Artigos de revistas sobre o tema "Attacks detection"
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BALIGA, SANDEEP, ETHAN BUENO DE MESQUITA e ALEXANDER WOLITZKY. "Deterrence with Imperfect Attribution". American Political Science Review 114, n.º 4 (3 de agosto de 2020): 1155–78. http://dx.doi.org/10.1017/s0003055420000362.
Texto completo da fonteKareem, Mohammed Ibrahim, Mohammad Jawad Kadhim Abood e Karrar Ibrahim. "Machine learning-based PortScan attacks detection using OneR classifier". Bulletin of Electrical Engineering and Informatics 12, n.º 6 (1 de dezembro de 2023): 3690–96. http://dx.doi.org/10.11591/eei.v12i6.4142.
Texto completo da fonteO, Belej, Spas N, Artyshchuk I e Fedastsou M. "Construction of a multi-agent attack detection system based on artificial intelligence models". Artificial Intelligence 26, jai2021.26(1) (30 de junho de 2021): 22–30. http://dx.doi.org/10.15407/jai2021.01.022.
Texto completo da fonteSambangi, Swathi, e Lakshmeeswari Gondi. "A Machine Learning Approach for DDoS (Distributed Denial of Service) Attack Detection Using Multiple Linear Regression". Proceedings 63, n.º 1 (25 de dezembro de 2020): 51. http://dx.doi.org/10.3390/proceedings2020063051.
Texto completo da fonteXuan, Cho Do, Duc Duong e Hoang Xuan Dau. "A multi-layer approach for advanced persistent threat detection using machine learning based on network traffic". Journal of Intelligent & Fuzzy Systems 40, n.º 6 (21 de junho de 2021): 11311–29. http://dx.doi.org/10.3233/jifs-202465.
Texto completo da fonteHaseeb-ur-rehman, Rana M. Abdul, Azana Hafizah Mohd Aman, Mohammad Kamrul Hasan, Khairul Akram Zainol Ariffin, Abdallah Namoun, Ali Tufail e Ki-Hyung Kim. "High-Speed Network DDoS Attack Detection: A Survey". Sensors 23, n.º 15 (1 de agosto de 2023): 6850. http://dx.doi.org/10.3390/s23156850.
Texto completo da fonteZhou, Qing Lei, Yan Ke Zhao e Wei Jun Zhu. "Intrusion Detection for Universal Attack Mode Based on Projection Temporal Logic". Applied Mechanics and Materials 556-562 (maio de 2014): 2821–24. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.2821.
Texto completo da fonteSravanthi, P. "Machine Learning Methods for Attack Detection in Smart Grid". International Journal for Research in Applied Science and Engineering Technology 12, n.º 3 (31 de março de 2024): 2257–61. http://dx.doi.org/10.22214/ijraset.2024.59222.
Texto completo da fonteGupta, Punit, e Pallavi Kaliyar. "History Aware Anomaly Based IDS for Cloud IaaS". INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 10, n.º 6 (30 de agosto de 2013): 1779–84. http://dx.doi.org/10.24297/ijct.v10i6.3205.
Texto completo da fonteQiao, Peng Zhe, Yi Ran Wang e Yan Ke Zhao. "Intrusion Detection for Universal Attack Mode Based on Linear Temporal Logic with Past Construct". Applied Mechanics and Materials 680 (outubro de 2014): 433–36. http://dx.doi.org/10.4028/www.scientific.net/amm.680.433.
Texto completo da fonteLi, Yong Liang, Wei Jun Zhu e Qing Lei Zhou. "Intrusion Detection for Universal Attack Mode Based on Interval Temporal Logic with Past Construct". Advanced Materials Research 1006-1007 (agosto de 2014): 1047–50. http://dx.doi.org/10.4028/www.scientific.net/amr.1006-1007.1047.
Texto completo da fonteSachdev, Rithik, Shreya Mishra e Shekhar Sharma. "Comparison of Supervised Learning Algorithms for DDOS Attack Detection". International Journal for Research in Applied Science and Engineering Technology 10, n.º 8 (31 de agosto de 2022): 1766–72. http://dx.doi.org/10.22214/ijraset.2022.46506.
Texto completo da fonteZaini, Nur Sholihah, Deris Stiawan, Mohd Faizal Ab Razak, Ahmad Firdaus, Wan Isni Sofiah Wan Din, Shahreen Kasim e Tole Sutikno. "Phishing detection system using nachine learning classifiers". Indonesian Journal of Electrical Engineering and Computer Science 17, n.º 3 (1 de março de 2020): 1165. http://dx.doi.org/10.11591/ijeecs.v17.i3.pp1165-1171.
Texto completo da fonteDeng, Wenping, Ziyu Yang, Peng Xun, Peidong Zhu e Baosheng Wang. "Advanced Bad Data Injection Attack and Its Migration in Cyber-Physical Systems". Electronics 8, n.º 9 (26 de agosto de 2019): 941. http://dx.doi.org/10.3390/electronics8090941.
Texto completo da fonteShang, Fute, Buhong Wang, Fuhu Yan e Tengyao Li. "Multidevice False Data Injection Attack Models of ADS-B Multilateration Systems". Security and Communication Networks 2019 (3 de março de 2019): 1–11. http://dx.doi.org/10.1155/2019/8936784.
Texto completo da fonteJaiganesh, M., G. ShivajiRao, P. Dhivya, M. Udhayamoorthi e A. Vincent Antony Kumar. "Intrusion Optimal Path Attack detection using ACO for Cloud Computing". E3S Web of Conferences 472 (2024): 02009. http://dx.doi.org/10.1051/e3sconf/202447202009.
Texto completo da fonteKumavat, Kavita S., e Joanne Gomes. "Common Mechanism for Detecting Multiple DDoS Attacks". International Journal on Recent and Innovation Trends in Computing and Communication 11, n.º 4 (4 de maio de 2023): 81–90. http://dx.doi.org/10.17762/ijritcc.v11i4.6390.
Texto completo da fonteLi, Feng, e Hai Ying Wang. "Design on DDoS Attack Detection and Prevention Systems". Applied Mechanics and Materials 530-531 (fevereiro de 2014): 798–801. http://dx.doi.org/10.4028/www.scientific.net/amm.530-531.798.
Texto completo da fonteFarane Shradha, Gotane Rutuja, Chandanshive Sakshi, Agrawal Khushi e Khandekar Srushti. "Detection of cyber-attacks and network attacks using Machine Learning". World Journal of Advanced Engineering Technology and Sciences 12, n.º 1 (30 de maio de 2024): 128–32. http://dx.doi.org/10.30574/wjaets.2024.12.1.0184.
Texto completo da fonteMiller, David, Yujia Wang e George Kesidis. "When Not to Classify: Anomaly Detection of Attacks (ADA) on DNN Classifiers at Test Time". Neural Computation 31, n.º 8 (agosto de 2019): 1624–70. http://dx.doi.org/10.1162/neco_a_01209.
Texto completo da fonteHsieh, Chih-Hsiang, Wei-Kuan Wang, Cheng-Xun Wang, Shi-Chun Tsai e Yi-Bing Lin. "Efficient Detection of Link-Flooding Attacks with Deep Learning". Sustainability 13, n.º 22 (12 de novembro de 2021): 12514. http://dx.doi.org/10.3390/su132212514.
Texto completo da fonteAridoss, Manimaran. "Defensive Mechanism Against DDoS Attack to Preserve Resource Availability for IoT Applications". International Journal of Handheld Computing Research 8, n.º 4 (outubro de 2017): 40–51. http://dx.doi.org/10.4018/ijhcr.2017100104.
Texto completo da fonteGhugar, Umashankar, Jayaram Pradhan, Sourav Kumar Bhoi e Rashmi Ranjan Sahoo. "LB-IDS: Securing Wireless Sensor Network Using Protocol Layer Trust-Based Intrusion Detection System". Journal of Computer Networks and Communications 2019 (6 de janeiro de 2019): 1–13. http://dx.doi.org/10.1155/2019/2054298.
Texto completo da fonteGara, Fatma, Leila Ben Saad e Rahma Ben Ayed. "An Efficient Intrusion Detection System for Selective Forwarding and Clone Attackers in IPv6-based Wireless Sensor Networks under Mobility". International Journal on Semantic Web and Information Systems 13, n.º 3 (julho de 2017): 22–47. http://dx.doi.org/10.4018/ijswis.2017070102.
Texto completo da fonteDu, Dajun, Rui Chen, Xue Li, Lei Wu, Peng Zhou e Minrui Fei. "Malicious data deception attacks against power systems: A new case and its detection method". Transactions of the Institute of Measurement and Control 41, n.º 6 (8 de janeiro de 2018): 1590–99. http://dx.doi.org/10.1177/0142331217740622.
Texto completo da fonteShchetinin, Eugeny Yu, e Tatyana R. Velieva. "Detection of cyber-attacks on the power smart grids using semi-supervised deep learning models". Discrete and Continuous Models and Applied Computational Science 30, n.º 3 (5 de outubro de 2022): 258–68. http://dx.doi.org/10.22363/2658-4670-2022-30-3-258-268.
Texto completo da fonteWang, Jing Lei. "Research on the Detection Method of the Malicious Attacks on Campus Network". Applied Mechanics and Materials 644-650 (setembro de 2014): 3291–94. http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.3291.
Texto completo da fonteAslan, Ömer, Semih Serkant Aktuğ, Merve Ozkan-Okay, Abdullah Asim Yilmaz e Erdal Akin. "A Comprehensive Review of Cyber Security Vulnerabilities, Threats, Attacks, and Solutions". Electronics 12, n.º 6 (11 de março de 2023): 1333. http://dx.doi.org/10.3390/electronics12061333.
Texto completo da fonteLiu, Bo, Hongyu Wu, Qihui Yang e Hang Zhang. "Random-Enabled Hidden Moving Target Defense against False Data Injection Alert Attackers". Processes 11, n.º 2 (21 de janeiro de 2023): 348. http://dx.doi.org/10.3390/pr11020348.
Texto completo da fonteD., Glăvan. "DDoS detection and prevention based on artificial intelligence techniques". Scientific Bulletin of Naval Academy XXII, n.º 1 (15 de julho de 2019): 134–43. http://dx.doi.org/10.21279/1454-864x-19-i1-018.
Texto completo da fonteSoe, Yan Naung, Yaokai Feng, Paulus Insap Santosa, Rudy Hartanto e Kouichi Sakurai. "Machine Learning-Based IoT-Botnet Attack Detection with Sequential Architecture". Sensors 20, n.º 16 (5 de agosto de 2020): 4372. http://dx.doi.org/10.3390/s20164372.
Texto completo da fonteFadlil, Abdul, Imam Riadi e Sukma Aji. "Review of Detection DDOS Attack Detection Using Naive Bayes Classifier for Network Forensics". Bulletin of Electrical Engineering and Informatics 6, n.º 2 (1 de junho de 2017): 140–48. http://dx.doi.org/10.11591/eei.v6i2.605.
Texto completo da fonteWatson, Lauren, Anupam Mediratta, Tariq Elahi e Rik Sarkar. "Privacy Preserving Detection of Path Bias Attacks in Tor". Proceedings on Privacy Enhancing Technologies 2020, n.º 4 (1 de outubro de 2020): 111–30. http://dx.doi.org/10.2478/popets-2020-0065.
Texto completo da fonteHairab, Belal Ibrahim, Heba K. Aslan, Mahmoud Said Elsayed, Anca D. Jurcut e Marianne A. Azer. "Anomaly Detection of Zero-Day Attacks Based on CNN and Regularization Techniques". Electronics 12, n.º 3 (23 de janeiro de 2023): 573. http://dx.doi.org/10.3390/electronics12030573.
Texto completo da fonteXia, Kui Liang. "Modeling and Simulation of Low Rate of Denial of Service Attacks". Applied Mechanics and Materials 484-485 (janeiro de 2014): 1063–66. http://dx.doi.org/10.4028/www.scientific.net/amm.484-485.1063.
Texto completo da fonteAlansari, Zainab, Nor Badrul Anuar, Amirrudin Kamsin e Mohammad Riyaz Belgaum. "A systematic review of routing attacks detection in wireless sensor networks". PeerJ Computer Science 8 (21 de outubro de 2022): e1135. http://dx.doi.org/10.7717/peerj-cs.1135.
Texto completo da fonteHan, Dezhi, Kun Bi, Han Liu e Jianxin Jia. "A DDoS attack detection system based on spark framework". Computer Science and Information Systems 14, n.º 3 (2017): 769–88. http://dx.doi.org/10.2298/csis161217028h.
Texto completo da fonteWu, Kongpei, Huiqin Qu e Conggui Huang. "A Network Intrusion Detection Method Incorporating Bayesian Attack Graph and Incremental Learning Part". Future Internet 15, n.º 4 (28 de março de 2023): 128. http://dx.doi.org/10.3390/fi15040128.
Texto completo da fontedos Santos, Rodrigo, Ashwitha Kassetty e Shirin Nilizadeh. "Disrupting Audio Event Detection Deep Neural Networks with White Noise". Technologies 9, n.º 3 (6 de setembro de 2021): 64. http://dx.doi.org/10.3390/technologies9030064.
Texto completo da fonteGavrić, Nikola, e Živko Bojović. "Security Concerns in MMO Games—Analysis of a Potent Application Layer DDoS Threat". Sensors 22, n.º 20 (14 de outubro de 2022): 7791. http://dx.doi.org/10.3390/s22207791.
Texto completo da fonteLee, Kyungroul, Jaehyuk Lee e Kangbin Yim. "Classification and Analysis of Malicious Code Detection Techniques Based on the APT Attack". Applied Sciences 13, n.º 5 (23 de fevereiro de 2023): 2894. http://dx.doi.org/10.3390/app13052894.
Texto completo da fonteYu, Zhenhua, Xudong Duan, Xuya Cong, Xiangning Li e Li Zheng. "Detection of Actuator Enablement Attacks by Petri Nets in Supervisory Control Systems". Mathematics 11, n.º 4 (13 de fevereiro de 2023): 943. http://dx.doi.org/10.3390/math11040943.
Texto completo da fonteKasture, Pradnya. "DDoS Attack Detection using ML". International Journal for Research in Applied Science and Engineering Technology 11, n.º 5 (31 de maio de 2023): 6421–24. http://dx.doi.org/10.22214/ijraset.2023.53133.
Texto completo da fonteAlamsyah, Hendri, Riska e Abdussalam Al Akbar. "Analisa Keamanan Jaringan Menggunakan Network Intrusion Detection and Prevention System". JOINTECS (Journal of Information Technology and Computer Science) 5, n.º 1 (25 de janeiro de 2020): 17. http://dx.doi.org/10.31328/jointecs.v5i1.1240.
Texto completo da fonteChauhan, Ravi, Ulya Sabeel, Alireza Izaddoost e Shahram Shah Heydari. "Polymorphic Adversarial Cyberattacks Using WGAN". Journal of Cybersecurity and Privacy 1, n.º 4 (12 de dezembro de 2021): 767–92. http://dx.doi.org/10.3390/jcp1040037.
Texto completo da fonteJoshi, Sagar Vasantrao, Nanda Wagh, Jambi Ratna Raja Kumar, Deepika Dongre, Nuzhat Rizvi e Mahua Bhowmik. "Mitigating DDoS attacks with an intrusion detection and prevention system based on 2-player Bayesian game theory". Journal of Discrete Mathematical Sciences and Cryptography 27, n.º 2-B (2024): 809–20. http://dx.doi.org/10.47974/jdmsc-1957.
Texto completo da fonteSiddiqa, Ayesha. "Web Based Intrusion Detection System for SQLIA". INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, n.º 11 (1 de novembro de 2023): 1–11. http://dx.doi.org/10.55041/ijsrem26708.
Texto completo da fonteChaves, Cesar, Siavoosh Azad, Thomas Hollstein e Johanna Sepúlveda. "DoS Attack Detection and Path Collision Localization in NoC-Based MPSoC Architectures". Journal of Low Power Electronics and Applications 9, n.º 1 (5 de fevereiro de 2019): 7. http://dx.doi.org/10.3390/jlpea9010007.
Texto completo da fontePeterson, Matthew, Todd Andel e Ryan Benton. "Towards Detection of Selfish Mining Using Machine Learning". International Conference on Cyber Warfare and Security 17, n.º 1 (2 de março de 2022): 237–43. http://dx.doi.org/10.34190/iccws.17.1.15.
Texto completo da fonteChamotra, Saurabh, Rakesh Kumar Sehgal e Ram Swaroop Misra. "Honeypot Baselining for Zero Day Attack Detection". International Journal of Information Security and Privacy 11, n.º 3 (julho de 2017): 63–74. http://dx.doi.org/10.4018/ijisp.2017070106.
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