Ali, Mashaleh Ashraf Sulieman (2025) A Hybrid Multi-Tier Approach For Iot Botnet Detection And Enhanced Risk Assessment. PhD thesis, Perpustakaan Hamzah Sendut.
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Abstract
The proliferation of internet of things (iot) devices has led to new cybersecurity challenges. A significant issue is the increasing occurrence of iot botnets, which refers to networks of compromised iot devices like routers, ip cameras, and smart appliances. These compromised entities are strategically utilized to carry out various cyber threats, including but not limited to distributed denial of service (ddos) attacks, data exfiltration, and network reconnaissance. Identifying iot botnets has unique issues due to the constrained resources of the devices involved. This research contributes significantly by identifying the active phase of the iot botnet attack life cycle and enabling flexible evaluation of attack severity levels through an ensemble model stacking and boosting via a soft voting system integrated with a fuzzy logic-based risk assessment methodology optimized by particle swarm optimization. This provides a basis for security teams to allocate resources efficiently, enabling a proactive and dynamic cybersecurity defense against iot botnet threats. A realistic and representative iot dataset was also generated, simulating the iot botnet lifecycle and incorporating the most recent attacks on iot ecosystems. The proposed approach significantly advances iot security by enabling precise detection of botnet activities and proactive threat mitigation. The integration of ensemble learning, fuzzy logic, and pso offers a dynamic solution that adapts to evolving cyber threats, ensuring targeted, efficient responses and safeguarding network integrity.
| Item Type: | Thesis (PhD) |
|---|---|
| Subjects: | Q Science > QA Mathematics > QA75.5-76.95 Electronic computers. Computer science |
| Divisions: | Pusat Pengajian Sains Komputer (School of Computer Sciences) > Thesis |
| Depositing User: | Mr Hasmizar Mansor |
| Date Deposited: | 02 Mar 2026 03:15 |
| Last Modified: | 02 Mar 2026 03:15 |
| URI: | http://eprints.usm.my/id/eprint/63689 |
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