| Type of Material: | Thesis |
| Title: | Efficient Intrusion Detection Using Machine Learning Approaches |
| Researcher: | Rajesh, S |
| Guide: | Sangeetha, M |
| Department: | Department of Electronics and Communication Engineering |
| Publisher: | Bharath University, Chennai |
| Place: | Chennai |
| Year: | 2022 |
| Language: | English |
| Subject: | Engineering and Technology | Engineering Electrical and Electronic | Engineering | Electronics and Communication Engineering | Engineering and Technology |
| Dissertation/Thesis Note: | PhD; Department of Electronics and Communication Engineering, Bharath University, Chennai, Chennai; 2022; D15EC515 |
| Fulltext: | Shodhganga |
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| 040 | __ | |aBHAU_600073|dIN-AhILN |
| 041 | __ | |aeng |
| 100 | __ | |aRajesh, S|eResearcher |
| 110 | __ | |aDepartment of Electronics and Communication Engineering|bBharath University, Chennai|dChennai|ein|0U-0446 |
| 245 | __ | |aEfficient Intrusion Detection Using Machine Learning Approaches |
| 260 | __ | |aChennai|bBharath University, Chennai|c2022 |
| 300 | __ | |dDVD |
| 502 | __ | |bPhD|cDepartment of Electronics and Communication Engineering, Bharath University, Chennai, Chennai|d2022|oD15EC515 |
| 520 | __ | |aThe fast propagation of computer networks has changed the viewpoint of network security. An easy accessibility conditions cause computer network as susceptible against several threats from hackers. Threats to networks are numerous and potentially devastating. Up to the moment, researchers have developed Intrusion Detection Systems (IDS) capable of detecting attacks in several available environments. A boundlessness of methods for misuse detection as well as anomaly detection has been applied. Many of the technologies proposed are complementary to each other, since for different kind of environments some approaches perform better than others. This research presents a new intrusion detection system that is then used to survey and classify them. The taxonomy consists of the detection principle, and second of certain operational aspects of the intrusion detection system. In our research we have used algorithms like Random Forest (RF), Support Vector Machine (SVM), Lexicographic Game Method, Artificial Neural Ne |
| 650 | __ | |aElectronics and Communication Engineering|2UGC |
| 650 | __ | |aEngineering and Technology|2AIU |
| 653 | __ | |aEngineering and Technology |
| 653 | __ | |aEngineering Electrical and Electronic |
| 653 | __ | |aEngineering |
| 700 | __ | |aSangeetha, M|eGuide |
| 856 | __ | |uhttp://shodhganga.inflibnet.ac.in/handle/10603/386525|yShodhganga |
| 905 | __ | |afromsg |
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