A Model for Intrusion Detection in IoT using Machine Learning
- Authors: Nkala, Junior Ruddy
- Date: 2019
- Subjects: Internet of things
- Language: English
- Type: Thesis , Masters , MSc (Computer Science )
- Identifier: http://hdl.handle.net/10353/17180 , vital:40863
- Description: The Internet of Things is an open and comprehensive global network of intelligent objects that have the capacity to auto-organize, share information, data and resources. There are currently over a billion devices connected to the Internet, and this number increases by the day. While these devices make our life easier, safer and healthier, they are expanding the number of attack targets vulnerable to cyber-attacks from potential hackers and malicious software. Therefore, protecting these devices from adversaries and unauthorized access and modification is very important. The purpose of this study is to develop a secure lightweight intrusion and anomaly detection model for IoT to help detect threats in the environment. We propose the use of data mining and machine learning algorithms as a classification technique for detecting abnormal or malicious traffic transmitted between devices due to potential attacks such as DoS, Man-In-Middle and Flooding attacks at the application level. This study makes use of two robust machine learning algorithms, namely the C4.5 Decision Trees and K-means clustering to develop an anomaly detection model. MATLAB Math Simulator was used for implementation. The study conducts a series of experiments in detecting abnormal data and normal data in a dataset that contains gas concentration readings from a number of sensors deployed in an Italian city over a year. Thereafter we examined the classification performance in terms of accuracy of our proposed anomaly detection model. Results drawn from the experiments conducted indicate that the size of the training sample improves classification ability of the proposed model. Our findings noted that the choice of discretization algorithm does matter in the quest for optimal classification performance. The proposed model proved accurate in detecting anomalies in IoT, and classifying between normal and abnormal data. The proposed model has a classification accuracy of 96.51% which proved to be higher compared to other algorithms such as the Naïve Bayes. The model proved to be lightweight and efficient in-terms of being faster at training and testing as compared to Artificial Neural Networks. The conclusions drawn from this research are a perspective from a novice machine learning researcher with valuable recommendations that ensure optimal classification of normal and abnormal IoT data.
- Full Text:
- Date Issued: 2019
- Authors: Nkala, Junior Ruddy
- Date: 2019
- Subjects: Internet of things
- Language: English
- Type: Thesis , Masters , MSc (Computer Science )
- Identifier: http://hdl.handle.net/10353/17180 , vital:40863
- Description: The Internet of Things is an open and comprehensive global network of intelligent objects that have the capacity to auto-organize, share information, data and resources. There are currently over a billion devices connected to the Internet, and this number increases by the day. While these devices make our life easier, safer and healthier, they are expanding the number of attack targets vulnerable to cyber-attacks from potential hackers and malicious software. Therefore, protecting these devices from adversaries and unauthorized access and modification is very important. The purpose of this study is to develop a secure lightweight intrusion and anomaly detection model for IoT to help detect threats in the environment. We propose the use of data mining and machine learning algorithms as a classification technique for detecting abnormal or malicious traffic transmitted between devices due to potential attacks such as DoS, Man-In-Middle and Flooding attacks at the application level. This study makes use of two robust machine learning algorithms, namely the C4.5 Decision Trees and K-means clustering to develop an anomaly detection model. MATLAB Math Simulator was used for implementation. The study conducts a series of experiments in detecting abnormal data and normal data in a dataset that contains gas concentration readings from a number of sensors deployed in an Italian city over a year. Thereafter we examined the classification performance in terms of accuracy of our proposed anomaly detection model. Results drawn from the experiments conducted indicate that the size of the training sample improves classification ability of the proposed model. Our findings noted that the choice of discretization algorithm does matter in the quest for optimal classification performance. The proposed model proved accurate in detecting anomalies in IoT, and classifying between normal and abnormal data. The proposed model has a classification accuracy of 96.51% which proved to be higher compared to other algorithms such as the Naïve Bayes. The model proved to be lightweight and efficient in-terms of being faster at training and testing as compared to Artificial Neural Networks. The conclusions drawn from this research are a perspective from a novice machine learning researcher with valuable recommendations that ensure optimal classification of normal and abnormal IoT data.
- Full Text:
- Date Issued: 2019
Dynamic service orchestration in heterogeneous internet of things environments
- Authors: Chindenga, Edmore
- Date: 2016
- Subjects: Internet of things
- Language: English
- Type: Thesis , Masters , MSc
- Identifier: http://hdl.handle.net/10353/8001 , vital:31457
- Description: Internet of Things (IoT) presents a dynamic global revolution in the Internet where physical and virtual “things” will communicate and share information. As the number of devices increases, there is a need for a plug-and–interoperate approach of deploying “things” to the existing network with less or no human need for configuration. The plug-and-interoperate approach allows heterogeneous “things” to seamlessly interoperate, interact and exchange information and subsequently share services. Services are represented as functionalities that are offered by the “things”. Service orchestration provides an approach to integration and interoperability that decouples applications from each other, enhancing capabilities to centrally manage and monitor components. This work investigated requirements for semantic interoperability and exposed current challenges in IoT interoperability as a means of facilitating services orchestration in IoT. The research proposes a platform that allows heterogeneous devices to collaborate thereby enabling dynamic service orchestration. The platform provides a common framework for representing semantics allowing for a consistent information exchange format. The information is stored and presented in an ontology thereby preserving semantics and making the information comprehensible to machines allowing for automated addressing, tracking and discovery as well as information representation, storage, and exchange. Process mining techniques were used to discover service orchestrations. Process mining techniques enabled the analysis of runtime behavior of service orchestrations and the semantic breakdown of the service request and creation in real time. This enabled the research to draw observations that led to conclusions presented in this work. The research noted that the use of semantic technologies facilitates interoperability in heterogeneous devices and can be implemented as a means to bypass challenges presented by differences in IoT “things”.
- Full Text:
- Date Issued: 2016
- Authors: Chindenga, Edmore
- Date: 2016
- Subjects: Internet of things
- Language: English
- Type: Thesis , Masters , MSc
- Identifier: http://hdl.handle.net/10353/8001 , vital:31457
- Description: Internet of Things (IoT) presents a dynamic global revolution in the Internet where physical and virtual “things” will communicate and share information. As the number of devices increases, there is a need for a plug-and–interoperate approach of deploying “things” to the existing network with less or no human need for configuration. The plug-and-interoperate approach allows heterogeneous “things” to seamlessly interoperate, interact and exchange information and subsequently share services. Services are represented as functionalities that are offered by the “things”. Service orchestration provides an approach to integration and interoperability that decouples applications from each other, enhancing capabilities to centrally manage and monitor components. This work investigated requirements for semantic interoperability and exposed current challenges in IoT interoperability as a means of facilitating services orchestration in IoT. The research proposes a platform that allows heterogeneous devices to collaborate thereby enabling dynamic service orchestration. The platform provides a common framework for representing semantics allowing for a consistent information exchange format. The information is stored and presented in an ontology thereby preserving semantics and making the information comprehensible to machines allowing for automated addressing, tracking and discovery as well as information representation, storage, and exchange. Process mining techniques were used to discover service orchestrations. Process mining techniques enabled the analysis of runtime behavior of service orchestrations and the semantic breakdown of the service request and creation in real time. This enabled the research to draw observations that led to conclusions presented in this work. The research noted that the use of semantic technologies facilitates interoperability in heterogeneous devices and can be implemented as a means to bypass challenges presented by differences in IoT “things”.
- Full Text:
- Date Issued: 2016
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