| Type of Material: | Thesis |
| Title: | Evaluation of hybrid extreme learning machine models for intrusion detection system |
| Researcher: | Kondru Mounika |
| Guide: | Venkateswara Rao, P |
| Department: | Department of Computer Science and Engineering |
| Publisher: | Adikavi Nannaya University |
| Place: | Rajahmundry |
| Year: | 2023 |
| Language: | English |
| Subject: | Computer Science | Computer Science and Applications | Engineering and Technology |
| Dissertation/Thesis Note: | PhD; Department of Computer Science and Engineering, Adikavi Nannaya University, Rajahmundry; 2023 |
| Fulltext: | Shodhganga |
| 003 | IN-AhILN | |
| 005 | 2026-08-25 15:32:57 | |
| 008 | __ | 260825t2023||||ii#||||g|m||||||||||eng|| |
| 035 | __ | |a(IN-AhILN)th_462301 |
| 040 | __ | |dIN-AhILN|aANUR_533105 |
| 041 | __ | |aeng |
| 100 | __ | |eResearcher|aKondru Mounika |
| 110 | __ | |aDepartment of Computer Science and Engineering|0U-0005|dRajahmundry|bAdikavi Nannaya University|ein |
| 245 | __ | |aEvaluation of hybrid extreme learning machine models for intrusion detection system |
| 260 | __ | |aRajahmundry|c2023|bAdikavi Nannaya University |
| 300 | __ | |dDVD |
| 502 | __ | |d2023|bPhD|cDepartment of Computer Science and Engineering, Adikavi Nannaya University, Rajahmundry |
| 518 | __ | |d2023|oDate of Award |
| 518 | __ | |oDate of Registration|d2000 |
| 518 | __ | |oDate of Completion|d2023 |
| 520 | __ | |aIntrusion Detection Systems (IDS) have been essential to computer security for many years. The history of IDS can be traced back to the early days of computing when the first computer viruses were created. At that time, the focus was creating antivirus programs to detect and remove viruses from infected systems. The different types of attacks are D enial of S ervice (DoS), P robe, R emote to L ocal (R2 L ), and U ser to R oot (U2R) attacks. The early detection of attacks can secure the data. However, due to improper feature analysis, the conventional artificial intelligence based IDS models failed to classify all these attacks. Initially, this work implemented the R ecursive F eature E limination with S election (RFES) based Extreme Learning Machine (ELM) cla ssifier for intrusion detection. Initially, the NSL KDD dataset is preprocessed to maintain the uniform nature of all records with missing data identification. Then, RFES is the process of selecting features sequentially, in which features |
| 650 | __ | |aComputer Science and Applications|2UGC |
| 650 | __ | |aEngineering and Technology|2AIU |
| 653 | __ | |aComputer Science |
| 700 | __ | |aVenkateswara Rao, P|eGuide |
| 856 | __ | |uhttp://shodhganga.inflibnet.ac.in/handle/10603/627474|yShodhganga |
| 905 | __ | |afromsg |
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