Artículos de revistas sobre el tema "IOT BOTNET DETECTION"
Crea una cita precisa en los estilos APA, MLA, Chicago, Harvard y otros
Consulte los 50 mejores artículos de revistas para su investigación sobre el tema "IOT BOTNET DETECTION".
Junto a cada fuente en la lista de referencias hay un botón "Agregar a la bibliografía". Pulsa este botón, y generaremos automáticamente la referencia bibliográfica para la obra elegida en el estilo de cita que necesites: APA, MLA, Harvard, Vancouver, Chicago, etc.
También puede descargar el texto completo de la publicación académica en formato pdf y leer en línea su resumen siempre que esté disponible en los metadatos.
Explore artículos de revistas sobre una amplia variedad de disciplinas y organice su bibliografía correctamente.
Sreeja, B. P. "Survey on Internet of Things Botnet Detection Methodologies: A Report". IRO Journal on Sustainable Wireless Systems 4, n.º 3 (15 de septiembre de 2022): 185–95. http://dx.doi.org/10.36548/jsws.2022.3.005.
Texto completoWazzan, Majda, Daniyal Algazzawi, Omaima Bamasaq, Aiiad Albeshri y Li Cheng. "Internet of Things Botnet Detection Approaches: Analysis and Recommendations for Future Research". Applied Sciences 11, n.º 12 (20 de junio de 2021): 5713. http://dx.doi.org/10.3390/app11125713.
Texto completoYang, Changjin, Weili Guan y Zhijie Fang. "IoT Botnet Attack Detection Model Based on DBO-Catboost". Applied Sciences 13, n.º 12 (15 de junio de 2023): 7169. http://dx.doi.org/10.3390/app13127169.
Texto completoJovanović, Đorđe y Pavle Vuletić. "Analysis and characterization of IoT malware command and control communication". Telfor Journal 12, n.º 2 (2020): 80–85. http://dx.doi.org/10.5937/telfor2002080j.
Texto completoWazzan, Majda, Daniyal Algazzawi, Aiiad Albeshri, Syed Hasan, Osama Rabie y Muhammad Zubair Asghar. "Cross Deep Learning Method for Effectively Detecting the Propagation of IoT Botnet". Sensors 22, n.º 10 (20 de mayo de 2022): 3895. http://dx.doi.org/10.3390/s22103895.
Texto completoNegera, Worku Gachena, Friedhelm Schwenker, Taye Girma Debelee, Henock Mulugeta Melaku y Yehualashet Megeresa Ayano. "Review of Botnet Attack Detection in SDN-Enabled IoT Using Machine Learning". Sensors 22, n.º 24 (14 de diciembre de 2022): 9837. http://dx.doi.org/10.3390/s22249837.
Texto completoHaq, Mohd Anul. "DBoTPM: A Deep Neural Network-Based Botnet Prediction Model". Electronics 12, n.º 5 (27 de febrero de 2023): 1159. http://dx.doi.org/10.3390/electronics12051159.
Texto completoAkash, Nazmus Sakib, Shakir Rouf, Sigma Jahan, Amlan Chowdhury y Jia Uddin. "Botnet Detection in IoT Devices Using Random Forest Classifier with Independent Component Analysis". Journal of Information and Communication Technology 21, No.2 (7 de abril de 2022): 201–32. http://dx.doi.org/10.32890/jict2022.21.2.3.
Texto completoAl-Duwairi, Basheer, Wafaa Al-Kahla, Mhd Ammar AlRefai, Yazid Abedalqader, Abdullah Rawash y Rana Fahmawi. "SIEM-based detection and mitigation of IoT-botnet DDoS attacks". International Journal of Electrical and Computer Engineering (IJECE) 10, n.º 2 (1 de abril de 2020): 2182. http://dx.doi.org/10.11591/ijece.v10i2.pp2182-2191.
Texto completoAlharbi, Abdullah, Wael Alosaimi, Hashem Alyami, Hafiz Tayyab Rauf y Robertas Damaševičius. "Botnet Attack Detection Using Local Global Best Bat Algorithm for Industrial Internet of Things". Electronics 10, n.º 11 (3 de junio de 2021): 1341. http://dx.doi.org/10.3390/electronics10111341.
Texto completoKaushik, Dr Priyanka. "Unleashing the Power of Multi-Agent Deep Learning: Cyber-Attack Detection in IoT". International Journal for Global Academic & Scientific Research 2, n.º 2 (30 de junio de 2023): 23–45. http://dx.doi.org/10.55938/ijgasr.v2i2.46.
Texto completoRezaei, Amirhossein. "Identifying Botnet on IoT by Using Supervised Learning Techniques". Oriental journal of computer science and technology 12, n.º 4 (28 de octubre de 2019): 185–93. http://dx.doi.org/10.13005/ojcst12.04.04.
Texto completoAbu Al-Haija, Qasem y Mu’awya Al-Dala’ien. "ELBA-IoT: An Ensemble Learning Model for Botnet Attack Detection in IoT Networks". Journal of Sensor and Actuator Networks 11, n.º 1 (9 de marzo de 2022): 18. http://dx.doi.org/10.3390/jsan11010018.
Texto completoAlmseidin, Mohammad y Mouhammd Alkasassbeh. "An Accurate Detection Approach for IoT Botnet Attacks Using Interpolation Reasoning Method". Information 13, n.º 6 (14 de junio de 2022): 300. http://dx.doi.org/10.3390/info13060300.
Texto completoBagui, Sikha, Xiaojian Wang y Subhash Bagui. "Machine Learning Based Intrusion Detection for IoT Botnet". International Journal of Machine Learning and Computing 11, n.º 6 (noviembre de 2021): 399–406. http://dx.doi.org/10.18178/ijmlc.2021.11.6.1068.
Texto completoS. Alrayes, Fatma, Mohammed Maray, Abdulbaset Gaddah, Ayman Yafoz, Raed Alsini, Omar Alghushairy, Heba Mohsen y Abdelwahed Motwakel. "Modeling of Botnet Detection Using Barnacles Mating Optimizer with Machine Learning Model for Internet of Things Environment". Electronics 11, n.º 20 (21 de octubre de 2022): 3411. http://dx.doi.org/10.3390/electronics11203411.
Texto completoAlqahtani, Mnahi, Hassan Mathkour y Mohamed Maher Ben Ismail. "IoT Botnet Attack Detection Based on Optimized Extreme Gradient Boosting and Feature Selection". Sensors 20, n.º 21 (6 de noviembre de 2020): 6336. http://dx.doi.org/10.3390/s20216336.
Texto completoAlkahtani, Hasan y Theyazn H. H. Aldhyani. "Botnet Attack Detection by Using CNN-LSTM Model for Internet of Things Applications". Security and Communication Networks 2021 (9 de septiembre de 2021): 1–23. http://dx.doi.org/10.1155/2021/3806459.
Texto completoSoe, Yan Naung, Yaokai Feng, Paulus Insap Santosa, Rudy Hartanto y 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 completoNafir, Abdenacer, Smaine Mazouzi y Salim Chikhi. "Collaborative Life-Cycle-Based Botnet Detection in IoT Using Event Entropy". International Journal of Organizational and Collective Intelligence 10, n.º 4 (octubre de 2020): 19–34. http://dx.doi.org/10.4018/ijoci.2020100102.
Texto completoSajjad, Syed Muhammad, Muhammad Rafiq Mufti, Muhammad Yousaf, Waqar Aslam, Reem Alshahrani, Nadhem Nemri, Humaira Afzal, Muhammad Asghar Khan y Chien-Ming Chen. "Detection and Blockchain-Based Collaborative Mitigation of Internet of Things Botnets". Wireless Communications and Mobile Computing 2022 (20 de abril de 2022): 1–26. http://dx.doi.org/10.1155/2022/1194899.
Texto completoAlissa, Khalid, Tahir Alyas, Kashif Zafar, Qaiser Abbas, Nadia Tabassum y Shadman Sakib. "Botnet Attack Detection in IoT Using Machine Learning". Computational Intelligence and Neuroscience 2022 (4 de octubre de 2022): 1–14. http://dx.doi.org/10.1155/2022/4515642.
Texto completoAfrifa, Stephen, Vijayakumar Varadarajan, Peter Appiahene, Tao Zhang y Emmanuel Adjei Domfeh. "Ensemble Machine Learning Techniques for Accurate and Efficient Detection of Botnet Attacks in Connected Computers". Eng 4, n.º 1 (16 de febrero de 2023): 650–64. http://dx.doi.org/10.3390/eng4010039.
Texto completoHussain, Zeeshan, Adnan Akhunzada, Javed Iqbal, Iram Bibi y Abdullah Gani. "Secure IIoT-Enabled Industry 4.0". Sustainability 13, n.º 22 (10 de noviembre de 2021): 12384. http://dx.doi.org/10.3390/su132212384.
Texto completoM. Ali Alheeti, Khattab, Ibrahim Alsukayti y Mohammed Alreshoodi. "Intelligent Botnet Detection Approach in Modern Applications". International Journal of Interactive Mobile Technologies (iJIM) 15, n.º 16 (23 de agosto de 2021): 113. http://dx.doi.org/10.3991/ijim.v15i16.24199.
Texto completoAl-Sarem, Mohammed, Faisal Saeed, Eman H. Alkhammash y Norah Saleh Alghamdi. "An Aggregated Mutual Information Based Feature Selection with Machine Learning Methods for Enhancing IoT Botnet Attack Detection". Sensors 22, n.º 1 (28 de diciembre de 2021): 185. http://dx.doi.org/10.3390/s22010185.
Texto completoShao, Zhou, Sha Yuan y Yongli Wang. "Adaptive online learning for IoT botnet detection". Information Sciences 574 (octubre de 2021): 84–95. http://dx.doi.org/10.1016/j.ins.2021.05.076.
Texto completoJung, Woosub, Hongyang Zhao, Minglong Sun y Gang Zhou. "IoT botnet detection via power consumption modeling". Smart Health 15 (marzo de 2020): 100103. http://dx.doi.org/10.1016/j.smhl.2019.100103.
Texto completoTatarnikova, T. M., I. A. Sikarev, P. Yu Bogdanov y T. V. Timochkina. "Botnet Attack Detection Approach in IoT Networks". Automatic Control and Computer Sciences 56, n.º 8 (diciembre de 2022): 838–46. http://dx.doi.org/10.3103/s0146411622080259.
Texto completoKim, Jiyeon, Minsun Shim, Seungah Hong, Yulim Shin y Eunjung Choi. "Intelligent Detection of IoT Botnets Using Machine Learning and Deep Learning". Applied Sciences 10, n.º 19 (8 de octubre de 2020): 7009. http://dx.doi.org/10.3390/app10197009.
Texto completoApostol, Ioana, Marius Preda, Constantin Nila y Ion Bica. "IoT Botnet Anomaly Detection Using Unsupervised Deep Learning". Electronics 10, n.º 16 (4 de agosto de 2021): 1876. http://dx.doi.org/10.3390/electronics10161876.
Texto completoLee, Seungjin, Azween Abdullah, Nz Jhanjhi y Sh Kok. "Classification of botnet attacks in IoT smart factory using honeypot combined with machine learning". PeerJ Computer Science 7 (25 de enero de 2021): e350. http://dx.doi.org/10.7717/peerj-cs.350.
Texto completoMalik, Kainat, Faisal Rehman, Tahir Maqsood, Saad Mustafa, Osman Khalid y Adnan Akhunzada. "Lightweight Internet of Things Botnet Detection Using One-Class Classification". Sensors 22, n.º 10 (10 de mayo de 2022): 3646. http://dx.doi.org/10.3390/s22103646.
Texto completoAlothman, Zainab, Mouhammd Alkasassbeh y Sherenaz Al-Haj Baddar. "An efficient approach to detect IoT botnet attacks using machine learning". Journal of High Speed Networks 26, n.º 3 (27 de noviembre de 2020): 241–54. http://dx.doi.org/10.3233/jhs-200641.
Texto completoSwathi, G. Chandana, G. Kishor Kumar y A. P. Siva Kumar. "Central Pivot Heuristics for Botnet Attack Defense in Iot". International Journal on Recent and Innovation Trends in Computing and Communication 10, n.º 10 (31 de octubre de 2022): 78–90. http://dx.doi.org/10.17762/ijritcc.v10i10.5738.
Texto completoLee, Seungjin, Azween Abdullah, N. Z. Jhanjhi y S. H. Kok. "Honeypot Coupled Machine Learning Model for Botnet Detection and Classification in IoT Smart Factory – An Investigation". MATEC Web of Conferences 335 (2021): 04003. http://dx.doi.org/10.1051/matecconf/202133504003.
Texto completoAlzahrani, Rami J. y Ahmed Alzahrani. "A Novel Multi Algorithm Approach to Identify Network Anomalies in the IoT Using Fog Computing and a Model to Distinguish between IoT and Non-IoT Devices". Journal of Sensor and Actuator Networks 12, n.º 2 (28 de febrero de 2023): 19. http://dx.doi.org/10.3390/jsan12020019.
Texto completoAl-Kasassbeh, Mouhammd, Mohammad Almseidin, Khaled Alrfou y Szilveszter Kovacs. "Detection of IoT-botnet attacks using fuzzy rule interpolation". Journal of Intelligent & Fuzzy Systems 39, n.º 1 (17 de julio de 2020): 421–31. http://dx.doi.org/10.3233/jifs-191432.
Texto completoNguyen, Giang L., Braulio Dumba, Quoc-Dung Ngo, Hai-Viet Le y Tu N. Nguyen. "A collaborative approach to early detection of IoT Botnet". Computers & Electrical Engineering 97 (enero de 2022): 107525. http://dx.doi.org/10.1016/j.compeleceng.2021.107525.
Texto completoNguyen, Huy-Trung, Quoc-Dung Ngo y Van-Hoang Le. "A novel graph-based approach for IoT botnet detection". International Journal of Information Security 19, n.º 5 (23 de octubre de 2019): 567–77. http://dx.doi.org/10.1007/s10207-019-00475-6.
Texto completoAbu Khurma, Ruba, Iman Almomani y Ibrahim Aljarah. "IoT Botnet Detection Using Salp Swarm and Ant Lion Hybrid Optimization Model". Symmetry 13, n.º 8 (28 de julio de 2021): 1377. http://dx.doi.org/10.3390/sym13081377.
Texto completode Caldas Filho, Francisco Lopes, Samuel Carlos Meneses Soares, Elder Oroski, Robson de Oliveira Albuquerque, Rafael Zerbini Alves da Mata, Fábio Lúcio Lopes de Mendonça y Rafael Timóteo de Sousa Júnior. "Botnet Detection and Mitigation Model for IoT Networks Using Federated Learning". Sensors 23, n.º 14 (11 de julio de 2023): 6305. http://dx.doi.org/10.3390/s23146305.
Texto completoCatillo, Marta, Antonio Pecchia y Umberto Villano. "A Deep Learning Method for Lightweight and Cross-Device IoT Botnet Detection". Applied Sciences 13, n.º 2 (7 de enero de 2023): 837. http://dx.doi.org/10.3390/app13020837.
Texto completoFaysal, Jabed Al, Sk Tahmid Mostafa, Jannatul Sultana Tamanna, Khondoker Mirazul Mumenin, Md Mashrur Arifin, Md Abdul Awal, Atanu Shome y Sheikh Shanawaz Mostafa. "XGB-RF: A Hybrid Machine Learning Approach for IoT Intrusion Detection". Telecom 3, n.º 1 (4 de enero de 2022): 52–69. http://dx.doi.org/10.3390/telecom3010003.
Texto completoMyridakis, Dimitrios, Stefanos Papafotikas, Konstantinos Kalovrektis y Athanasios Kakarountas. "Enhancing Security on IoT Devices via Machine Learning on Conditional Power Dissipation". Electronics 9, n.º 11 (29 de octubre de 2020): 1799. http://dx.doi.org/10.3390/electronics9111799.
Texto completoAL-Akhras, Mousa, Abdulmajeed Alshunaybir, Hani Omar y Samah Alhazmi. "Botnet attacks detection in IoT environment using machine learning techniques". International Journal of Data and Network Science 7, n.º 4 (2023): 1683–706. http://dx.doi.org/10.5267/j.ijdns.2023.7.021.
Texto completoKerrakchou, Imane, Adil Abou El Hassan, Sara Chadli, Mohamed Emharraf y Mohammed Saber. "Selection of efficient machine learning algorithm on Bot-IoT dataset for intrusion detection in internet of things networks". Indonesian Journal of Electrical Engineering and Computer Science 31, n.º 3 (1 de septiembre de 2023): 1784. http://dx.doi.org/10.11591/ijeecs.v31.i3.pp1784-1793.
Texto completoTrajanovski, Tolijan y Ning Zhang. "An Automated and Comprehensive Framework for IoT Botnet Detection and Analysis (IoT-BDA)". IEEE Access 9 (2021): 124360–83. http://dx.doi.org/10.1109/access.2021.3110188.
Texto completoPopoola, Segun I., Bamidele Adebisi, Ruth Ande, Mohammad Hammoudeh, Kelvin Anoh y Aderemi A. Atayero. "SMOTE-DRNN: A Deep Learning Algorithm for Botnet Detection in the Internet-of-Things Networks". Sensors 21, n.º 9 (24 de abril de 2021): 2985. http://dx.doi.org/10.3390/s21092985.
Texto completoNegera, Worku Gachena, Friedhelm Schwenker, Taye Girma Debelee, Henock Mulugeta Melaku y Degaga Wolde Feyisa. "Lightweight Model for Botnet Attack Detection in Software Defined Network-Orchestrated IoT". Applied Sciences 13, n.º 8 (7 de abril de 2023): 4699. http://dx.doi.org/10.3390/app13084699.
Texto completo