Virual Oral Presentation Slide by Slide

IEEE/ICACT20240269 Slide.12        [Big Slide]	Oral Presentation
	In this phase, further processing is applied to the data that was previously clustered using K-means and labeled with MITRE ATT&CK. We will use Locality-sensitive Hashing to deal with the data. LSH is utilized for clustering, grouping similar payloads into the same clusters to generate more clusters. The typical processing involves the following steps: 1. Shingling: converting a string of text into a set of ¡®shingles.¡¯ 2. To obtain a denser vector of named signature, n min-Hash functions are used to obtain n min-Hash values. After applying Min-Hashing, we get signatures that retain similar information. 3. The final step in identifying similar sentences is the LSH function itself. We choose a hash function to calculate the similarity and put the similar payload into the same buckets. After that, we will get more clusters with a hybrid clustering algorithm compared to the former.

[Go to Next Slide]

Select Voice: Virtual Oral Presentation!!