- Using a Web browser, perform some research on a newer malware variant
that has been reported by a major malware containment vendor. Using a
search engine, go to the vendor’s Web site; this could be Symantec, McAfee,
or any of their competitors. Visit one malware prevention software vendor.
Search for the newest malware variants and pick one. Note its name and try
to understand how it works. Now look for information about that same malware from at
least one other vendor. Were you able to see this malware at both vendors? If so, are there
any differences in how they are reported between the two vendors?
need min 2 to 3 pages paper excluding title and reference page
Include at least 250 words in your posting and at least 250 words in your reply. Indicate at least one source or reference in your original post. Please see syllabus for details on submission requirements.
Search “scholar.google.com” or your textbook. Include at least 250 words in your reply. Indicate at least one source or reference in your original post. Discuss ways organizations have built a CSIRT. What are the components to building an effective and successful CSIRT team?
main post -300 words
2 secondary post -each 200 words
The objective of this research is to use content-based recommendation to recommend shots to a user that contain similar characteristics to movies that user previously rated highly. This entails finding a set of movies the user likes using the MovieLens 20-M dataset. For each of those movies, an item profile is to be built, which is a description of the movie based on a number of pre-determined features. From these movies, a user profile will be inferred. For instance, because the user likes movies with Brad Pitt, it will be inferred that the user will prefer shots with Brad Pitt in a movie trailer. Because the user likes horror movies, it will be inferred that the user likes shots that are stylistically similar to horror movies. Informed by the literature discussed above, the following features will be used to create item profiles for the content-based recommendation system: 1. Actor appearance. As one of the most important influencers on film quality expectations, actor appearance should be taken into account as a feature to guide scene recommendation. Actor appearances can be extracted from the IMDB dataset. The logic that this follows from is that if a user likes multiple movies that feature the same actor, this actor should have a high degree of importance in building a user profile. 2. Genre. As discussed above, genre is an important influence on film content expectations and a widely-used feature for segmenting audiences. Genre features are included in the MMTF-14K dataset. 3. Visual descriptors. Low-level visual features have been shown to be v>GET ANSWER