Výsledky vyhledávání - "Characterization and Detection of Android Malware"

An Automated Threat Intelligence Framework for Vehicle–Road Cooperation Systems

Autoři: Prabhat Kumar Randhir Kumar Alireza Jolfaei a další

Zdroj: IEEE Internet of Things Journal. 11:35964-35974

Témata: FOS: Computer and information sciences, Computer Networks and Communications, Information Security Policy Compliance and Awareness, Security Awareness, Transport engineering, Computer science, 7. Clean energy, Intrusion Detection, Detection, Characterization and Detection of Android Malware, Engineering, Computer security, 13. Climate action, Computer Science, Physical Sciences, Signal Processing, 11. Sustainability, Network Intrusion Detection and Defense Mechanisms, Security Analysis, Intelligent transportation system, Information Systems, Deterrence

Securing RPL-Based Networks Against Version Number and Rank Attacks

Autoři: Alaa Eddine Khalfoune Rachid Beghdad

Zdroj: Acta Informatica Pragensia, Vol 13, Iss 3, Pp 340-358 (2024)

Témata: Computer Networks and Communications, routing protocol for low power and lossy networks, security, 7. Clean energy, Characterization and Detection of Android Malware, Artificial Intelligence, Computer security, blocking techniques, Side-Channel Attacks, FOS: Mathematics, Cache Attacks, Language-based Information Flow Security, DDoS Attacks, lln, QA75.5-76.95, internet of things, Computer science, Network Security, Rank (graph theory), Combinatorics, Electronic computers. Computer science, Signal Processing, Computer Science, Physical Sciences, Network Intrusion Detection and Defense Mechanisms, Botnet Detection, Mathematics

Přístupová URL adresa: https://doaj.org/article/75ddb5b014fa43ba9d1e92c3478364d2

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

A Comprehensive Review of Machine Learning Approaches for Detecting Malicious Software

Autoři: Liu Yuanming Rodziah Latih

Zdroj: International Journal on Advanced Science, Engineering and Information Technology. 14:826-834

Témata: FOS: Computer and information sciences, Artificial intelligence, Computer Networks and Communications, Feature engineering, Variety (cybernetics), Malware, Leverage (statistics), Identification (biology), Characterization and Detection of Android Malware, Memory Analysis, Computer security, Machine learning, Digital Forensics Research and Challenges, Data mining, Biology, Botany, Deep learning, Computer science, Intrusion Detection, Process (computing), Programming language, 3. Good health, Detection, Operating system, Signal Processing, Computer Science, Physical Sciences, Network Intrusion Detection and Defense Mechanisms, Security Analysis, Botnet Detection, Software, Information Systems

A Cloud Forensics Framework to Identify, Gather, and Analyze Cloud Computing Incidents

Autoři: Rafef Al-mugern Siti Hajar Othman Arafat Al-Dhaqm a další

Zdroj: Engineering, Technology & Applied Science Research. 14:14483-14491

Témata: FOS: Computer and information sciences, Social Sciences, Analysis and Detection of Latent Fingerprints, 02 engineering and technology, Data science, 03 medical and health sciences, 0302 clinical medicine, Characterization and Detection of Android Malware, Computer security, 0202 electrical engineering, electronic engineering, information engineering, Cloud computing, Digital Forensics Research and Challenges, Digital forensics, Cloud Computing, 16. Peace & justice, Computer science, Digital Forensics, Detection, Operating system, Computer Science, Physical Sciences, Signal Processing, Security Analysis, Data Forensics, Safety Research, Information Systems

Cybersecurity Awareness, and Cybersecurity Behavior of High School Students in Davao City: A Mediation Role of Perceived Behavioral Control

Autoři: Neil Bryan B. Booc Kenneth Budiongan Ramil Carballo

Zdroj: European Journal of Applied Science, Engineering and Technology. 2:4-9

Témata: FOS: Computer and information sciences, Behavioral Intentions, Artificial intelligence, Computer Networks and Communications, FOS: Political science, Security Awareness, Control (management), FOS: Law, Characterization and Detection of Android Malware, Behavioral Analysis, 5. Gender equality, Computer security, Psychology, Political science, 4. Education, Information Security Policy Compliance and Awareness, Mediation, Computer science, FOS: Psychology, Computer Science, Physical Sciences, Signal Processing, Network Intrusion Detection and Defense Mechanisms, Internet privacy, Law, Information Systems

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

A Hybrid Deep Learning Approach for Spam Detection in Twitter

Autoři: Hemza Loucif

Zdroj: Ingénierie des systèmes d information. 29:117-123

Témata: FOS: Computer and information sciences, Review Spam, Artificial intelligence, Computer Networks and Communications, 7. Clean energy, Detection and Prevention of Phishing Attacks, Characterization and Detection of Android Malware, Deep Learning, Machine learning, Spamming, Deep learning, 16. Peace & justice, Computer science, 3. Good health, World Wide Web, Spam Detection, Spambot, Computer Science, Physical Sciences, Signal Processing, Bot Detection, 8. Economic growth, Network Intrusion Detection and Defense Mechanisms, Botnet Detection, The Internet, Information Systems

Blockchain and explainable AI for enhanced decision making in cyber threat detection

Autoři: Prabhat Kumar Danish Javeed Randhir Kumar a další

Zdroj: Software: Practice and Experience. 54:1337-1360

Témata: Computer Networks and Communications, 02 engineering and technology, Computer science, 7. Clean energy, Intrusion Detection, 3. Good health, Anomaly Detection in High-Dimensional Data, Characterization and Detection of Android Malware, Blockchain, Artificial Intelligence, Computer security, Computer Science, Physical Sciences, Signal Processing, 0202 electrical engineering, electronic engineering, information engineering, Network Intrusion Detection and Defense Mechanisms, Security Analysis, Internet privacy

CNN-GRU-FF: a double-layer feature fusion-based network intrusion detection system using convolutional neural network and gated recurrent units

Autoři: Yakubu Imrana Yanping Xiang Liaqat Ali a další

Zdroj: Complex & Intelligent Systems, Vol 10, Iss 3, Pp 3353-3370 (2024)

Témata: Artificial neural network, Artificial intelligence, Class imbalance, Computer Networks and Communications, Feature (linguistics), Convolutional neural network, Information technology, 02 engineering and technology, Deep neural network, Pattern recognition (psychology), Gated recurrent unit, Layer (electronics), Characterization and Detection of Android Malware, Artificial Intelligence, 0202 electrical engineering, electronic engineering, information engineering, Nanotechnology, Computational intelligence, Feature fusion, FOS: Nanotechnology, 9. Industry and infrastructure, Intrusion detection system, Linguistics, QA75.5-76.95, T58.5-58.64, Computer science, Materials science, Intrusion Detection, FOS: Philosophy, ethics and religion, Network Security, Detection, Philosophy, Machine Learning for Internet Traffic Classification, Electronic computers. Computer science, Computer Science, Physical Sciences, Signal Processing, Network Intrusion Detection and Defense Mechanisms, FOS: Languages and literature, Security Analysis, Botnet Detection, Network intrusion detection

Přístupová URL adresa: https://doaj.org/article/fa3e6463a35f45beb0246e30c47ee0e6

Digital Forensic Investigation Standards in Cloud Computing

Autoři: Ehigiator Egho-Promise Sunday Idahosa George Asante a další

Zdroj: Universal Journal of Computer Sciences and Communications. 3:23-45

Témata: FOS: Computer and information sciences, History, Digital forensics, Cloud Computing, 16. Peace & justice, Computer science, Data science, Digital Forensics, Detection, Operating system, Characterization and Detection of Android Malware, Archaeology, Computer security, Computer Science, Physical Sciences, Signal Processing, Cloud computing, Data Forensics, Digital evidence, Forensic science, Digital Forensics Research and Challenges, Information Systems

Adaptive Approach to Anomaly Detection in Internet of Things Using Autoencoders and Dynamic Thresholds

Autoři: Nayer Tumi Figueroa E Vishnu Priya A Selvanayaki Kolandapalayam Shanmugam a další

Zdroj: Journal of Machine and Computing. :1-10

Témata: Artificial intelligence, Outlier Detection, IoT Security, Computer Networks and Communications, Internet of Things, 02 engineering and technology, Anomaly detection, 7. Clean energy, Data science, Anomaly Detection in High-Dimensional Data, Characterization and Detection of Android Malware, Artificial Intelligence, Computer security, 11. Sustainability, 0202 electrical engineering, electronic engineering, information engineering, Biology, Ecology, Intrusion detection system, Computer science, Intrusion Detection, Adaptability, FOS: Biological sciences, Computer Science, Physical Sciences, Signal Processing, Network Intrusion Detection and Defense Mechanisms, Anomaly Detection, Botnet Detection, Interconnectivity

Smell-Aware Bug Classification

Autoři: null Khyber Sikandar Ali Fazli Wahid a další

Zdroj: IEEE Access, Vol 12, Pp 14061-14082 (2024)

Témata: FOS: Computer and information sciences, Code Clone Detection, Artificial intelligence, source code, Support vector machine, smell-aware, Software Defect Prediction, Software quality, Set (abstract data type), Code smell, Software bug, Characterization and Detection of Android Malware, Machine learning, Source code, Data mining, Code (set theory), Source Code Analysis, Precision and recall, bugs classification, Software engineering, Naive Bayes classifier, 4. Education, Software development, Computer science, TK1-9971, Programming language, Sensitivity Analysis, Maintainability, Computer Science, Physical Sciences, Signal Processing, Software Reliability Modeling, Electrical engineering. Electronics. Nuclear engineering, Software Reliability Assessment and Prediction, Software, Java, Empirical Studies in Software Engineering, Information Systems, Random forest

Přístupová URL adresa: https://doaj.org/article/5a8b603ffc5b4e7ca2882ee05b76d3b8

Toward Reduction in False Positives Just-In-Time Software Defect Prediction Using Deep Reinforcement Learning

Autoři: Ahmad Zuhdi Ismail Siti Hafizah Ab Hamid Asmiza Abdul Sani a další

Zdroj: IEEE Access, Vol 12, Pp 47568-47580 (2024)

Témata: FOS: Computer and information sciences, Artificial intelligence, QA75 Electronic computers. Computer science, False positive paradox, Software Defect Prediction, Geometry, Dynamic Analysis, Deep Q-Network, Characterization and Detection of Android Malware, Deep Learning, False positives and false negatives, QA76 Computer software, Reinforcement learning, Machine learning, false positives, FOS: Mathematics, Fault Detection and Correction, Data mining, Computer science, TK1-9971, Programming language, Computer Science, Physical Sciences, Signal Processing, Software Reliability Modeling, Reduction (mathematics), Electrical engineering. Electronics. Nuclear engineering, Software Reliability Assessment and Prediction, Classifier (UML), Software defect prediction, Software, Mathematics, Empirical Studies in Software Engineering, Information Systems

Přístupová URL adresa: https://doaj.org/article/dec7a1c5a9eb4be4abac954c05ece237

Utilizing Ensemble Learning for Detecting Multi-Modal Fake News

Autoři: Muhammad Luqman Muhammad Faheem Waheed Yousuf Ramay a další

Přispěvatelé: Muhammad Luqman Muhammad Faheem Waheed Yousuf Ramay a další

Zdroj: IEEE Access, Vol 12, Pp 15037-15049 (2024)

Témata: FOS: Computer and information sciences, Exploit, Artificial intelligence, Sociology and Political Science, bagged CNN, convolutional neural network, Social Sciences, fi=Tietotekniikka|en=Computer Science, Boosting (machine learning), Detection and Prevention of Phishing Attacks, Social media, Characterization and Detection of Android Malware, Computer security, Ensemble learning, Machine learning, boosted CNN, Information retrieval, Polymer chemistry, 10. No inequality, Preprocessor, Fake News, Natural language processing, 4. Education, multi-modal fake news, The Spread of Misinformation Online, Deep learning, Autoencoder, Rumor Detection, 16. Peace & justice, Computer science, TK1-9971, Encoder, World Wide Web, Chemistry, Operating system, classification, Fake news, Computer Science, Physical Sciences, Signal Processing, 8. Economic growth, Misinformation, Internet privacy, Electrical engineering. Electronics. Nuclear engineering, Modal, Information Systems

Popis souboru: fi=kokoteksti; en=fulltext; true

Přístupová URL adresa: https://doaj.org/article/e62e84da4e1a4312834825ff5dfbf74c

Inverse chi-square-based flamingo search optimization with machine learning-based security solution for Internet of Things edge devices

Autoři: Youseef Alotaibi R. Deepa K. Shankar a další

Zdroj: AIMS Mathematics, Vol 9, Iss 1, Pp 22-37 (2024)

Témata: FOS: Computer and information sciences, Exploit, Artificial intelligence, IoT Security, Computer Networks and Communications, security, 02 engineering and technology, 01 natural sciences, 7. Clean energy, Detection and Prevention of Phishing Attacks, feature selection, Characterization and Detection of Android Malware, Computer engineering, Computer security, Machine learning, QA1-939, 0202 electrical engineering, electronic engineering, information engineering, 0101 mathematics, Data mining, Geography, deep learning, edge devices, Edge computing, internet of things, Computer science, Intrusion Detection, Enhanced Data Rates for GSM Evolution, Algorithm, Detection, machine learning, Computer Science, Physical Sciences, Signal Processing, Feature selection, Network Intrusion Detection and Defense Mechanisms, Security Analysis, Botnet Detection, Benchmark (surveying), Mathematics, Geodesy, Information Systems

Přístupová URL adresa: https://doaj.org/article/8f0a63c24ba649e1ba93f37b61d15e19

A Survey on Network Slicing Security: Attacks, Challenges, Solutions and Research Directions: Attacks, Challenges, Solutions and Research Directions

Autoři: Chamitha de Alwis Pawani Porambage Kapal Dev a další

Zdroj: EEE Communications Surveys & Tutorials

Témata: Subject Categories::G420 Networks and Communications, Security through obscurity, network slicing, Computer Networks and Communications, Network Softwarization, 5G security, Surveys, Trust, privacy, 5G Security, Security in SDN, Characterization and Detection of Android Malware, Hardware Security and Authentication Techniques, Computer security, 5G mobile communication, network security, Cloud computing security, Cloud computing, Key (lock), Network slicing, Information security, Ultra reliable low latency communication, Software-Defined Networking and Network Virtualization, trust, Network security, Computer science, Software-Defined Networking, Network Security, Operating system, Privacy, network softwarization, Hardware and Architecture, Security service, Network Slicing, Computer Science, Physical Sciences, Signal Processing, Security, Security Analysis, Security information and event management

Přístupová URL adresa: https://cris.vtt.fi/en/publications/9a3dba62-d98a-4c65-836d-645127456d07
https://doi.org/10.1109/COMST.2023.3312349

Secure Storage of Crypto Wallet Seed Phrase Using ECC and Splitting Technique

Autoři: Syeda Tayyaba Bukhari Muhammad Umar Janjua Junaid Qadir

Zdroj: IEEE Open Journal of the Computer Society, Vol 5, Pp 278-289 (2024)

Témata: blockchain, FOS: Computer and information sciences, Cryptocurrency, Artificial intelligence, FOS: Political science, Encryption, FOS: Law, security, Information technology, Detection and Prevention of Phishing Attacks, Password, Characterization and Detection of Android Malware, Blockchain, Computer security, Political science, mnemonic phrase, Blockchain and Internet of Things Integration, Seed phrase storage, QA75.5-76.95, Phrase, T58.5-58.64, 16. Peace & justice, Computer science, Popularity, Electronic computers. Computer science, Signal Processing, Computer Science, Physical Sciences, Cryptography, master phrase, crypto wallets, Law, Information Systems

Popis souboru: application/pdf

Přístupová URL adresa: https://doaj.org/article/ab1ab48c549a463d98127b5cf2876455
https://hdl.handle.net/10576/66081

Weighted salp swarm algorithm with deep learning-powered cyber-threat detection for robust network security

Autoři: Maha M. Althobaiti José Escorcia‐Gutierrez

Zdroj: AIMS Mathematics, Vol 9, Iss 7, Pp 17676-17695 (2024)

Témata: Artificial intelligence, Computer Networks and Communications, 02 engineering and technology, Pattern recognition (psychology), 7. Clean energy, Swarm behaviour, Anomaly Detection in High-Dimensional Data, 03 medical and health sciences, Characterization and Detection of Android Malware, Deep Learning, 0302 clinical medicine, Artificial Intelligence, Computer security, Machine learning, network security, QA1-939, 0202 electrical engineering, electronic engineering, information engineering, salp swarm algorithm, cyber threat, Deep learning, Network security, Computer science, convolutional autoencoder, Intrusion Detection, Network Security, Algorithm, intrusion detection systems, Computer Science, Physical Sciences, Signal Processing, Network Intrusion Detection and Defense Mechanisms, Security Analysis, Botnet Detection, Mathematics

Přístupová URL adresa: https://doaj.org/article/70ff11a1d438471ba0570f1e62c30cef

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