| Type of Material: | Thesis |
| Title: | Designing an optimizing strategy for malware analysis using innovative techniques from data mining |
| Researcher: | Panda, Binayak |
| Guide: | Tripathy, Satya Narayan |
| Department: | Department of Computer Science |
| Publisher: | Berhampur University, Berhampur |
| Place: | Berhampur |
| Year: | 2020 |
| Language: | English |
| Subject: | Computer Science | Computer Science Software Engineering | Engineering and Technology | Computer Science and Applications | Engineering and Technology |
| Dissertation/Thesis Note: | PhD; Department of Computer Science, Berhampur University, Berhampur, Berhampur; 2020 |
| Fulltext: | Shodhganga |
| 003 | IN-AhILN | |
| 005 | 2026-10-16 17:04:38 | |
| 008 | __ | 261016t2020||||ii#||||g|m||||||||||eng|| |
| 035 | __ | |a(IN-AhILN)th_464376 |
| 040 | __ | |dIN-AhILN|aBRMP_760007 |
| 041 | __ | |aeng |
| 100 | __ | |eResearcher|aPanda, Binayak |
| 110 | __ | |aDepartment of Computer Science|0U-0350|dBerhampur|bBerhampur University, Berhampur|ein |
| 245 | __ | |aDesigning an optimizing strategy for malware analysis using innovative techniques from data mining |
| 260 | __ | |aBerhampur|c2020|bBerhampur University, Berhampur |
| 300 | __ | |a184p.|dCD |
| 502 | __ | |cDepartment of Computer Science, Berhampur University, Berhampur, Berhampur|d2020|bPhD |
| 518 | __ | |oDate of Award|d2022 |
| 518 | __ | |d2015|oDate of Registration |
| 518 | __ | |oDate of Completion|d2020 |
| 520 | __ | |aMalware an all-time threat to the computing world, goes through many revolutions to evade antimalware mechanisms. Protecting a host from untrusted programs, as well as known/unknown malware, has evolved as a challenge in front of researchers. Hence the search for optimal approaches are being continued, which may be computationally feasible and have higher accuracy by lowering the false positive and false negative cases. This research investigates both misuse and anomaly detection approaches. This dissertation uses a novel approach named LCSBV (Longest Common Subsequence Based majority Voting) and a Graph structure based optimized approach by extending concepts from data mining to detect new malware and host specific anomalous processes respectively. The proposed methodology towards misuse detection explores a static feature named PSI (Printable String Information) sequence, dynamic feature named fixed length API (Application Programming Interface) sequence and hybrid feature set for analysis and detection o |
| 650 | __ | |aComputer Science and Applications|2UGC |
| 650 | __ | |aEngineering and Technology|2AIU |
| 653 | __ | |aComputer Science |
| 653 | __ | |aComputer Science Software Engineering |
| 653 | __ | |aEngineering and Technology |
| 700 | __ | |aTripathy, Satya Narayan|eGuide |
| 856 | __ | |uhttp://shodhganga.inflibnet.ac.in/handle/10603/409165|yShodhganga |
| 905 | __ | |afromsg |
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