Anomaly detection In IoT using machine learning algorithms

Anomaly detection In IoT using machine learning algorithms

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2019.

Bibliografiske detaljer
Main Authors:	Arko, Aritro Roy, Khan, Saadat Hasan, Preety, A a Anjum, Biswas, Mehrab Hossain
Andre forfattere:	Chakrabarty, Amitabha
Format:	Thesis
Sprog:	English
Udgivet:	Brac University 2019
Fag:	IoT (Internet of Things) Anomaly detection Intrusion detection Machine learning algorithms Sensor data Network data UNSW-NB15 Machine learning Internet of things Intrusion detection systems (Computer security) Anomaly detection (Computer security)
Online adgang:	http://hdl.handle.net/10361/12776

Lignende værker

Performance analysis of intrusion detection systems using the PyCaret machine learning library on the UNSW-NB15 dataset
af: Abdullah, et al.
Udgivet: (2021)

Performance analysis of deep learning algorithms for Intrusion Detection System(IDS) of IoT Security
af: Fiha, Yeamin Jahan, et al.
Udgivet: (2022)

IoT based automated entry system with integration of Covid-19 symptom detection
af: Ruhin, Rubaiyat Alam, et al.
Udgivet: (2024)

Fault detection and predictive maintenance in IoT-based building management system using machine learning
af: Sabuj, Khaja Sheikh Imran, et al.
Udgivet: (2022)

Automated intruder detection from image sequences using minimum volume sets
af: Ahmed, Tarem, et al.
Udgivet: (2016)

A convolutional neural network based model with improved activation function and optimizer for effective intrusion detection and classification
af: Kabir, Solaiman, et al.
Udgivet: (2021)

Advance waterway transport system based on Internet of Things (IoT)
af: Miah, MD Imtiaj, et al.
Udgivet: (2024)

Exploring the intersection of machine learning and explainable artificial intelligence: An analysis and validation of ML models through XAI for intrusion detection
af: Rahman, Masroor, et al.
Udgivet: (2023)

IoT based Road accident controlling model using Wireless Body Area Network and GPS
af: Islam, Nayem-Ul, et al.
Udgivet: (2021)

Modeling and simulation of a gas detecting device for providing safety; and reducing the risk of accidents due to gas explosion or fire
af: Khan, Rafsan, et al.
Udgivet: (2022)

ML based performance assurance and VoC management of highly convergence mobile operator network.
af: Rahman, Md. Arifur
Udgivet: (2024)

IoT based health monitoring system for pregnant women & children
af: Ohana, Fariha Islam, et al.
Udgivet: (2022)

Machine learning based data processing and latency reduction in the internet of things for agriculture
af: Rahman, Sajeed Ur, et al.
Udgivet: (2019)

Fault detection in waste water treatment plant using statistical analysis & machine learning
af: Haque, Md. Ezazul, et al.
Udgivet: (2019)

Learning algorithms for anomaly detection from images
af: Ahmed, Tarem, et al.
Udgivet: (2018)

IoT security risk analysis
af: Sakib, Nazmus, et al.
Udgivet: (2020)

Botnet detection In IoT devices using machine learning
af: Rouf, Shakir, et al.
Udgivet: (2020)

Implementation of an IoT based home automation system using real time sensors
af: Mandal, Dipta, et al.
Udgivet: (2019)

3G and 4G paging success rate based mobile network anomaly detection using supervised and unsupervised learning
af: Ahasan, Md Rakibul
Udgivet: (2022)

Detecting anomalies in human eyes using structural similarity index measurement
af: Jahan, Monwar, et al.
Udgivet: (2019)

Real-time DDoS detection in software-defined networks using machine learning
af: Hasan, Kadir, et al.
Udgivet: (2024)

Introducing AI in garment fault detection using YOLOv5 to reduce bottleneck
af: Sanjana, Jasia, et al.
Udgivet: (2024)

Analyzing security threats and mitigation techniques for fog computing in IoT-enabled smart cities
af: Emon, Sheikh Sadi, et al.
Udgivet: (2024)

The application of Internet Of Things (IoT) to the traditional power grid
af: Morshed, Ragib, et al.
Udgivet: (2020)

Data security model using deep learning and edge computing for Internet of Things (IoT) in smart city
af: Aziz, Md. Yasin, et al.
Udgivet: (2021)

A deep learning based autonomous electric vehicle on unstructured road conditions
af: Adnan, Ashik, et al.
Udgivet: (2021)

Real time performance analysis on DDoS attack detection using machine learning
af: Suvra, Debashis Kar, et al.
Udgivet: (2021)

Multi images retrieved for automatic intruder detection using kernel-based learning algorithm
af: Shahrear, S.M Fardin, et al.
Udgivet: (2016)

Flood monitoring and warning system for embankment using IoT
af: Dutta, Aparup, et al.
Udgivet: (2022)

Using deep learning algorithms to detect violent activities
af: Ammar, S.M. Rojin, et al.
Udgivet: (2019)

The influence of IoT-based agrotech system in Bangladesh on developing urban farming
af: Barua, Pranto
Udgivet: (2023)

Internet of Things (IoT) based air quality index measurement system for Dhaka city
af: Aurangajeb, Raoha, et al.
Udgivet: (2022)

Multi-modal hate speech detection using machine learning
af: Shill, Ponkoj Chandra, et al.
Udgivet: (2021)

TinyML for emotion detection in voice signals: evaluating and proposing algorithms for IoT wearable devices
af: Ahmed, Hasibul Hasan, et al.
Udgivet: (2024)

IoT based air components collection for machine learning reinforcement
af: Islam, Tanjima, et al.
Udgivet: (2024)

Hate speech detection using machine learning techniques
af: Manzoor, Tahbib, et al.
Udgivet: (2024)

Real-time foul detection in football matches using machine learning techniques
af: Siddiqui, Bishal Sadi, et al.
Udgivet: (2024)

IoT smart meter for electricity consumers
af: Wali-ur- Rahman, Md., et al.
Udgivet: (2019)

Malicious data classification in packet data network through hybrid meta deep learning
af: Tapu, Sakib Uddin, et al.
Udgivet: (2023)

Anomaly clustering based on correspondence analysis
af: Islam, Humayra
Udgivet: (2018)