Malware Analysis
| dc.contributor.author | Chahak | |
| dc.contributor.supervisor | Madan, Sanjay | |
| dc.contributor.supervisor | Verma, Anil Kumar | |
| dc.date.accessioned | 2017-09-05T09:02:14Z | |
| dc.date.available | 2017-09-05T09:02:14Z | |
| dc.date.issued | 2017-09-05 | |
| dc.description.abstract | Malwares are a trending menace in today’s cyber world. They are installed surreptitiously in the system and the results are alarmingly dangerous. Many static analysis approaches and anti-virus tools can be bypassed by the malwares. By analyzing the exact behavior, tendency and execution of the code, dynamic malware analyses have somehow overcome these chicaneries. Analyzing the difference between the desired nodes as well as observing the runtime behavior of malware differentiates dynamic behavior from static. An appropriate tool studies the malware in lieu of its behavior, function and execution and is able to handle multiple processes. Objectifying the scope and functionality of a malware sample is the motive of malware analysis. Unfortunately the amount of specimens to be analyzed by the vendors is rapidly growing on a daily basis. Analyzing the sample during execution time is known as Dynamic Analysis whereas Static analysis is done by inspecting the program and Memory Analysis is defined by studying the memory and registry. Using static approaches leads to a huge level of complications and challenges as it limits itself to combat the malicious content due to the unavailability of the source most of the times. Dynamic analysis overcomes these issues and provides detailed information when a monitored program is executed. | en_US |
| dc.identifier.uri | http://hdl.handle.net/10266/4829 | |
| dc.language.iso | en | en_US |
| dc.subject | Malware Analysis, | en_US |
| dc.subject | Feature Extraction, | en_US |
| dc.subject | Sandbox Environment, | en_US |
| dc.subject | System Calls | en_US |
| dc.title | Malware Analysis | en_US |
| dc.type | Thesis | en_US |
