A Framework for Detecting Multiple Cyberattacks in IoT Environment
| dc.contributor.advisor | Mesfin Kifle (PhD) | |
| dc.contributor.author | Yonas Mekonnen | |
| dc.date.accessioned | 2025-08-17T21:40:40Z | |
| dc.date.available | 2025-08-17T21:40:40Z | |
| dc.date.issued | 2025-02-25 | |
| dc.description.abstract | The Internet of Things refers to the growing trend of embedding ubiquitous and pervasive computing capabilities through sensor networks and internet connectivity. The growth and expansion of newly evolved cyberattacks, network patterns and heterogeneous nature of cyberattacks trend has become the warfare across the globe and challenges to apply single layer cyberattacks detection techniques to the Internet of Things. This research work identified the lack of cyberattacks detection framework as the major gap for detection of multiple cyberattacks such as denial of services, distributed denial of services, and Mairi attacks while it includes multiple parameters at the same time. The proposed framework contains three modules; data acquisition and preprocessing module that is responsible for capturing and pre-processing the captured data and ready for the construction of the model, then the attack detection module which is the core engine that orchestrates the detection of cyberattacks, the third module notifies and displays the results in a dashboard. This research study used multiple parameters including multiple attack classes, network packet patterns, and three scaler types namely no scaler, MinMax, and Standard, and regardless of the defined parameters used, minmax scaler followed by standard scaler gives better detection performance than models trained with no scaler. The proposed framework is trained and evaluated with different models including CNN, Hybrid, FFNN, and LSTM provides a result of 91.42%, 82.75%, and 78.38% ,74.83% detection accuracy respectively where it is observed that CNN model outperforms the optimal results among followed by hybrid and FFNN. | |
| dc.identifier.uri | https://etd.aau.edu.et/handle/123456789/6869 | |
| dc.language.iso | en_US | |
| dc.publisher | Addis Ababa University | |
| dc.subject | Iot Environments | |
| dc.subject | Cyberattacks | |
| dc.subject | Multiple Attack Detection | |
| dc.subject | Framework | |
| dc.title | A Framework for Detecting Multiple Cyberattacks in IoT Environment | |
| dc.type | Thesis |