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.
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- 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.
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Development of an Extensible Framework for Easy Implementation of Image Processing Applications on Android Operating System
- Authors: Gunu, Bulelani
- Date: 2019
- Subjects: Operating systems (Computers)
- Language: English
- Type: Thesis , Masters , MSc (Computer Science )
- Identifier: http://hdl.handle.net/10353/17201 , vital:40865
- Description: Image processing is a field that has been in existence for many years and it continues to grow with many other research areas adopting its use. One such research area is the area of mobile devices. Mobile devices have been equipped with image processing software and hardware so as to apply image processing features. While there are many applications of image processing and new applications have been developed, there are still many functionalities that these image processing software perform the same. The development of these software from scratch requires a lot of effort and can be time consuming. This becomes even worse for mobile device application developers, specifically Android developers, who have no knowledge of implementing image processing functionalities. This project offers a software framework which allows Android application developers to focus on their unique requirements while incorporating image processing features into their applications. The framework provides the common image processing functionalities and Android developers do not need to know the internal working of the framework in order to use it. This helps reduce application development time and effort. The framework also offers an extensibility feature which takes into consideration the future growth. This means that third party developers can keep the framework up to date with the technological advancements. The presented framework is shown to be requiring less technical expertise. Also, the way in which the system is design makes it easy to understand. This design can be adopted for other related projects that require extensible frameworks for the Android operating system.
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- Authors: Gunu, Bulelani
- Date: 2019
- Subjects: Operating systems (Computers)
- Language: English
- Type: Thesis , Masters , MSc (Computer Science )
- Identifier: http://hdl.handle.net/10353/17201 , vital:40865
- Description: Image processing is a field that has been in existence for many years and it continues to grow with many other research areas adopting its use. One such research area is the area of mobile devices. Mobile devices have been equipped with image processing software and hardware so as to apply image processing features. While there are many applications of image processing and new applications have been developed, there are still many functionalities that these image processing software perform the same. The development of these software from scratch requires a lot of effort and can be time consuming. This becomes even worse for mobile device application developers, specifically Android developers, who have no knowledge of implementing image processing functionalities. This project offers a software framework which allows Android application developers to focus on their unique requirements while incorporating image processing features into their applications. The framework provides the common image processing functionalities and Android developers do not need to know the internal working of the framework in order to use it. This helps reduce application development time and effort. The framework also offers an extensibility feature which takes into consideration the future growth. This means that third party developers can keep the framework up to date with the technological advancements. The presented framework is shown to be requiring less technical expertise. Also, the way in which the system is design makes it easy to understand. This design can be adopted for other related projects that require extensible frameworks for the Android operating system.
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