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Статті в журналах з теми "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 (September 15, 2022): 185–95. http://dx.doi.org/10.36548/jsws.2022.3.005.
Повний текст джерелаWazzan, Majda, Daniyal Algazzawi, Omaima Bamasaq, Aiiad Albeshri, and Li Cheng. "Internet of Things Botnet Detection Approaches: Analysis and Recommendations for Future Research." Applied Sciences 11, no. 12 (June 20, 2021): 5713. http://dx.doi.org/10.3390/app11125713.
Повний текст джерелаYang, Changjin, Weili Guan, and Zhijie Fang. "IoT Botnet Attack Detection Model Based on DBO-Catboost." Applied Sciences 13, no. 12 (June 15, 2023): 7169. http://dx.doi.org/10.3390/app13127169.
Повний текст джерелаJovanović, Đorđe, and 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.
Повний текст джерелаWazzan, Majda, Daniyal Algazzawi, Aiiad Albeshri, Syed Hasan, Osama Rabie, and Muhammad Zubair Asghar. "Cross Deep Learning Method for Effectively Detecting the Propagation of IoT Botnet." Sensors 22, no. 10 (May 20, 2022): 3895. http://dx.doi.org/10.3390/s22103895.
Повний текст джерелаNegera, Worku Gachena, Friedhelm Schwenker, Taye Girma Debelee, Henock Mulugeta Melaku, and Yehualashet Megeresa Ayano. "Review of Botnet Attack Detection in SDN-Enabled IoT Using Machine Learning." Sensors 22, no. 24 (December 14, 2022): 9837. http://dx.doi.org/10.3390/s22249837.
Повний текст джерелаHaq, Mohd Anul. "DBoTPM: A Deep Neural Network-Based Botnet Prediction Model." Electronics 12, no. 5 (February 27, 2023): 1159. http://dx.doi.org/10.3390/electronics12051159.
Повний текст джерелаAkash, Nazmus Sakib, Shakir Rouf, Sigma Jahan, Amlan Chowdhury, and Jia Uddin. "Botnet Detection in IoT Devices Using Random Forest Classifier with Independent Component Analysis." Journal of Information and Communication Technology 21, No.2 (April 7, 2022): 201–32. http://dx.doi.org/10.32890/jict2022.21.2.3.
Повний текст джерелаAl-Duwairi, Basheer, Wafaa Al-Kahla, Mhd Ammar AlRefai, Yazid Abedalqader, Abdullah Rawash, and Rana Fahmawi. "SIEM-based detection and mitigation of IoT-botnet DDoS attacks." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 2 (April 1, 2020): 2182. http://dx.doi.org/10.11591/ijece.v10i2.pp2182-2191.
Повний текст джерелаAlharbi, Abdullah, Wael Alosaimi, Hashem Alyami, Hafiz Tayyab Rauf, and Robertas Damaševičius. "Botnet Attack Detection Using Local Global Best Bat Algorithm for Industrial Internet of Things." Electronics 10, no. 11 (June 3, 2021): 1341. http://dx.doi.org/10.3390/electronics10111341.
Повний текст джерелаДисертації з теми "IOT BOTNET DETECTION"
KARAMVEER. "IOT BOTNET DETECTION." Thesis, 2022. http://dspace.dtu.ac.in:8080/jspui/handle/repository/19143.
Повний текст джерелаLin, Jheng-Fong, and 林正逢. "Application of Deep Learning in IoT Botnet Detection." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/ab2xv7.
Повний текст джерела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.
Знайти повний текст джерелаInternational 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].
Частини книг з теми "IOT BOTNET DETECTION"
Nakip, Mert, and Erol Gelenbe. "Botnet Attack Detection with Incremental Online Learning." In 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.
Повний текст джерелаKumar, Bandari Pranay, Gautham Rampalli, Pille Kamakshi, and T. Senthil Murugan. "DDoS Botnet Attack Detection in IoT Devices." In 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.
Повний текст джерелаAmina, Shehu, Raul Vera, Tooska Dargahi, and Ali Dehghantanha. "A Bibliometric Analysis of Botnet Detection Techniques." In 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.
Повний текст джерелаTulasi Ratnakar, P., N. Uday Vishal, P. Sai Siddharth, and S. Saravanan. "Detection of IoT Botnet Using Recurrent Neural Network." In 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.
Повний текст джерелаAborujilah, Abdulaziz, Rasheed Mohammad Nassr, AbdulAleem Al- Othmani, Nor Azlina Ali, Zalizah Awang Long, Mohd Nizam Husen, Tawfik Al-Hadhrami, and Hideya Ochiai. "SMOTE-Based Framework for IoT Botnet Attack Detection." In Communications in Computer and Information Science, 287–96. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-6835-4_19.
Повний текст джерелаSakthipriya, N., V. Govindasamy, and V. Akila. "Review of Deep Learning Approaches for IoT Botnet Detection." In Algorithms for Intelligent Systems, 521–33. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-3951-8_40.
Повний текст джерелаSangher, Kanti Singh, Archana Singh, Hari Mohan Pandey, and Lakshmi Kalyani. "Implementation of Threats Detection Modeling with Deep Learning in IoT Botnet Attack Environment." In IOT with Smart Systems, 585–92. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-3575-6_57.
Повний текст джерелаChunduri, Hrushikesh, T. Gireesh Kumar, and P. V. Sai Charan. "A Multi Class Classification for Detection of IoT Botnet Malware." In 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.
Повний текст джерелаJwalin, B., and S. Saravanan. "A Large Scale IoT Botnet Attack Detection Using Ensemble Learning." In 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.
Повний текст джерелаNgo, Quoc-Dung, Huy-Trung Nguyen, Hoang-Long Pham, Hoang Hanh-Nhan Ngo, Doan-Hieu Nguyen, Cong-Minh Dinh, and Xuan-Hanh Vu. "A Graph-Based Approach for IoT Botnet Detection Using Reinforcement Learning." In Computational Collective Intelligence, 465–78. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-63007-2_36.
Повний текст джерелаТези доповідей конференцій з теми "IOT BOTNET DETECTION"
Alazzam, Hadeel, Abdulsalam Alsmady, and Amaal Al Shorman. "Supervised detection of IoT botnet attacks." In the Second International Conference. New York, New York, USA: ACM Press, 2019. http://dx.doi.org/10.1145/3368691.3368733.
Повний текст джерелаRabhi, Sana, Tarek Abbes, and Faouzi Zarai. "IoT botnet detection using deep learning." In 2023 International Wireless Communications and Mobile Computing (IWCMC). IEEE, 2023. http://dx.doi.org/10.1109/iwcmc58020.2023.10182422.
Повний текст джерелаMemos, Vasileios A., and Kostas E. Psannis. "AI-Powered Honeypots for Enhanced IoT Botnet Detection." In 2020 3rd World Symposium on Communication Engineering (WSCE). IEEE, 2020. http://dx.doi.org/10.1109/wsce51339.2020.9275581.
Повний текст джерелаDietz, Christian, Raphael Labaca Castro, Jessica Steinberger, Cezary Wilczak, Marcel Antzek, Anna Sperotto, and Aiko Pras. "IoT-Botnet Detection and Isolation by Access Routers." In 2018 9th International Conference on the Network of the Future (NOF). IEEE, 2018. http://dx.doi.org/10.1109/nof.2018.8598138.
Повний текст джерелаKhaing, Myint Soe, Yee Mon Thant, Thazin Tun, Chaw Su Htwe, and Mie Mie Su Thwin. "IoT Botnet Detection Mechanism Based on UDP Protocol." In 2020 IEEE Conference on Computer Applications (ICCA). IEEE, 2020. http://dx.doi.org/10.1109/icca49400.2020.9022832.
Повний текст джерелаEsha, H., Basanagouda S. Hadimani, S. P. Devika, P. T. Shanthala, and R. Bhavana. "IoT Botnet Creation and Detection using Machine Learning." In 2023 International Conference on Advancement in Computation & Computer Technologies (InCACCT). IEEE, 2023. http://dx.doi.org/10.1109/incacct57535.2023.10141717.
Повний текст джерелаWu, Yalian, Xieen He, and Xingnian Chen. "IoT-Botnet Traffic Detection Based on Deep Forest." In 2022 IEEE 22nd International Conference on Communication Technology (ICCT). IEEE, 2022. http://dx.doi.org/10.1109/icct56141.2022.10072774.
Повний текст джерелаMashaleh, Ashraf S., Noor Farizah Binti Ibrahim, Mohammad Alauthman, and Ammar Almomani. "A Proposed Framework for Early Detection IoT Botnet." In 2022 International Arab Conference on Information Technology (ACIT). IEEE, 2022. http://dx.doi.org/10.1109/acit57182.2022.9994166.
Повний текст джерелаNomm, Sven, and Hayretdin Bahsi. "Unsupervised Anomaly Based Botnet Detection in IoT Networks." In 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA). IEEE, 2018. http://dx.doi.org/10.1109/icmla.2018.00171.
Повний текст джерелаLiu, Junyi, Shiyue Liu, and Sihua Zhang. "Detection of IoT Botnet Based on Deep Learning." In 2019 Chinese Control Conference (CCC). IEEE, 2019. http://dx.doi.org/10.23919/chicc.2019.8866088.
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