Intrusion of Malware (DDoS) detection in IoT devices using Machine Learning on Cyberspace

Intrusion of Malware (DDoS) detection in IoT devices using Machine Learning on Cyberspace

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022.

التفاصيل البيبلوغرافية
المؤلفون الرئيسيون:	Al Amin, Istiak, Lamiya, Salsabil, Sheikh, Noshin Anjum, Haque, S. M. Tanjimul
مؤلفون آخرون:	Arif, Hossain
التنسيق:	أطروحة
اللغة:	en_US
منشور في:	Brac University 2022
الموضوعات:	IoT DDoS k-Nearest-Neighbour Random Forest Naive Bayes Artificial Neural Network Support Vector Machine Cyberspace Internet of things Machine learning
الوصول للمادة أونلاين:	http://hdl.handle.net/10361/17018

مواد مشابهة

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A machine learning based approach for DDos attack detection
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Best feature selection and data visualization for breast cancer prediction
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A machine learning approach to analyze and predict rainfall in different regions of Bangladesh
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Automatic classified myocardial infarction detection using machine learning and forewarning system with location of the patient using GSM module
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Envisioning cyberspace : designing 3-D electronic spaces /
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An approach to detect epileptic seizure using XAI and machine learning
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Predicting brain age from EEG signals using machine learning and neural network
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Cardiovascular disease prediction model using Machine Learning Algorithm
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Effectiveness of data mining in predicting heart diseases
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Prediction of diabetes induced complications using different machine learning algorithms
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Analyzing data of social media to evaluate customer behavior of companies using sentiment analysis
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Rating detection by reviews using ML and NLP towards mobile phone recommendation
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