engineering, Business, Source code, Data mining, Marketing, Automated Software Testing Techniques, Android Malware, Computer science, 3. Good health, Benchmarking, Operating system, Signal Processing, Computer Science, Physical Sciences, Network Intrusion Detection and Defense Mechanisms, Security Analysis, Botnet Detection, Software

Android malicious attacks detection models using machine learning techniques based on permissions

Autoři: Mousa Al-Akhras Abdulrhman ALMohawes Hani Omar a další

Zdroj: International Journal of Data and Network Science, Vol 7, Iss 4, Pp 2053-2076 (2023)

Témata: FOS: Computer and information sciences, Artificial intelligence, Computer Networks and Communications, Android malware, Social Sciences, Mobile device, Malware, 7. Clean energy, Characterization and Detection of Android Malware, Android (operating system), Computer security, Machine learning, Management. Industrial management, Embedded system, Mobile Application Development and Cross-Platform Solutions, HD28-70, Android Malware, Computer science, 3. Good health, Operating system, Mobile Application Development, Signal Processing, Computer Science, Physical Sciences, Network Intrusion Detection and Defense Mechanisms, Security Analysis, Mobile malware, Information Systems

Přístupová URL adresa: https://doaj.org/article/098a110707b24ab786f8563a265c4685

AndroMalPack: enhancing the ML-based malware classification by detection and removal of repacked apps for Android systems

Autoři: Husnain Rafiq Nauman Aslam Muhammad Aleem a další

Zdroj: Sci Rep
Scientific Reports, Vol 12, Iss 1, Pp 1-18 (2022)

Témata: Malware analysis, Computer Networks and Communications, Science, Android malware, 02 engineering and technology, Malware, Article, Characterization and Detection of Android Malware, Android (operating system), Computer security, 0202 electrical engineering, electronic engineering, information engineering, Data mining, Automated Software Testing Techniques, Data Collection, Android Malware, Computer science, Cryptovirology, Operating system, Signal Processing, Computer Science, Physical Sciences, Network Intrusion Detection and Defense Mechanisms, Medicine, Security Analysis, Smartphone, Algorithms, Software, Mobile malware

Přístupová URL adresa: https://pubmed.ncbi.nlm.nih.gov/36376412
https://doaj.org/article/8f8440d06b3e437894397b5ebf5257f9

Securing Android IoT devices with GuardDroid transparent and lightweight malware detection

Autoři: Ahsan Wajahat Jingsha He Nafei Zhu a další

Zdroj: Ain Shams Engineering Journal, Vol 15, Iss 5, Pp 102642-(2024)

Témata: FOS: Computer and information sciences, IoT, IoT Security, Computer Networks and Communications, Internet of Things, Android malware, RFE, Malware, Characterization and Detection of Android Malware, Android (operating system), Computer security, Android application, Machine learning, Digital Forensics Research and Challenges, Embedded system, Engineering (General). Civil engineering (General), Android Malware, Computer science, Android malware detection, Intrusion Detection, Operating system, SHAP, Signal Processing, Computer Science, Physical Sciences, Network Intrusion Detection and Defense Mechanisms, TA1-2040, Botnet Detection, Random forest, Information Systems

Přístupová URL adresa: https://doaj.org/article/0f3a3b6b6c4347edbfd34013ff727ecc

Applying Wide & Deep Learning Model for Android Malware Classification

Autoři: Le Duc Thuan Phạm Văn Hưởng Hoang Van Hiep a další

Zdroj: Computer Systems Science and Engineering. 45:2741-2759

Témata: FOS: Computer and information sciences, Artificial intelligence, Deep learning, Convolutional neural network, 02 engineering and technology, Android Malware, Computer science, Malware, 3. Good health, Operating system, Characterization and Detection of Android Malware, Deep Learning, Android (operating system), Signal Processing, Computer Science, Physical Sciences, Machine learning, 0202 electrical engineering, electronic engineering, information engineering, Computer Vision and Pattern Recognition, Digital Image Forgery Detection and Identification, Digital Forensics Research and Challenges, Data mining, Information Systems

Malware Evolution and Detection Based on the Variable Precision Rough Set Model

Autoři: M. Jerbi Zaineb Chelly Dagdia Slim Bechikh a další

Přispěvatelé: M. Jerbi Zaineb Chelly Dagdia Slim Bechikh a další

Zdroj: Annals of computer science and information systems, Vol 30, Pp 253-262 (2022)

Témata: Artificial intelligence, Outlier Detection, Computer Networks and Communications, Novelty Detection, Set (abstract data type), Information technology, 02 engineering and technology, [INFO] Computer Science [cs], Malware, Mathematical analysis, Anomaly Detection in High-Dimensional Data, Database, Characterization and Detection of Android Malware, Artificial Intelligence, Computer security, FOS: Mathematics, 0202 electrical engineering, electronic engineering, information engineering, Variable (mathematics), Data mining, [INFO.INFO-CR] Computer Science [cs]/Cryptography and Security [cs.CR], QA75.5-76.95, T58.5-58.64, Computer science, Intrusion Detection, Programming language, Detection, Electronic computers. Computer science, Rough set, Signal Processing, Computer Science, Physical Sciences, Network Intrusion Detection and Defense Mechanisms, Botnet Detection, Mathematics, Data modeling

Popis souboru: application/pdf

Přístupová URL adresa: https://uvsq.hal.science/hal-03723132v1
https://doaj.org/article/d036f251b13b4f3cacecfd65bccf3f02

AMDDLmodel: Android smartphones malware detection using deep learning model

Autoři: Muhammad Aamir Muhammad Waseem Iqbal Mariam Nosheen a další

Zdroj: PLoS One
PLoS ONE, Vol 19, Iss 1, p e0296722 (2024)

Témata: Artificial intelligence, Computer Networks and Communications, Science, FOS: Political science, Mobile device, Convolutional neural network, FOS: Law, Permission, Malware, Deep Learning, Engineering, Characterization and Detection of Android Malware, Android (operating system), Computer security, Machine learning, Political science, Automated Software Testing Techniques, Deep learning, Android Malware, Computer science, 3. Good health, Operating system, Computers, Handheld, Mental Recall, Signal Processing, Computer Science, Physical Sciences, Network Intrusion Detection and Defense Mechanisms, Medicine, Smartphone, Botnet Detection, Law, Software, Mobile malware, Research Article

Přístupová URL adresa: https://pubmed.ncbi.nlm.nih.gov/38241330
https://doaj.org/article/bde8333468b84ef9809d90c95bf479eb

Detection of Malware and Adware Using Machine Learning: A Practical Approach

Autoři: Dhruv Chaudhary Kanishk Bakshi

Témata: Artificial intelligence, Computer Networks and Communications, Android Malware, Malware, Computer science, Intrusion Detection, Data science, 3. Good health, Anomaly Detection in High-Dimensional Data, Detection, Characterization and Detection of Android Malware, Artificial Intelligence, Computer security, Signal Processing, Computer Science, Physical Sciences, Machine learning, Network Intrusion Detection and Defense Mechanisms, Security Analysis, Botnet Detection

Resampled Correlation-Based Feature Descriptors: A Novel Approach to Enhancing Malware Detection Capabilities

Autoři: Alex Dodoo Michael Koranteng Daniel Kwame Amissah a další

Témata: Artificial intelligence, Computer Networks and Communications, Feature (linguistics), Geometry, Linguistics, Pattern recognition (psychology), Malware, Computer science, Correlation, FOS: Philosophy, ethics and religion, 3. Good health, Anomaly Detection in High-Dimensional Data, Philosophy, Characterization and Detection of Android Malware, Artificial Intelligence, Computer security, Signal Processing, Computer Science, Physical Sciences, Machine learning, Network Intrusion Detection and Defense Mechanisms, FOS: Mathematics, FOS: Languages and literature, Data mining, Mathematics

AndroDex: Android Dex Images of Obfuscated Malware

Autoři: Sana Aurangzeb Muhammad Aleem Muhammad Taimoor Khan a další

Zdroj: Sci Data
Scientific Data, Vol 11, Iss 1, Pp 1-10 (2024)

Témata: Data Descriptor, Malware analysis, Artificial intelligence, Computer Networks and Communications, Obfuscation, Science, Android malware, Opcode, 02 engineering and technology, Malware, Characterization and Detection of Android Malware, Android (operating system), Computer security, Machine learning, 0202 electrical engineering, electronic engineering, information engineering, Data mining, Automated Software Testing Techniques, Static analysis, Android Malware, Computer science, Programming language, 3. Good health, Cryptovirology, Operating system, Signal Processing, Computer Science, Physical Sciences, Network Intrusion Detection and Defense Mechanisms, Software

Přístupová URL adresa: https://pubmed.ncbi.nlm.nih.gov/38365866
https://doaj.org/article/abc784bc8d5d4c4788559ac970e04d1c

Deep learning hybridization for improved malware detection in smart Internet of Things

Autoři: Abdulwahab Ali Almazroi Nasir Ayub

Zdroj: Sci Rep
Scientific Reports, Vol 14, Iss 1, Pp 1-18 (2024)

Témata: Optimization, Artificial intelligence, IoT Security, Computer Networks and Communications, Science, Flexibility (engineering), IoT security, Internet of Things, 02 engineering and technology, Malware, 7. Clean energy, Article, Data science, Anomaly Detection in High-Dimensional Data, Big data, Characterization and Detection of Android Malware, Deep Learning, Artificial Intelligence, Computer security, Botnet, Machine learning, FOS: Mathematics, 0202 electrical engineering, electronic engineering, information engineering, Data mining, Biology, Ecology, Statistics, Computer science, Intrusion Detection, Adaptability, World Wide Web, Detection, FOS: Biological sciences, Malware detection, Signal Processing, Computer Science, Physical Sciences, Network Intrusion Detection and Defense Mechanisms, BERT-based neural network, Medicine, Botnet Detection, The Internet, Mathematics

Přístupová URL adresa: https://pubmed.ncbi.nlm.nih.gov/38570575
https://doaj.org/article/8f0a6dc2874e414b8f916ae6032bbae5

FortisEDoS: A Deep Transfer Learning-Empowered Economical Denial of Sustainability Detection Framework for Cloud-Native Network Slicing

Autoři: Chafika Benzaïd Tarik Taleb Ashkan Sami a další

Zdroj: IEEE Transactions on Dependable and Secure Computing

Témata: Artificial intelligence, Denial-of-service attack, Computer Networks and Communications, 7. Clean energy, 12. Responsible consumption, Anomaly Detection in High-Dimensional Data, Transfer of learning, Characterization and Detection of Android Malware, Deep Learning, Artificial Intelligence, Computer security, 11. Sustainability, Cloud computing, 14. Life underwater, DDoS Attacks, Deep learning, 15. Life on land, Computer science, Intrusion Detection, World Wide Web, Detection, Operating system, 13. Climate action, Computer Science, Physical Sciences, Signal Processing, Network Intrusion Detection and Defense Mechanisms, Botnet Detection, The Internet

MalDroid: Secure DL‐enabled intelligent malware detection framework

Autoři: Ikram Ul Haq Tamim Ahmed Khan Adnan Akhunzada a další

Zdroj: IET Communications, Vol 16, Iss 10, Pp 1160-1171 (2022)

Témata: Computer communications, FOS: Computer and information sciences, Computer Networks and Communications, Android malware, TK5101-6720, 02 engineering and technology, Malware, Characterization and Detection of Android Malware, Android (operating system), Computer security, 0202 electrical engineering, electronic engineering, information engineering, Digital Forensics Research and Challenges, Data security, Mobile, ubiquitous and pervasive computing, 9. Industry and infrastructure, 4. Education, 1. No poverty, QA75.5-76.95 Electronic computers. Computer science, 16. Peace & justice, Android Malware, Computer science, Intrusion Detection, 3. Good health, Detection, Operating system, Signal Processing, Computer Science, Physical Sciences, Telecommunication, Network Intrusion Detection and Defense Mechanisms, Security Analysis, Botnet Detection, Mobile malware, Information Systems

Popis souboru: text

Přístupová URL adresa: https://onlinelibrary.wiley.com/doi/pdfdirect/10.1049/cmu2.12265
https://doaj.org/article/f56d31224eac4f7b902c48636bbac7c9
https://ietresearch.onlinelibrary.wiley.com/doi/10.1049/cmu2.12265

Enhanced detection of obfuscated malware in memory dumps: a machine learning approach for advanced cybersecurity

Autoři: Md. Alamgir Hossain Md. Saiful Islam

Zdroj: Cybersecurity, Vol 7, Iss 1, Pp 1-23 (2024)

Témata: FOS: Computer and information sciences, Computer engineering. Computer hardware, Computer Networks and Communications, 02 engineering and technology, Malware, Detection and Prevention of Phishing Attacks, TK7885-7895, Characterization and Detection of Android Malware, Computer security, 0202 electrical engineering, electronic engineering, information engineering, Memory dump analysis, Embedded system, Machine learning in cybersecurity, QA75.5-76.95, Computer science, Intrusion Detection, 3. Good health, Obfuscated malware detection, Detection, Advanced malware analytics, Electronic computers. Computer science, Signal Processing, Computer Science, Physical Sciences, Network Intrusion Detection and Defense Mechanisms, Security Analysis, Malware behavioral patterns, Botnet Detection, Information Systems

Přístupová URL adresa: https://doaj.org/article/a2cecb06ac364c6b9d2256e3efd7381e