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https://ptsldigital.ukm.my/jspui/handle/123456789/513234Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.advisor | Ravie Chandren Muniyandi, Assoc. Prof. | - |
| dc.contributor.author | Rufai Kazeem Idowu (P63472) | - |
| dc.date.accessioned | 2023-10-16T04:34:55Z | - |
| dc.date.available | 2023-10-16T04:34:55Z | - |
| dc.date.issued | 2015-12 | - |
| dc.identifier.other | ukmvital:83254 | - |
| dc.identifier.uri | https://ptsldigital.ukm.my/jspui/handle/123456789/513234 | - |
| dc.description | Within the cyber space, the network and information systems constantly suffer from intruders“ heinous activities such as integrity compromise, denial of availability and performance inefficiency. Despite various efforts done for Intrusion Detection Systems (IDSs) solution, they still experience general performance problems which are due to high dimensional features in traffic data, boundary problem and huge real time traffics. The popular methods usually adopted to solve these problems are the use of good algorithms, models and architectural designs built on feature selection and fuzzy-based techniques. Furthermore, the negative impact of packet drop/loss is commonly reduced by increasing the throughput and speedup of detection process using distributed and parallelism mechanisms such as in multi-agent systems. Meanwhile, Membrane Computing (MC) which is an emerging branch of computer science, is configured on the inspiration from the functioning of the living cells. MC as a very promising distributed computing model for solving NP-hard problems, has many inherent benefits including maxima parallelism and communication advantage. It has successfully been applied in several fields among which are biology, linguistics, medicine, optimization and cryptography. Therefore, this research work aims to improve IDS parameters by applying the advantages of MC to select appropriate feature subset. Consequently, through the specific objectives of the research, the challenges of "curse of dimensionality", big data and sharp boundary problems were tackled. The first objective proposes a hybrid MC with Bees algorithm (MC-BA) features selection technique for IDS by increasing the communication advantage of membrane system within bee algorithm. In the second objective and still building on appropriate feature subset selection strategy, a variant of MC known as Trapezoidal Fuzzy Reasoning Spiking Neural P system model is proposed for intrusion detection, where earlier it has been used in fault diagnosis in machines. The benefit of applying the approach is that it has the ability to produce knowledge in a form of rule-based and also could overcome the sharp boundary problem by allowing different degrees of memberships. Relatedly, the thesis proposes a MC-based IDS architecture which is designed in line with recognizer tissue P system. It works by applying classification and symport communication rules on the objects contained within the membrane regions to ensure load balancing among the GPU processors. The third objective focuses on the evaluation of the proposed algorithm, model and architecture by performing experiments using the standard benchmark KDD Cup dataset. Whereas the results show that the MC-BA has the ability of considerably reducing the false alarm rate compared with the Bees algorithm and state of art algorithms, the proposed model has improved high detection rate for denial-of-service and brute force attacks. Meanwhile, for the proposed MC-IDS architecture which is implemented on NVIDIA Geforce 680 GPU, the result shows that it has processing speedup of over 5 times and increases the average of throughput (50000p/s). It can be concluded that applying membrane system-based methods could enhance IDS performance by increasing its quality solution and its efficiency.,Certification of Master's/Doctoral Thesis" is not available | - |
| dc.language.iso | eng | - |
| dc.publisher | UKM, Bangi | - |
| dc.relation | Faculty of Information Science and Technology / Fakulti Teknologi dan Sains Maklumat | - |
| dc.rights | UKM | - |
| dc.subject | Membrane computing | - |
| dc.subject | Intrusion detection | - |
| dc.subject | Bees algorithm | - |
| dc.subject | Dissertations, Academic -- Malaysia | - |
| dc.title | Membrane computing for feature selection with intrusion detection parameters | - |
| dc.type | Theses | - |
| dc.format.pages | 192 | - |
| Appears in Collections: | Faculty of Information Science and Technology / Fakulti Teknologi dan Sains Maklumat | |
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| File | Description | Size | Format | |
|---|---|---|---|---|
| ukmvital_83254+SOURCE1+SOURCE1.0.PDF Restricted Access | 363.05 kB | Adobe PDF |  View/Open |
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