Littérature scientifique sur le sujet « IOT BOTNET DETECTION »
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Articles de revues sur le sujet "IOT BOTNET DETECTION"
Sreeja, B. P. « Survey on Internet of Things Botnet Detection Methodologies : A Report ». IRO Journal on Sustainable Wireless Systems 4, no 3 (15 septembre 2022) : 185–95. http://dx.doi.org/10.36548/jsws.2022.3.005.
Texte intégralWazzan, Majda, Daniyal Algazzawi, Omaima Bamasaq, Aiiad Albeshri et Li Cheng. « Internet of Things Botnet Detection Approaches : Analysis and Recommendations for Future Research ». Applied Sciences 11, no 12 (20 juin 2021) : 5713. http://dx.doi.org/10.3390/app11125713.
Texte intégralYang, Changjin, Weili Guan et Zhijie Fang. « IoT Botnet Attack Detection Model Based on DBO-Catboost ». Applied Sciences 13, no 12 (15 juin 2023) : 7169. http://dx.doi.org/10.3390/app13127169.
Texte intégralJovanović, Đorđe, et Pavle Vuletić. « Analysis and characterization of IoT malware command and control communication ». Telfor Journal 12, no 2 (2020) : 80–85. http://dx.doi.org/10.5937/telfor2002080j.
Texte intégralWazzan, Majda, Daniyal Algazzawi, Aiiad Albeshri, Syed Hasan, Osama Rabie et Muhammad Zubair Asghar. « Cross Deep Learning Method for Effectively Detecting the Propagation of IoT Botnet ». Sensors 22, no 10 (20 mai 2022) : 3895. http://dx.doi.org/10.3390/s22103895.
Texte intégralNegera, Worku Gachena, Friedhelm Schwenker, Taye Girma Debelee, Henock Mulugeta Melaku et Yehualashet Megeresa Ayano. « Review of Botnet Attack Detection in SDN-Enabled IoT Using Machine Learning ». Sensors 22, no 24 (14 décembre 2022) : 9837. http://dx.doi.org/10.3390/s22249837.
Texte intégralHaq, Mohd Anul. « DBoTPM : A Deep Neural Network-Based Botnet Prediction Model ». Electronics 12, no 5 (27 février 2023) : 1159. http://dx.doi.org/10.3390/electronics12051159.
Texte intégralAkash, Nazmus Sakib, Shakir Rouf, Sigma Jahan, Amlan Chowdhury et 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 avril 2022) : 201–32. http://dx.doi.org/10.32890/jict2022.21.2.3.
Texte intégralAl-Duwairi, Basheer, Wafaa Al-Kahla, Mhd Ammar AlRefai, Yazid Abedalqader, Abdullah Rawash et Rana Fahmawi. « SIEM-based detection and mitigation of IoT-botnet DDoS attacks ». International Journal of Electrical and Computer Engineering (IJECE) 10, no 2 (1 avril 2020) : 2182. http://dx.doi.org/10.11591/ijece.v10i2.pp2182-2191.
Texte intégralAlharbi, Abdullah, Wael Alosaimi, Hashem Alyami, Hafiz Tayyab Rauf et Robertas Damaševičius. « Botnet Attack Detection Using Local Global Best Bat Algorithm for Industrial Internet of Things ». Electronics 10, no 11 (3 juin 2021) : 1341. http://dx.doi.org/10.3390/electronics10111341.
Texte intégralThèses sur le sujet "IOT BOTNET DETECTION"
KARAMVEER. « IOT BOTNET DETECTION ». Thesis, 2022. http://dspace.dtu.ac.in:8080/jspui/handle/repository/19143.
Texte intégralLin, Jheng-Fong, et 林正逢. « Application of Deep Learning in IoT Botnet Detection ». Thesis, 2019. http://ndltd.ncl.edu.tw/handle/ab2xv7.
Texte intégral崑山科技大學
資訊管理研究所
107
With the development of Internet of Things (IoT), IoT devices are rapidly expanding at an unprecedented rate. Smart home appliances, home security devices and wireless network were intensely integrated via a centralized control device (Coordinator) to provide remote control by smart phone and a more convenient and smarter life.IoT has become the main attack target of hackers with the massive deployment of IoT devices and the proliferation of automated attack tools. To increase the detection accuracy of IoT-based botnet and reduce the false positive rate, this research proposes a botnet detection and protection system usingdeep learning Bidirectional Long Short-Term Memory(BLSTM)architecture to enhance the IoT security. Thepurpose of IoT-basedbotnet detection and protection system is to monitor and defend the cyber attacks by learning the various features of botnet and comparing the behavioral featuresofpotential threats. In practice, theexperiment uses Ryu SDN framework as SDN controller, Open vSwitch as OpenFlow switch and other tools to detect the threatsfor botnet protection. Experiment results show that the developed system can detect the Miraibotnet attacks and guard the networks effectively.
(10723905), Meghana Raghavendra. « Detection of IoT Botnets using Decision Trees ». Thesis, 2021.
Trouver le texte intégralInternational Data Corporation[3] (IDC) data estimates that 152,200 Internet of things (IoT) devices will be connected to the Internet every minute by the year 2025. This rapid expansion in the utilization of IoT devices in everyday life leads to an increase in the attack surface for cybercriminals. IoT devices are frequently compromised and used for the creation of botnets. However, it is difficult to apply the traditional methods to counteract IoT botnets and thus calls for finding effective and efficient methods to mitigate such threats. In this work, the network snapshots of IoT traffic infected with two botnets, i.e., Mirai and Bashlite, are studied. Specifically, the collected datasets include network traffic from 9 different IoT devices such as baby monitor, doorbells, thermostat, web cameras, and security cameras. Each dataset consists of 115 stream aggregation feature statistics like weight, mean, covariance, correlation coefficient, standard deviation, radius, and magnitude with a timeframe decay factor, along with a class label defining the traffic as benign or anomalous.
The goal of the research is to identify a proper machine learning method that can detect IoT botnet traffic accurately and in real-time on IoT edge devices with low computation power, in order to form the first line of defense in an IoT network. The initial step is to identify the most important features that distinguish between benign and anomalous traffic for IoT devices. Specifically, the Input Perturbation Ranking algorithm[12] with XGBoost[26]is applied to find the 9 most important features among the 115 features. These 9 features can be collected in real time and be applied as inputs to any detection method. Next, a supervised predictive machine learning method, i.e., Decision Trees, is proposed for faster and accurate detection of botnet traffic. The advantage of using decision trees over other machine learning methodologies, is that it achieves accurate results with low computation time and power. Unlike deep learning methodologies, decision trees can provide visual representation of the decision making and detection process. This can be easily translated into explicit security policies in the IoT environment. In the experiments conducted, it can be clearly seen that decision trees can detect anomalous traffic with an accuracy of 99.997% and takes 59 seconds for training and 0.068 seconds for prediction, which is much faster than the state-of-art deep-learning based detector, i.e., Kitsune[4]. Moreover, our results show that decision trees have an extremely low false positive rate of 0.019%. Using the 9 most important features, decision trees can further reduce the processing time while maintaining the accuracy. Hence, decision trees with important features are able to accurately and efficiently detect IoT botnets in real time and on a low performance edge device such as Raspberry Pi[9].
Chapitres de livres sur le sujet "IOT BOTNET DETECTION"
Nakip, Mert, et Erol Gelenbe. « Botnet Attack Detection with Incremental Online Learning ». Dans Communications in Computer and Information Science, 51–60. Cham : Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-09357-9_5.
Texte intégralKumar, Bandari Pranay, Gautham Rampalli, Pille Kamakshi et T. Senthil Murugan. « DDoS Botnet Attack Detection in IoT Devices ». Dans Lecture Notes in Networks and Systems, 21–27. Singapore : Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9967-2_3.
Texte intégralAmina, Shehu, Raul Vera, Tooska Dargahi et Ali Dehghantanha. « A Bibliometric Analysis of Botnet Detection Techniques ». Dans Handbook of Big Data and IoT Security, 345–65. Cham : Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-10543-3_15.
Texte intégralTulasi Ratnakar, P., N. Uday Vishal, P. Sai Siddharth et S. Saravanan. « Detection of IoT Botnet Using Recurrent Neural Network ». Dans Intelligent Data Communication Technologies and Internet of Things, 869–84. Singapore : Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-7610-9_63.
Texte intégralAborujilah, Abdulaziz, Rasheed Mohammad Nassr, AbdulAleem Al- Othmani, Nor Azlina Ali, Zalizah Awang Long, Mohd Nizam Husen, Tawfik Al-Hadhrami et Hideya Ochiai. « SMOTE-Based Framework for IoT Botnet Attack Detection ». Dans Communications in Computer and Information Science, 287–96. Singapore : Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-6835-4_19.
Texte intégralSakthipriya, N., V. Govindasamy et V. Akila. « Review of Deep Learning Approaches for IoT Botnet Detection ». Dans Algorithms for Intelligent Systems, 521–33. Singapore : Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-3951-8_40.
Texte intégralSangher, Kanti Singh, Archana Singh, Hari Mohan Pandey et Lakshmi Kalyani. « Implementation of Threats Detection Modeling with Deep Learning in IoT Botnet Attack Environment ». Dans IOT with Smart Systems, 585–92. Singapore : Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-3575-6_57.
Texte intégralChunduri, Hrushikesh, T. Gireesh Kumar et P. V. Sai Charan. « A Multi Class Classification for Detection of IoT Botnet Malware ». Dans Communications in Computer and Information Science, 17–29. Cham : Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-76776-1_2.
Texte intégralJwalin, B., et S. Saravanan. « A Large Scale IoT Botnet Attack Detection Using Ensemble Learning ». Dans Communications in Computer and Information Science, 183–93. Cham : Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-35644-5_14.
Texte intégralNgo, Quoc-Dung, Huy-Trung Nguyen, Hoang-Long Pham, Hoang Hanh-Nhan Ngo, Doan-Hieu Nguyen, Cong-Minh Dinh et Xuan-Hanh Vu. « A Graph-Based Approach for IoT Botnet Detection Using Reinforcement Learning ». Dans Computational Collective Intelligence, 465–78. Cham : Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-63007-2_36.
Texte intégralActes de conférences sur le sujet "IOT BOTNET DETECTION"
Alazzam, Hadeel, Abdulsalam Alsmady et Amaal Al Shorman. « Supervised detection of IoT botnet attacks ». Dans the Second International Conference. New York, New York, USA : ACM Press, 2019. http://dx.doi.org/10.1145/3368691.3368733.
Texte intégralRabhi, Sana, Tarek Abbes et Faouzi Zarai. « IoT botnet detection using deep learning ». Dans 2023 International Wireless Communications and Mobile Computing (IWCMC). IEEE, 2023. http://dx.doi.org/10.1109/iwcmc58020.2023.10182422.
Texte intégralMemos, Vasileios A., et Kostas E. Psannis. « AI-Powered Honeypots for Enhanced IoT Botnet Detection ». Dans 2020 3rd World Symposium on Communication Engineering (WSCE). IEEE, 2020. http://dx.doi.org/10.1109/wsce51339.2020.9275581.
Texte intégralDietz, Christian, Raphael Labaca Castro, Jessica Steinberger, Cezary Wilczak, Marcel Antzek, Anna Sperotto et Aiko Pras. « IoT-Botnet Detection and Isolation by Access Routers ». Dans 2018 9th International Conference on the Network of the Future (NOF). IEEE, 2018. http://dx.doi.org/10.1109/nof.2018.8598138.
Texte intégralKhaing, Myint Soe, Yee Mon Thant, Thazin Tun, Chaw Su Htwe et Mie Mie Su Thwin. « IoT Botnet Detection Mechanism Based on UDP Protocol ». Dans 2020 IEEE Conference on Computer Applications (ICCA). IEEE, 2020. http://dx.doi.org/10.1109/icca49400.2020.9022832.
Texte intégralEsha, H., Basanagouda S. Hadimani, S. P. Devika, P. T. Shanthala et R. Bhavana. « IoT Botnet Creation and Detection using Machine Learning ». Dans 2023 International Conference on Advancement in Computation & Computer Technologies (InCACCT). IEEE, 2023. http://dx.doi.org/10.1109/incacct57535.2023.10141717.
Texte intégralWu, Yalian, Xieen He et Xingnian Chen. « IoT-Botnet Traffic Detection Based on Deep Forest ». Dans 2022 IEEE 22nd International Conference on Communication Technology (ICCT). IEEE, 2022. http://dx.doi.org/10.1109/icct56141.2022.10072774.
Texte intégralMashaleh, Ashraf S., Noor Farizah Binti Ibrahim, Mohammad Alauthman et Ammar Almomani. « A Proposed Framework for Early Detection IoT Botnet ». Dans 2022 International Arab Conference on Information Technology (ACIT). IEEE, 2022. http://dx.doi.org/10.1109/acit57182.2022.9994166.
Texte intégralNomm, Sven, et Hayretdin Bahsi. « Unsupervised Anomaly Based Botnet Detection in IoT Networks ». Dans 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA). IEEE, 2018. http://dx.doi.org/10.1109/icmla.2018.00171.
Texte intégralLiu, Junyi, Shiyue Liu et Sihua Zhang. « Detection of IoT Botnet Based on Deep Learning ». Dans 2019 Chinese Control Conference (CCC). IEEE, 2019. http://dx.doi.org/10.23919/chicc.2019.8866088.
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