Anomaly Detection Using Deep Neural Network for IoT Architecture
The revolutionary idea of the internet of things (IoT) architecture has gained enormous popularity over the last decade, resulting in an exponential growth in the IoT networks, connected devices, and the data processed therein. Since IoT devices generate and exchange sensitive data over the traditio...
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| Format: | Article |
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MDPI AG
2021-07-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/15/7050 |
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| author | Zeeshan Ahmad Adnan Shahid Khan Kashif Nisar Iram Haider Rosilah Hassan Muhammad Reazul Haque Seleviawati Tarmizi Joel J. P. C. Rodrigues |
| author_facet | Zeeshan Ahmad Adnan Shahid Khan Kashif Nisar Iram Haider Rosilah Hassan Muhammad Reazul Haque Seleviawati Tarmizi Joel J. P. C. Rodrigues |
| author_sort | Zeeshan Ahmad |
| collection | DOAJ |
| description | The revolutionary idea of the internet of things (IoT) architecture has gained enormous popularity over the last decade, resulting in an exponential growth in the IoT networks, connected devices, and the data processed therein. Since IoT devices generate and exchange sensitive data over the traditional internet, security has become a prime concern due to the generation of zero-day cyberattacks. A network-based intrusion detection system (NIDS) can provide the much-needed efficient security solution to the IoT network by protecting the network entry points through constant network traffic monitoring. Recent NIDS have a high false alarm rate (FAR) in detecting the anomalies, including the novel and zero-day anomalies. This paper proposes an efficient anomaly detection mechanism using mutual information (MI), considering a deep neural network (DNN) for an IoT network. A comparative analysis of different deep-learning models such as DNN, Convolutional Neural Network, Recurrent Neural Network, and its different variants, such as Gated Recurrent Unit and Long Short-term Memory is performed considering the IoT-Botnet 2020 dataset. Experimental results show the improvement of 0.57–2.6% in terms of the model’s accuracy, while at the same time reducing the FAR by 0.23–7.98% to show the effectiveness of the DNN-based NIDS model compared to the well-known deep learning models. It was also observed that using only the 16–35 best numerical features selected using MI instead of 80 features of the dataset result in almost negligible degradation in the model’s performance but helped in decreasing the overall model’s complexity. In addition, the overall accuracy of the DL-based models is further improved by almost 0.99–3.45% in terms of the detection accuracy considering only the top five categorical and numerical features. |
| first_indexed | 2024-03-10T09:18:40Z |
| format | Article |
| id | doaj.art-473b80a816c64bae80508bf3c088d967 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T09:18:40Z |
| publishDate | 2021-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-473b80a816c64bae80508bf3c088d9672023-11-22T05:23:38ZengMDPI AGApplied Sciences2076-34172021-07-011115705010.3390/app11157050Anomaly Detection Using Deep Neural Network for IoT ArchitectureZeeshan Ahmad0Adnan Shahid Khan1Kashif Nisar2Iram Haider3Rosilah Hassan4Muhammad Reazul Haque5Seleviawati Tarmizi6Joel J. P. C. Rodrigues7Faculty of Computer Science and Information Technology, Universiti Malaysia Sarawak, Kota Samarahan 94300, MalaysiaFaculty of Computer Science and Information Technology, Universiti Malaysia Sarawak, Kota Samarahan 94300, MalaysiaFaculty of Computing and Informatics, Universiti Malaysia Sabah, Jalan UMS, Kota Kinabalu 88400, MalaysiaFaculty of Computing and Informatics, Universiti Malaysia Sabah, Jalan UMS, Kota Kinabalu 88400, MalaysiaCentre for Cyber Security, Faculty of Information Science and Technology (FTSM), Universiti Kebangsaan Malaysia, Bangi 43600, MalaysiaFaculty of Computing & Informatics, Multimedia University, Persiaran Multimedia, Cyberjaya 63100, MalaysiaFaculty of Computer Science and Information Technology, Universiti Malaysia Sarawak, Kota Samarahan 94300, MalaysiaPost-Graduation Program on Electrical Engineering, Federal University of Piauí (UFPI), Teresina 64049-550, PI, BrazilThe revolutionary idea of the internet of things (IoT) architecture has gained enormous popularity over the last decade, resulting in an exponential growth in the IoT networks, connected devices, and the data processed therein. Since IoT devices generate and exchange sensitive data over the traditional internet, security has become a prime concern due to the generation of zero-day cyberattacks. A network-based intrusion detection system (NIDS) can provide the much-needed efficient security solution to the IoT network by protecting the network entry points through constant network traffic monitoring. Recent NIDS have a high false alarm rate (FAR) in detecting the anomalies, including the novel and zero-day anomalies. This paper proposes an efficient anomaly detection mechanism using mutual information (MI), considering a deep neural network (DNN) for an IoT network. A comparative analysis of different deep-learning models such as DNN, Convolutional Neural Network, Recurrent Neural Network, and its different variants, such as Gated Recurrent Unit and Long Short-term Memory is performed considering the IoT-Botnet 2020 dataset. Experimental results show the improvement of 0.57–2.6% in terms of the model’s accuracy, while at the same time reducing the FAR by 0.23–7.98% to show the effectiveness of the DNN-based NIDS model compared to the well-known deep learning models. It was also observed that using only the 16–35 best numerical features selected using MI instead of 80 features of the dataset result in almost negligible degradation in the model’s performance but helped in decreasing the overall model’s complexity. In addition, the overall accuracy of the DL-based models is further improved by almost 0.99–3.45% in terms of the detection accuracy considering only the top five categorical and numerical features.https://www.mdpi.com/2076-3417/11/15/7050IoT architecturedeep neural networkanomaly detectiondeep learningnetwork-based intrusion detection system |
| spellingShingle | Zeeshan Ahmad Adnan Shahid Khan Kashif Nisar Iram Haider Rosilah Hassan Muhammad Reazul Haque Seleviawati Tarmizi Joel J. P. C. Rodrigues Anomaly Detection Using Deep Neural Network for IoT Architecture Applied Sciences IoT architecture deep neural network anomaly detection deep learning network-based intrusion detection system |
| title | Anomaly Detection Using Deep Neural Network for IoT Architecture |
| title_full | Anomaly Detection Using Deep Neural Network for IoT Architecture |
| title_fullStr | Anomaly Detection Using Deep Neural Network for IoT Architecture |
| title_full_unstemmed | Anomaly Detection Using Deep Neural Network for IoT Architecture |
| title_short | Anomaly Detection Using Deep Neural Network for IoT Architecture |
| title_sort | anomaly detection using deep neural network for iot architecture |
| topic | IoT architecture deep neural network anomaly detection deep learning network-based intrusion detection system |
| url | https://www.mdpi.com/2076-3417/11/15/7050 |
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