Suchergebnisse - "Network Intrusion Detection and Defense Mechanisms"

An Automated Threat Intelligence Framework for Vehicle–Road Cooperation Systems

Autoren: Prabhat Kumar Randhir Kumar Alireza Jolfaei et al.

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

Schlagwörter: 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

Autoren: Alaa Eddine Khalfoune Rachid Beghdad

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

Schlagwörter: 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

Zugangs-URL: https://doaj.org/article/75ddb5b014fa43ba9d1e92c3478364d2

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

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

Autoren: Chafika Benzaïd Tarik Taleb Ashkan Sami et al.

Quelle: IEEE Transactions on Dependable and Secure Computing

Schlagwörter: 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

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

A Comprehensive Review of Machine Learning Approaches for Detecting Malicious Software

Autoren: Liu Yuanming Rodziah Latih

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

Schlagwörter: 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

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

Autoren: Neil Bryan B. Booc Kenneth Budiongan Ramil Carballo

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

Schlagwörter: 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

Cumulative histogram as a feature selection technique for anomaly detection

Autoren: Mostafa Y. Nassar Fathi E. Abd El‐Samie Adel A. Saleeb et al.

Quelle: Multimedia Tools and Applications. 84:10095-10107

Schlagwörter: Artificial intelligence, Computer Networks and Communications, Feature (linguistics), Biomedical Engineering, 02 engineering and technology, Anomaly detection, FOS: Medical engineering, Pattern recognition (psychology), Anomaly Detection in High-Dimensional Data, Engineering, Selection (genetic algorithm), Artificial Intelligence, 0202 electrical engineering, electronic engineering, information engineering, Artificial Immune Systems in Science and Engineering, Image (mathematics), Data mining, Histogram, Physics, Linguistics, Condensed matter physics, Computer science, Anomaly (physics), FOS: Philosophy, ethics and religion, Philosophy, Computer Science, Physical Sciences, Feature selection, Network Intrusion Detection and Defense Mechanisms, FOS: Languages and literature

Advancing IoT Cybersecurity: Adaptive Threat Identification with Deep Learning in Cyber-Physical Systems

Autoren: C. Atheeq Ruhiat Sultana Syeda Asfiya Sabahath et al.

Quelle: Engineering, Technology & Applied Science Research. 14:13559-13566

Schlagwörter: Artificial neural network, Artificial intelligence, IoT Security, Computer Networks and Communications, Industrial control system, Internet of Things, Control (management), 02 engineering and technology, 7. Clean energy, Cyber-physical system, Anomaly Detection in High-Dimensional Data, Identification (biology), Engineering, Deep Learning, Artificial Intelligence, Computer security, Machine learning, Decision tree, 0202 electrical engineering, electronic engineering, information engineering, Biology, Cyber-Physical Systems, Botany, Deep learning, Security Challenges in Smart Grid Systems, 15. Life on land, Computer science, 6. Clean water, Intrusion Detection, Operating system, Control and Systems Engineering, Attack Detection, 13. Climate action, Physical Sciences, Computer Science, Network Intrusion Detection and Defense Mechanisms

MLSTL-WSN: machine learning-based intrusion detection using SMOTETomek in WSNs

Autoren: Md. Alamin Talukder Selina Sharmin Md. Ashraf Uddin et al.

Quelle: International Journal of Information Security. 23:2139-2158

Schlagwörter: Computer Science - Machine Learning, Wireless Home Automation Systems, Artificial intelligence, Computer Science - Cryptography and Security, Computer Networks and Communications, 02 engineering and technology, Engineering, Computer security, 0202 electrical engineering, electronic engineering, information engineering, FOS: Electrical engineering, electronic engineering, information engineering, Electrical and Electronic Engineering, Embedded system, Computer network, Sensors, Intrusion, Intrusion detection system, Geology, FOS: Earth and related environmental sciences, Security Challenges in Smart Grid Systems, Computer science, Intrusion Detection, Geochemistry, Control and Systems Engineering, Attack Detection, Computer Science, Physical Sciences, Network Intrusion Detection and Defense Mechanisms, Cryptography, Botnet Detection, Wireless sensor network

Zugangs-URL: http://arxiv.org/abs/2402.13277

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

Autoren: Nachiappan Subramanian Logesh Ravi Mithin Jain Shaan et al.

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

Schlagwörter: 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

Complex networks after centrality-based attacks and defense

Autoren: Maham Zafar Kashif Kifayat Arif Gul et al.

Quelle: Complex & Intelligent Systems, Vol 10, Iss 3, Pp 3907-3923 (2024)

Schlagwörter: 0301 basic medicine, Artificial intelligence, Computer Networks and Communications, Complex networks, Statistical Physics of Opinion Dynamics, Random graph, Information technology, 01 natural sciences, 7. Clean energy, Business intelligence, 03 medical and health sciences, Computer security, 0103 physical sciences, Centrality Measures, FOS: Mathematics, Centrality, Centrality-based-attack, Data mining, Computational intelligence, Centrality-based-defense, Betweenness centrality, Statistical and Nonlinear Physics, QA75.5-76.95, T58.5-58.64, Computer science, Intrusion Detection, Network Dynamics, Physics and Astronomy, Combinatorics, Electronic computers. Computer science, Physical Sciences, Computer Science, Network Intrusion Detection and Defense Mechanisms, Statistical Mechanics of Complex Networks, Bridging centrality, Botnet Detection, Network Analysis, Mathematics

Zugangs-URL: https://doaj.org/article/f84c410ec7214e9aba90f3c12114d24e

A Hybrid Deep Learning Approach for Spam Detection in Twitter

Autoren: Hemza Loucif

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

Schlagwörter: 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

Autoren: Prabhat Kumar Danish Javeed Randhir Kumar et al.

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

Schlagwörter: 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

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

Optimizing Misinformation Control: A Cloud-Enhanced Machine Learning Approach

Autoren: Muhammad Daniyal Baig Waseem Akram Hafiz Burhan Ul Haq et al.

Quelle: Information Dynamics and Applications. 3:1-11

Schlagwörter: FOS: Computer and information sciences, Artificial intelligence, Sociology and Political Science, Computer Networks and Communications, Social Sciences, The Spread of Misinformation Online, Control (management), 15. Life on land, 16. Peace & justice, Computer science, 7. Clean energy, Detection and Prevention of Phishing Attacks, Operating system, Computer security, Computer Science, Physical Sciences, 11. Sustainability, Network Intrusion Detection and Defense Mechanisms, Misinformation, Cloud computing, Information Systems

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

Autoren: Nayer Tumi Figueroa E Vishnu Priya A Selvanayaki Kolandapalayam Shanmugam et al.

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

Schlagwörter: 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

Intrusion Detection in Internet of Things Systems: A Feature Extraction with Naive Bayes Classifier Approach

Autoren: Juan Carlos Juárez Vargas Hayder M. A. Ghanimi S Sivaprakash et al.

Quelle: Journal of Machine and Computing. :21-30

Schlagwörter: Artificial intelligence, Outlier Detection, Support vector machine, IoT Security, Computer Networks and Communications, Novelty Detection, FOS: Mechanical engineering, Dissemination, 02 engineering and technology, 7. Clean energy, Anomaly Detection in High-Dimensional Data, Automation, Engineering, Artificial Intelligence, Machine learning, 11. Sustainability, 0202 electrical engineering, electronic engineering, information engineering, Data mining, Naive Bayes classifier, Intrusion detection system, Computer science, Mechanical engineering, Intrusion Detection, World Wide Web, Computer Science, Physical Sciences, Feature selection, Network Intrusion Detection and Defense Mechanisms, Telecommunications, Feature extraction, Computer Vision and Pattern Recognition, Botnet Detection, Classifier (UML), Face Recognition and Dimensionality Reduction Techniques, The Internet

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

Autoren: Youseef Alotaibi R. Deepa K. Shankar et al.

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

Schlagwörter: 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

Zugangs-URL: https://doaj.org/article/8f0a63c24ba649e1ba93f37b61d15e19

Intelligent computing knacks for infected media and time delay impacts on dynamical behaviors and control measures of rumor-spreading model

Autoren: Muhammad Asif Zahoor Raja Adeeba Haider Kottakkaran Sooppy Nisar et al.

Quelle: AIMS Biophysics, Vol 11, Iss 1, Pp 1-17 (2024)

Schlagwörter: Artificial neural network, 0301 basic medicine, Artificial intelligence, Public relations, QH301-705.5, Computer Networks and Communications, FOS: Political science, Statistical Physics of Opinion Dynamics, Bounded Confidence, Backpropagation, Dissemination, Rumor, 7. Clean energy, bayesian regularization method, 03 medical and health sciences, neural networks backpropagation, Artificial Intelligence, Machine learning, Regularization (linguistics), rumors spread model, Biology (General), Political science, 0303 health sciences, Statistical and Nonlinear Physics, Computer science, Physics and Astronomy, Physical Sciences, Computer Science, Network Intrusion Detection and Defense Mechanisms, Telecommunications, regression, Scientific Computing and Data Analysis with Python, TP248.13-248.65, Biotechnology

Zugangs-URL: https://doaj.org/article/93b578e19bc54eec8876e3221fdca2f9