Suchergebnisse - "Machine Learning for Internet Traffic Classification"

Hateful Sentiment Detection in Real-Time Tweets: An LSTM-Based Comparative Approach

Autoren: Sanjiban Sekhar Roy Abhijit Guha Roy Pijush Samui et al.

Quelle: IEEE Transactions on Computational Social Systems. 11:5028-5037

Schlagwörter: Artificial neural network, Artificial intelligence, Support vector machine, Naive Bayes classifier, Computer Networks and Communications, Offensive Language, Natural language processing, 4. Education, 16. Peace & justice, Computer science, 3. Good health, Detection, Artificial Intelligence, Machine Learning for Internet Traffic Classification, Computer Science, Physical Sciences, Machine learning, 8. Economic growth, Automated Detection of Hate Speech and Offensive Language, Network Intrusion Detection and Defense Mechanisms, Hate Speech, Botnet Detection, 10. No inequality, Natural Language Processing, Random forest

Meta‐analysis and systematic review for anomaly network intrusion detection systems: Detection methods, dataset, validation methodology, and challenges

Autoren: Ziadoon Kamil Maseer Qusay Kanaan Kadhim Baidaa Al‐Bander et al.

Quelle: IET Networks, Vol 13, Iss 5-6, Pp 339-376 (2024)

Schlagwörter: FOS: Computer and information sciences, Artificial intelligence, Computer Science - Cryptography and Security, Outlier Detection, Computer Networks and Communications, TK5101-6720, Systems and Control (eess.SY), Anomaly detection, Electrical Engineering and Systems Science - Systems and Control, Anomaly Detection in High-Dimensional Data, Traffic Analysis, Artificial Intelligence, computer network security, FOS: Electrical engineering, electronic engineering, information engineering, Anomaly-based intrusion detection system, Data mining, Intrusion detection system, Computer science, Intrusion Detection, Machine Learning for Internet Traffic Classification, Computer Science, Physical Sciences, Telecommunication, Network Intrusion Detection and Defense Mechanisms, Anomaly Detection, computer networks, Botnet Detection, Cryptography and Security (cs.CR)

Zugangs-URL: http://arxiv.org/abs/2308.02805
https://doaj.org/article/fb47601826e4494991980cd451b5df62

Social sensing with big data: Detecting hate speech in social media

Autoren: Umar Ibrahim Usman Lawal Gulma Ishaq Abdullahi Lawal

Quelle: International Journal of Science and Research Archive. 11:1146-1152

Schlagwörter: 9. Industry and infrastructure, Communication, 4. Education, Social Sciences, Speech recognition, 16. Peace & justice, Voice activity detection, Computer science, FOS: Sociology, Data science, Social media, World Wide Web, Big data, Sociology, Artificial Intelligence, Machine Learning for Internet Traffic Classification, Speech processing, Computer Science, Physical Sciences, 8. Economic growth, Automated Detection of Hate Speech and Offensive Language, Internet privacy, The Impact of Digital Media on Public Discourse, 10. No inequality, Data mining

Transforming online voting: a novel system utilizing blockchain and biometric verification for enhanced security, privacy, and transparency

Autoren: Md Jobair Hossain Faruk Fazlul Alam Mazharul Islam et al.

Quelle: Cluster Computing. 27:4015-4034

Schlagwörter: FOS: Computer and information sciences, User Authentication Methods and Security Measures, FOS: Political science, FOS: Law, 02 engineering and technology, Transparency (behavior), Blockchain, Artificial Intelligence, Computer security, 0202 electrical engineering, electronic engineering, information engineering, Political science, Authentication, Politics, Blockchain and Internet of Things Integration, Continuous Authentication, 16. Peace & justice, Computer science, Biometrics, Machine Learning for Internet Traffic Classification, E-Voting, Computer Science, Physical Sciences, Internet privacy, Voting, Law, Information Systems

Achieving Multi-Time-Step Segment Routing via Traffic Prediction and Compressive Sensing Techniques

Autoren: Van An Le Yusheng Ji Hoang The Tran et al.

Quelle: IEEE Transactions on Network and Service Management. 21:1534-1549

Schlagwörter: Computer Networks and Communications, End-to-End Congestion Control in Networks, Social Sciences, Network traffic simulation, 7. Clean energy, Real-time computing, Traffic engineering, Traffic Analysis, Traffic Management, Artificial Intelligence, Dynamics of Urban Structure through Spatial Network Analysis, Routing (electronic design automation), Computer network, Network packet, Computer science, Spatial Network Analysis, Network traffic control, Urban Studies, Overhead (engineering), Operating system, Machine Learning for Internet Traffic Classification, Traffic generation model, Computer Science, Physical Sciences, Delay Analysis, Encrypted Traffic

Using a Cloud-Based Electronic Signature System for Organizing Electronic Voting

Autoren: V. А. Herasimau М. А. Kazlouski

Quelle: Цифровая трансформация, Vol 30, Iss 1, Pp 52-62 (2024)

Schlagwörter: electronic voting, FOS: Political science, Geometry, FOS: Law, Information technology, 7. Clean energy, personal data protection, Artificial Intelligence, Computer security, authentication protocol, FOS: Mathematics, Cloud computing, Political science, signature activation protocol, electronic digital signature, Signature (topology), Digital Image Watermarking Techniques, Electronic voting, Electronic signature, Politics, T58.5-58.64, Computer science, Process (computing), cloud signature system, Operating system, Machine Learning for Internet Traffic Classification, E-Voting, Computer Science, Physical Sciences, Computer Vision and Pattern Recognition, Voting, Law, Mathematics

Zugangs-URL: https://doaj.org/article/8cb268c3cb864ab1b96c115534932ffd

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

Autoren: Yakubu Imrana Yanping Xiang Liaqat Ali et al.

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

Schlagwörter: 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

Zugangs-URL: https://doaj.org/article/fa3e6463a35f45beb0246e30c47ee0e6

An Improved and Efficient Coercion-resistant Measure for Electronic Voting System

Autoren: Tzer‐Long Chen Chia‐Hui Liu Ya-Hui Ou et al.

Quelle: International Journal of Information Security. 23:2637-2654

Schlagwörter: Secure Multi-party Computation, FOS: Political science, FOS: Law, 02 engineering and technology, 7. Clean energy, Artificial Intelligence, Computer security, Bullet voting, 0202 electrical engineering, electronic engineering, information engineering, Business, Coercion (linguistics), Political science, Order (exchange), Secure Computation, Advanced Cryptographic Schemes and Protocols, Electronic voting, Politics, Privacy-Preserving Techniques for Data Analysis and Machine Learning, Linguistics, Volition (linguistics), 16. Peace & justice, Computer science, Cardinal voting systems, FOS: Philosophy, ethics and religion, Philosophy, Machine Learning for Internet Traffic Classification, E-Voting, Computer Science, Physical Sciences, Cryptography, FOS: Languages and literature, Internet privacy, Voting, Law, Finance

MAPLE: MArkov Process Leakage attacks on Encrypted Search

Autoren: Seny Kamara Abdelkarim Kati Tarik Moataz et al.

Quelle: Proceedings on Privacy Enhancing Technologies. 2024:430-446

Schlagwörter: Economics, Markov chain, Macroeconomics, Encryption, 02 engineering and technology, Leverage (statistics), Searchable Encryption, Theoretical computer science, Artificial Intelligence, Computer security, Machine learning, 0202 electrical engineering, electronic engineering, information engineering, Attribute-Based Encryption, Data mining, Secure Computation, Advanced Cryptographic Schemes and Protocols, Privacy-Preserving Techniques for Data Analysis and Machine Learning, Computer science, Information leakage, Leakage (economics), Machine Learning for Internet Traffic Classification, Computer Science, Physical Sciences, Hash function, Cryptography, Encrypted Traffic, Homomorphic Encryption

Developing a Unique Identifier of Smallest Possible Ballot Area that Create One-One-Match with Voting Unit Identifier for Efficient Election Management in Jos South LGA Plateau State, Nigeria

Autoren: Isa Agaie Dawodu Francis Ifeanyi Okeke Mubarak Umar Faruk et al.

Quelle: Journal of Psychology and Political Science. :1-9

Schlagwörter: FOS: Computer and information sciences, Metric (unit), Identifier, FOS: Political science, FOS: Law, Ballot, Database, Engineering, Artificial Intelligence, Computer security, FOS: Mathematics, Political science, Computer network, Politics, Unit (ring theory), Polling, 16. Peace & justice, Computer science, Mathematics education, Operations management, Machine Learning for Internet Traffic Classification, E-Voting, Computer Science, Physical Sciences, Voting, Data Mining in Various Applications, Law, Mathematics, Information Systems

Quantum-empowered federated learning and 6G wireless networks for IoT security: Concept, challenges and future directions

Autoren: Danish Javeed Muhammad Shahid Saeed Ijaz Ahmad et al.

Quelle: Future Generation Computer Systems. 160:577-597

Schlagwörter: Computer network, Computer Networks and Communications, Physics, Internet of Things, Privacy-Preserving Techniques for Data Analysis and Machine Learning, Computer science, Quantum mechanics, 7. Clean energy, Quantum, IoT Devices, Cooperative Diversity in Wireless Networks, Artificial Intelligence, Machine Learning for Internet Traffic Classification, Computer security, Computer Science, Physical Sciences, Wireless, Telecommunications, Federated Learning, Wireless network, Secure Computation

Hades: Practical Decentralized Identity with Full Accountability and Fine-grained Sybil-resistance

Autoren: Ke Wang Jianbo Gao Q. Edward Wang et al.

Quelle: Annual Computer Security Applications Conference. :216-228

Schlagwörter: Sociology and Political Science, Information privacy, FOS: Political science, Social Sciences, FOS: Law, Artificial Intelligence, Computer security, Trust and Reputation Systems in Online Environments, Access control, Accountability, Political science, Biology, Identity-Based Encryption, Ecology, Advanced Cryptographic Schemes and Protocols, 9. Industry and infrastructure, Physics, Access Control, 1. No poverty, Resistance (ecology), Acoustics, 16. Peace & justice, Computer science, Overhead (engineering), Identity (music), Operating system, Machine Learning for Internet Traffic Classification, FOS: Biological sciences, Computer Science, Physical Sciences, Identity management, Internet privacy, Anonymity, Law

Passion-Net: a robust precise and explainable predictor for hate speech detection in Roman Urdu text

Autoren: Faiza Mehmood Hina Ghafoor Muhammad Nabeel Asim et al.

Quelle: Neural Computing and Applications. 36:3077-3100

Schlagwörter: FOS: Computer and information sciences, Artificial intelligence, Offensive Language, 02 engineering and technology, Speech recognition, Detection and Prevention of Phishing Attacks, Sentiment analysis, Computational Science and Engineering, Artificial Intelligence, F1 score, Machine learning, Automated Detection of Hate Speech and Offensive Language, 0202 electrical engineering, electronic engineering, information engineering, 10. No inequality, Natural Language Processing, Geography, Natural language processing, 4. Education, Discriminative model, Linguistics, German, Computer science, Urdu, FOS: Philosophy, ethics and religion, Detection, Philosophy, Machine Learning for Internet Traffic Classification, Computer Science, Physical Sciences, Bot Detection, FOS: Languages and literature, Benchmark (surveying), Classifier (UML), Geodesy, Information Systems

New Research Trends in Designing E-Government Architecture Based on Blockchain Technology

Autoren: Raed Abduljabbar Falih Yusmadi Yah Jusoh Dheya Jassim Khadhim

Quelle: Journal of Engineering, Vol 29, Iss 11 (2023)

Schlagwörter: FOS: Computer and information sciences, Economics, FOS: Political science, Smart contract, Social Sciences, FOS: Mechanical engineering, Engineering, Evolution and Impact of E-Government Initiatives, Computer security, Architecture, 11. Sustainability, Government (linguistics), Business, Political science, Corporate governance, Politics, Blockchain and Internet of Things Integration, Engineering (General). Civil engineering (General), 16. Peace & justice, Mechanical engineering, Management, FOS: Philosophy, ethics and religion, E-voting, Machine Learning for Internet Traffic Classification, Physical Sciences, TA1-2040, Peer to Peer, Art, Information Systems, Certification, FOS: Law, E-government, Visual arts, Transparency (behavior), Blockchain, Artificial Intelligence, Work (physics), Bureaucracy, BVC, Linguistics, Computer science, Philosophy, Computer Science, Political Science and International Relations, Security, FOS: Languages and literature, E-Government, Law, FOS: Civil engineering, Finance

Zugangs-URL: https://doaj.org/article/c32aa100e73946a8b6a0f9a27c77ee84

An Intelligent Intrusion Detection System for Smart Consumer Electronics Network

Autoren: Danish Javeed Muhammad Shahid Saeed Ijaz Ahmad et al.

Weitere Verfasser: Danish Javeed Muhammad Shahid Saeed Ijaz Ahmad et al.

Quelle: IEEE Transactions on Consumer Electronics. 69:906-913

Schlagwörter: Cyber-Attacks, Computer Networks and Communications, Internet of Things, Control reconfiguration, Consumer Electronics, Security in SDN, IoT Devices, Engineering, Artificial Intelligence, Computer security, Cloud computing, Embedded system, SDN Controllers, Intrusion Detection System, Computer network, Intrusion detection system, Home automation, Network architecture, Software-Defined Networking and Network Virtualization, Deep learning, Computer science, Software-Defined Networking, Distributed computing, Intrusion Detection, Operating system, Machine Learning for Internet Traffic Classification, Electrical engineering, Computer Science, Physical Sciences, Network Intrusion Detection and Defense Mechanisms, Electronics

Dateibeschreibung: fulltext

Zugangs-URL: https://lutpub.lut.fi/handle/10024/165659

Improving spam email classification accuracy using ensemble techniques: a stacking approach

Autoren: Muhammad Adnan Muhammad Osama Imam Mobin Javed et al.

Quelle: International Journal of Information Security. 23:505-517

Schlagwörter: FOS: Computer and information sciences, Review Spam, Artificial neural network, Artificial intelligence, Support vector machine, Computer Networks and Communications, Overfitting, 02 engineering and technology, Boosting (machine learning), Pattern recognition (psychology), Detection and Prevention of Phishing Attacks, Nuclear magnetic resonance, Artificial Intelligence, Ensemble learning, Machine learning, 0202 electrical engineering, electronic engineering, information engineering, Decision tree, Data mining, Precision and recall, Naive Bayes classifier, Physics, AdaBoost, Statistical classification, Computer science, Intrusion Detection, Spam Detection, Stacking, Machine Learning for Internet Traffic Classification, Computer Science, Physical Sciences, Bot Detection, Network Intrusion Detection and Defense Mechanisms, Botnet Detection, Classifier (UML), Information Systems, Random forest

Zugangs-URL: https://hdl.handle.net/10037/31848

An Intelligent Framework Based on Deep Learning for SMS and e-mail Spam Detection

Autoren: Umair Maqsood Saif Ur Rehman Tariq Ali et al.

Quelle: Applied Computational Intelligence and Soft Computing, Vol 2023 (2023)

Schlagwörter: FOS: Computer and information sciences, Review Spam, Artificial intelligence, Support vector machine, Computer Networks and Communications, 02 engineering and technology, Phishing, Malware, Detection and Prevention of Phishing Attacks, Artificial Intelligence, Computer security, Machine learning, 0202 electrical engineering, electronic engineering, information engineering, Phone, Short Message Service, Linguistics, QA75.5-76.95, Computer science, FOS: Philosophy, ethics and religion, World Wide Web, Operating system, Philosophy, Spam Detection, Machine Learning for Internet Traffic Classification, Electronic computers. Computer science, Computer Science, Physical Sciences, Bot Detection, Network Intrusion Detection and Defense Mechanisms, FOS: Languages and literature, Botnet Detection, Classifier (UML), Software, The Internet, Information Systems

Dateibeschreibung: text/xhtml

Zugangs-URL: https://doaj.org/article/eb43b3bf844447c9a4bc339b0594ce25

A secure user authentication scheme for crypto-wallet in Iot environment

Autoren: Toan-Thinh Truong Minh–Triet Tran Anh-Duc Duong

Quelle: Peer-to-Peer Networking and Applications. 17:3025-3048

Schlagwörter: FOS: Computer and information sciences, Database transaction, Mutual authentication, Authentication, Digital Image Watermarking Techniques, User Authentication Methods and Security Measures, Internet of Things, Computer science, Mathematical analysis, 7. Clean energy, Programming language, IoT Devices, Image Authentication, Machine Learning for Internet Traffic Classification, Artificial Intelligence, Computer security, Authentication (law), Computer Science, Physical Sciences, Cryptography, FOS: Mathematics, Computer Vision and Pattern Recognition, Mathematics, Information Systems, Scheme (mathematics)

Cloud-Based Intrusion Detection Approach Using Machine Learning Techniques

Autoren: Hanaa Attou Azidine Guezzaz Said Benkirane et al.

Quelle: Big Data Mining and Analytics, Vol 6, Iss 3, Pp 311-320 (2023)

Schlagwörter: features engineering, Artificial intelligence, Computer Networks and Communications, Characterization and Detection of Android Malware, Artificial Intelligence, Machine learning, Cloud computing, Data mining, Intrusion detection system, QA75.5-76.95, 16. Peace & justice, Computer science, cloud security, anomaly detection, Intrusion Detection, Detection, Operating system, Machine Learning for Internet Traffic Classification, Electronic computers. Computer science, Computer Science, Physical Sciences, Signal Processing, Network Intrusion Detection and Defense Mechanisms, Anomaly Detection, Classifier (UML), random forest, Random forest

Zugangs-URL: https://doaj.org/article/831bac8cf73e4f2f855189504d29f54d

Effective network intrusion detection using stacking-based ensemble approach

Autoren: Muhammad Ali Mansoor-ul- Haque Muhammad Hanif Durad et al.

Quelle: International Journal of Information Security. 22:1781-1798

Schlagwörter: Artificial intelligence, Computer Networks and Communications, Network simulation, 02 engineering and technology, Anomaly detection, 7. Clean energy, Characterization and Detection of Android Malware, Intranet, Artificial Intelligence, Computer security, Ensemble learning, Machine learning, 0202 electrical engineering, electronic engineering, information engineering, Key (lock), Data mining, Computer network, Network topology, Intrusion detection system, Network security, Computer science, Intrusion Detection, 3. Good health, Network Security, Detection, Operating system, Machine Learning for Internet Traffic Classification, Computer Science, Physical Sciences, Signal Processing, Network Intrusion Detection and Defense Mechanisms, Anomaly Detection, Botnet Detection, The Internet