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.

Xehetasun bibliografikoak
Egile Nagusiak:	Arko, Aritro Roy, Khan, Saadat Hasan, Preety, A a Anjum, Biswas, Mehrab Hossain
Beste egile batzuk:	Chakrabarty, Amitabha
Formatua:	Thesis
Hizkuntza:	English
Argitaratua:	Brac University 2019
Gaiak:	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)
Sarrera elektronikoa:	http://hdl.handle.net/10361/12776

Antzeko izenburuak

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

IoT security risk analysis
nork: Sakib, Nazmus, et al.
Argitaratua: (2020)

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Anomaly clustering based on correspondence analysis
nork: Islam, Humayra
Argitaratua: (2018)