國立虎尾科技大學 |

Cybersecurity Attacks Detection for MQTT-IoT Networks Using Machine Learning Ensemble Techniques.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Cybersecurity Attacks Detection for MQTT-IoT Networks Using Machine Learning Ensemble Techniques./
作者:	Abdelbasit, Sahar Mohamed Bukhari.
面頁冊數:	1 online resource (93 pages)
附註:	Source: Masters Abstracts International, Volume: 85-03.
Contained By:	Masters Abstracts International85-03.
標題:	Electrical engineering. -
電子資源:	click for full text (PQDT)
ISBN:	9798380179188

Cybersecurity Attacks Detection for MQTT-IoT Networks Using Machine Learning Ensemble Techniques.
Abdelbasit, Sahar Mohamed Bukhari.

Cybersecurity Attacks Detection for MQTT-IoT Networks Using Machine Learning Ensemble Techniques. - 1 online resource (93 pages)

Source: Masters Abstracts International, Volume: 85-03.

Thesis (M.Sc.C.)--Rochester Institute of Technology, 2023.

Includes bibliographical references

The Internet of Things (IoT) is one of the technical advancements that is progressing swiftly. which promises to be revolutionary soon. IoT systems are convenient due to its device centralized and computerized control. This technology allows various physical devices, home applications, vehicles, appliances, etc., to be interconnected and exposed to the Internet. On the other hand, it entails the fundamental need to protect the network from adversarial and unwanted alterations. Machine-to-machine protocols like Message Queuing Telemetry Transport (MQTT) are typically used by IoT devices to communicate. Numerous techniques to attack networks employ the lightweight messaging protocol known as MQTT (Message Queuing Telemetry Transport). Due to its heterogeneous nature and the lack of security approaches, the publish-subscribe strategy utilized by the MQTT protocol increases the number of potential network attacks. This thesis presents a novel approach to detecting cybersecurity breaches in MQTT-IoT networks using machine learning techniques.We suggest a detection system to address the issue of cybersecurity threats in MQTT-IoT networks. Our method involves cleaning the data to pull out relevant features, training the ensemble machine learning models on these features, and then using these models to find anomaly behavior that could indicate a cyberattack.We implemented our plan by using Machine Learning Ensemble techniques and Feature selection. To test our system, we ran many experiments using MQTT-IoT-IDS2020, a dataset that included both normal MQTT-IoT network activity and simulated attacks of different types. Our experimental findings indicate that our detection system, grounded in machine learning, can identify cybersecurity threats on MQTT-IoT networks with notable accuracy, precision, F1-score, and recall.The obtained results for binary and multiclass classification indicate that the proposed system can bring a remarkable layer of security. We show how Machine Learning Ensemble Techniques applied to small low-cost devices are an efficient and versatile combination characterized by a bright future ahead.This thesis advances the application of machine learning methodologies in cybersecurity and contributes to the enhancement of security protocols within MQTT-IoT networks.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798380179188Subjects--Topical Terms:

596380
Electrical engineering.
Subjects--Index Terms:

Internet of ThingsIndex Terms--Genre/Form:

554714
Electronic books.

Cybersecurity Attacks Detection for MQTT-IoT Networks Using Machine Learning Ensemble Techniques.
LDR:03712ntm a22003977 4500 001 1149645
005 20241022112605.5
006 m o d
007 cr bn ---uuuuu
008 250605s2023 xx obm 000 0 eng d
020 $a 9798380179188
035 $a (MiAaPQ)AAI30638572
035 $a AAI30638572
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a Abdelbasit, Sahar Mohamed Bukhari. $3 1475957
245 1 0 $a Cybersecurity Attacks Detection for MQTT-IoT Networks Using Machine Learning Ensemble Techniques.
264 0 $c 2023
300 $a 1 online resource (93 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Masters Abstracts International, Volume: 85-03.
500 $a Advisor: Almobaideen, Wesam.
502 $a Thesis (M.Sc.C.)--Rochester Institute of Technology, 2023.
504 $a Includes bibliographical references
520 $a The Internet of Things (IoT) is one of the technical advancements that is progressing swiftly. which promises to be revolutionary soon. IoT systems are convenient due to its device centralized and computerized control. This technology allows various physical devices, home applications, vehicles, appliances, etc., to be interconnected and exposed to the Internet. On the other hand, it entails the fundamental need to protect the network from adversarial and unwanted alterations. Machine-to-machine protocols like Message Queuing Telemetry Transport (MQTT) are typically used by IoT devices to communicate. Numerous techniques to attack networks employ the lightweight messaging protocol known as MQTT (Message Queuing Telemetry Transport). Due to its heterogeneous nature and the lack of security approaches, the publish-subscribe strategy utilized by the MQTT protocol increases the number of potential network attacks. This thesis presents a novel approach to detecting cybersecurity breaches in MQTT-IoT networks using machine learning techniques.We suggest a detection system to address the issue of cybersecurity threats in MQTT-IoT networks. Our method involves cleaning the data to pull out relevant features, training the ensemble machine learning models on these features, and then using these models to find anomaly behavior that could indicate a cyberattack.We implemented our plan by using Machine Learning Ensemble techniques and Feature selection. To test our system, we ran many experiments using MQTT-IoT-IDS2020, a dataset that included both normal MQTT-IoT network activity and simulated attacks of different types. Our experimental findings indicate that our detection system, grounded in machine learning, can identify cybersecurity threats on MQTT-IoT networks with notable accuracy, precision, F1-score, and recall.The obtained results for binary and multiclass classification indicate that the proposed system can bring a remarkable layer of security. We show how Machine Learning Ensemble Techniques applied to small low-cost devices are an efficient and versatile combination characterized by a bright future ahead.This thesis advances the application of machine learning methodologies in cybersecurity and contributes to the enhancement of security protocols within MQTT-IoT networks.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2024
538 $a Mode of access: World Wide Web
650 4 $a Electrical engineering. $3 596380
650 4 $a Computer engineering. $3 569006
653 $a Internet of Things
653 $a Machine learning
653 $a MQTT-IoT networks
653 $a Cybersecurity threats
653 $a Computerized control
655 7 $a Electronic books. $2 local $3 554714
690 $a 0544
690 $a 0464
690 $a 0800
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a Rochester Institute of Technology. $b Electrical Engineering. $3 1184409
773 0 $t Masters Abstracts International $g 85-03.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30638572 $z click for full text (PQDT)