AdFlush
dl.acm.org
AdFlush
1–10 of 112 posts
Re: AdFlush
#2I didn't realize this was an active area of research, love this.
Re: AdFlush
#3Looks like this is the associated repo on GitHub: https://github.com/SKKU-SecLab/AdFlush
Re: AdFlush
#4> We tested AdFlush on a dataset of 10,000 real-world websites, achieving an F1 score of 0.98, thereby outperforming AdGraph (F1 score: 0.93), WebGraph (F1 score: 0.90), and WTAgraph (F1 score: 0.84). Additionally, AdFlush significantly reduces computational overhead, requiring 56% less CPU and 80% less memory than AdGraph. We also assessed AdFlush's robustness against adversarial manipulations, demonstrating superior resilience with F1 scores ranging from 0.89 to 0.98
Neat results, I wonder how it compares to uBO or the different blacklists. I assume it self-update with newer techniques and can detect certain patterns?
Re: AdFlush
#5How does this compare to list based solutions? An overblocking/underblocking comparison would be great
Re: AdFlush
#6How realtime is this? Or well enough to not be noticeable while browsing
Re: AdFlush
#7AdFlush (F1 Score: 0.98) seems to do better than some other adblockers: AdGraph (F1 score: 0.93), WebGraph (F1 score: 0.90), and WTAgraph (F1 score: 0.84), but it begs the question: why not compare to the most popular adblockers: uBlock Origin, Adblock Plus etc.
I think the authors want to compare apples with apples, so they only compare their algorithm to other adblockers that use algorithms, as opposed to those which use crowdsourced lists. The paper somewhat acknowledges this:
> However, manual maintenance of these filter lists requires significant human effort
Seems like one of those tasks where crowdsourcing scales so nicely (only one person has to report an ad for it to go into a crowdsourced list that blocks it for millions of others) that it makes an algorithmic approach unnecessary.
Re: AdFlush
#8The future is here.
If I recall, in Permutation City there's some part where somebody deals with spam with AI. The user tries to use a simulation to listen to potential spam to filter it, while the spam tries to figure out whether a real person is listening to it and only tries to spam when a real person is there.
Or something along those lines, it's been a long time since I read it.
Re: AdFlush
#9Re: AdFlush
#10How realtime is this? Or well enough to not be noticeable while browsing
I'd be okay with a hybrid approach: lists for real-time blocking and machine learning for passive analysis to augment the lists over time.