Enhancing Mobile Banking Service Availability Using Machine Learning
| dc.contributor.advisor | Murad, Ridwan (PhD) | |
| dc.contributor.author | Said, Ahmed Said | |
| dc.date.accessioned | 2018-12-21T09:47:24Z | |
| dc.date.accessioned | 2023-11-04T15:13:06Z | |
| dc.date.available | 2018-12-21T09:47:24Z | |
| dc.date.available | 2023-11-04T15:13:06Z | |
| dc.date.issued | 2018-10 | |
| dc.description.abstract | One of the main obstacles for adoption of mobile banking is that of security concern. This concern is becoming a reality in the case of mobile core inter-node protocol, Signaling System number 7 (SS7). SS7 was developed with the assumption of trusted network within and among operators. With growing number of value-added service providers and roaming partners connecting to operators, the trusted network is no longer a closed network. Attackers continue to exploit vulnerabilities of SS7 network to conduct attacks that compromise confidentiality, integrity and availability of mobile banking users and mobile network operators. In Ethiopia, Short Message Service (SMS) and Unstructured Supplementary Service Data (USSD) are mainly used for mobile banking. These services are both vulnerable to availability attacks. This thesis is an effort to detect SMS availability attacks on Mobile Application Part (MAP) layer of SS7. To mitigate these attacks, machine learning techniques using real SMS traffic data from ethio telecom is used for adaptive detection of abnormal SMS. A novel approach of using aggregation of Message Origination (MO) error codes is proposed for class feature extraction. A combination of expert judgments, literature reviews and information gain are used for optimal feature selection. As a result, it is recommended to use origination, destination, and mobile switching center address and write time as optimal features. To solve the problem of attack message detection, PART, Random Forest and J48 algorithms are compared. It is found that J48 has a superior performance with an accuracy of 98.6465% and model build time of 3.71 seconds. | en_US |
| dc.identifier.uri | http://etd.aau.edu.et/handle/123456789/15243 | |
| dc.language.iso | en_US | en_US |
| dc.subject | Mobile Banking | en_US |
| dc.subject | SS7 | en_US |
| dc.subject | DoS | en_US |
| dc.subject | DDoS | en_US |
| dc.subject | Availability | en_US |
| dc.subject | Machine Learning | en_US |
| dc.subject | SMS | en_US |
| dc.subject | USSD | en_US |
| dc.title | Enhancing Mobile Banking Service Availability Using Machine Learning | en_US |
| dc.type | Thesis | en_US |