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Insights into Malware Distribution with Graph Analytics
| Content Provider | Semantic Scholar |
|---|---|
| Author | Gao, Mingfei Kim, Doowon Li, Tongyang Srinivas, Virinchi |
| Copyright Year | 2016 |
| Abstract | Malicious software (malware) destroys and steals access to users’ private computer systems, which can lead to breaches of sensitive personal information. It has been rapidly growing, spreading and infecting computer systems; it continues to be an active threat. Currently, more than 200 million unique variants of malware exist. Anti-virus is a software tool that is used to protect against attacks from malware. Lots of works have been dedicated to detecting malware which broadly focused on better understanding the properties of malware such as malware network behaviors. However, this approach is not always able to help detecting new malware because the malware continuously keep evolving by re-packing and obfuscating themselves. Therefore, a new approach, called content-agnostic techniques, has come into the limelight. Content agnostic techniques do not solely rely on the content of various files (benign/malware) and stresses for the need to focus on the relationship between these different files (downloaders). The need for using content agnostic techniques can be motivated with an example that follows. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | https://wiki.cs.umd.edu/cmsc798F_s16/images/4/4c/Main.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
